# Lennart Nacke, PhD > I build the systems that turn deep knowledge into visible authority. Depth over hype. AI systems for experts who should be impossible to ignore. Public Ghost content for AI and LLM tooling. This file includes a bounded export of public pages first, then recent public posts. Append `.md` to any post or page URL to get the content in Markdown (for example, `/example-post.md`). ## Pages ### Lennart Nacke URL: https://lennartnacke.com/about/ Last updated: 2026-03-26T13:11:04.000Z Meanwhile people with half the depth built audiences, landed consulting clients, and charged premium prices, often just by talking about work I'd helped produce. So, I figured out the system. In 3 years I build a 180,000+ audience across platforms, 14,000+ newsletter subscribers, and a coaching practice built on one idea: That deep expertise deserves to be findable, followable, and hireable. Now, I help expert founders, consultants, and senior operators turn invisible expertise into inbound demand using AI systems and sharp positioning. No bro marketing. No motivation porn. Just the infrastructure that makes the right people find you. ## How I Work With Clients ### 1:1 Coaching: Your Expertise, Finally Visible If you've built 10+ years of real domain expertise but your online presence doesn't reflect it, this is what I do. I work directly with founders and consultants to build their authority system: Positioning, AI-assisted content workflows, and a publishing rhythm that compounds over time. A personal brand is not optional anymore as AI is coming for your job. The methodology I teach is IDEA 2 SHIP. Most expert clients go straight to SHIP: sharpen one idea, hone the audience, install your AI-assisted content system, publish and prove it works. Packages range from a 2-session Authority Sprint to ongoing Executive Advisory. Every engagement starts with a 20-minute clarity call. **For companies:** I run hands-on AI training workshops for sales, marketing, research, and engineering teams. [**Book a Clarity Call →**](https://tidycal.com/nacke/discovery?ref=lennartnacke.com) ## The Write Insight Newsletter Every week, I publish a new AI workflow, prompt system, or framework for translating expertise into clear, compelling content, without sacrificing your voice or letting AI degrade your writing. I see writing as a path to authority. 14,000+ subscribers read it. → [Read past issues here.](https://lennartnacke.com/tag/write-insight/) ## Brand Partnerships If you're a brand or company in the AI, productivity, or founder space interested in reaching a highly engaged audience of expert professionals, check out [my partnership offers on Passionfroot.](https://www.passionfront.com/?ref=lennartnacke.com) ## The Research Behind the Work My expertise isn't theoretical. While I'm building this practice, I also run a research program at the University of Waterloo with millions of dollars in funding and partnerships with organizations including RBC Royal Bank of Canada, TD Bank, Rogers, Scotiabank, the Canadian Association of Journalists (CAJ), the International Test Pilots School (ITPS) Canada, and government agencies like NSERC, SSHRC, CIHR, and Mitacs. I've published in the top venues in human-computer interaction (CHI, CSCW, CHI PLAY), earned multiple Best Paper awards, co-edited *Games User Research* (Oxford University Press), and founded the ACM CHI PLAY conference. I was ranked in the top 10 most-cited HCI researchers (2011–2020) and the top 2% of researchers worldwide. I mention this not as my identity, but as proof. I understand what it means to build real depth over decades, and what it takes to make that depth visible to the people who need it the most. ### Home URL: https://lennartnacke.com/home/ Last updated: 2024-09-02T22:56:49.000Z Dr. Lennart Nacke is a pioneer in games, gamification, and user experience. As Professor of Human-Computer Interaction (HCI) in Games at University of Waterloo, he explores how user experience of video and exercise games can drive engagement and change behaviours. Over the past 15 years, he has published more than 200 academic papers and a best-selling book on Games User Research. A sought-after keynote speaker, Dr. Nacke has advised organizations worldwide on effective gamification strategies. He was recognized among the top 10 HCI scholars of the last decade and the top 2% of scientists worldwide. His groundbreaking work continues to shape how we understand and apply games research. ### Privacy Policy URL: https://lennartnacke.com/privacy-policy/ Last updated: 2026-07-17T20:11:21.000Z **Last updated:** 16 July, 2026. Lennart Nacke, operating as The Acagamic, a business registered in Ontario, Canada ("Company," "we," "us," or "our"), operates the website lennartnacke.com and related services, including our newsletter, online courses, membership community, and coaching services (collectively, the "Service"). This Privacy Policy explains what personal information we collect, why we collect it, how we use and share it, how long we keep it, and the rights and choices you have. → [Cookie preferences](#cookie-preferences) **Person in charge of the protection of personal information / Privacy Officer:** Lennart Nacke Email: legal@lennartnacke.com Mail: ℅ Lennart Nacke, 330 Av Avro, Pointe-Claire, QC H9R 5W5, Canada This Privacy Policy is designed to meet the requirements of the *Personal Information Protection and Electronic Documents Act* (PIPEDA), Quebec's *Act respecting the protection of personal information in the private sector* (as amended by Law 25), Canada's Anti-Spam Legislation (CASL), and — where they apply to you — the EU General Data Protection Regulation (GDPR) and UK GDPR. ## 1\. Information We Collect ### 1.1 Information You Provide Directly We collect personal information you provide when you: - **Subscribe to our newsletter** — name and email address; - **Purchase a product, membership, or coaching package** — name, email address, billing address, and transaction details (your full payment card details are collected and processed by our payment processors, not by us); - **Join our membership community** — profile information you choose to provide (name, photo, bio) and the posts, comments, and messages you share; - **Book or attend a coaching or discovery call** — scheduling details, information you share on intake forms, and the content of the session itself, including notes, **session recordings, and transcripts** (see Section 1.4); - **Contact us** — the contents of your message and your contact details; - **Provide a testimonial** — only with your separate, express consent. ### 1.2 Information Collected Automatically When you use the Service, we and our service providers may automatically collect: - **Log and usage data** — IP address, browser type, operating system, pages visited, referring pages, and time spent; - **Device information** — hardware model, operating system version, and unique identifiers; - **Cookies and similar technologies** — see Section 7. ### 1.3 Information from Other Sources We may receive information from third-party platforms you interact with us through — for example, the platform profile you use in our community (Skool), your subscription status from our email platform (Kit) or publishing platforms (Ghost, Substack), purchase confirmations from our checkout providers, and public social media interactions. We combine this with information we hold to administer your purchases and improve the Service. ### 1.4 Coaching Sessions, Recordings, and Transcripts **Coaching and discovery calls are recorded and transcribed by default** (including using automated/AI transcription tools) for the purposes of delivering the coaching engagement, preparing session notes and action plans, and quality assurance. This is disclosed here and in your booking confirmation; by booking and attending a session, you consent to recording and transcription. If you do not wish a session to be recorded, tell us before the session starts and we will accommodate where practicable; you may also request deletion of a recording after the fact (Section 9). Session recordings, transcripts, and coaching notes are treated as confidential, are accessible only to us and the service providers that process them on our behalf, and are never used for marketing or published without your separate, express consent. Because coaching conversations may include information you consider sensitive, we apply heightened care to this data (see Sections 5 and 6). ### 1.5 What We Do Not Collect We do not knowingly collect government identifiers, health records, biometric data, or precise geolocation. Please do not share information in coaching sessions or community posts that you are not comfortable being processed as described in this Policy. ## 2\. Purposes and Legal Bases We collect and use personal information for the following purposes. Where GDPR/UK GDPR applies, the corresponding lawful basis is noted. | Purpose | Examples | Lawful basis (GDPR) | | ---------------------------------- | ----------------------------------------------------------------------------------------------- | -------------------------------------------------------- | | Providing the Service | Delivering courses, memberships, coaching sessions; account administration; processing payments | Performance of a contract | | Communications about your purchase | Receipts, renewal reminders, schedule changes, security and service notices | Performance of a contract; legitimate interests | | Newsletter and marketing | Sending The Write Insight and promotional emails you signed up for | Consent (you may withdraw at any time) | | Improving the Service | Analytics, understanding which content is useful, fixing problems | Legitimate interests; consent where required for cookies | | Coaching delivery | Session notes, recordings, transcripts, action plans, progress tracking | Performance of a contract; consent for recordings | | Legal compliance | Tax and accounting records, responding to lawful requests, enforcing our Terms | Legal obligation; legitimate interests | | Safety and security | Preventing fraud, abuse, and unauthorized access | Legitimate interests | We identify our purposes at or before the time of collection and do not use your personal information for new, incompatible purposes without your consent, except where permitted or required by law. **We do not sell your personal information, and we do not share it with third parties for their own advertising purposes.** ## 3\. Consent Where consent is our basis for collecting, using, or disclosing your personal information, we seek consent that is specific to the purpose, and we ask for it separately from other terms where the law requires (for example, recording consent for coaching sessions and express consent for testimonials). You may withdraw your consent at any time, subject to legal or contractual restrictions and reasonable notice, by contacting legal@lennartnacke.com or using the mechanisms described in this Policy (for example, unsubscribe links). Withdrawal does not affect processing that occurred before withdrawal, and some services cannot be provided without certain information. ## 4\. Email and CASL Compliance We send commercial electronic messages (such as our newsletter and promotional emails) only with your express or implied consent as permitted by Canada's Anti-Spam Legislation (CASL). Every marketing email we send identifies us, includes our contact information, and contains a working **unsubscribe** link that takes effect promptly (and in any event within 10 business days). If you unsubscribe from marketing, we may still send you non-promotional, transactional messages related to your purchases or account (receipts, renewal notices, service announcements). ## 5\. Sharing of Information We share personal information only as described below. We do not sell it. ### 5.1 Service Providers (Processors) We use third-party providers to operate the Service. They process personal information on our behalf, under contracts that restrict their use of it to providing services to us. Categories and current providers include: - **Email and newsletter delivery:** Kit (ConvertKit); Ghost; Substack - **Community and course hosting:** Skool - **Payments and checkout:** Stripe; Payhip; ThriveCart (your card details go to the payment processor, not to us; where a platform such as Payhip acts as merchant of record, it is an independent controller of your purchase data under its own privacy policy) - **Scheduling and events:** Calendly; Luma - **Website hosting and analytics:** our web host and privacy-respecting analytics tools - **Video conferencing, recording, and transcription:** the conferencing and AI-transcription tools used to deliver and document coaching sessions - **Business operations:** accounting, tax, and (where needed) legal or professional advisors The specific providers may change over time; this section describes the categories, and you can request the current list at legal@lennartnacke.com. ### 5.2 Business Transfers If we are involved in a merger, acquisition, financing, or sale of assets, personal information may be transferred as part of that transaction. We will require the recipient to honour commitments materially consistent with this Policy, and we will notify you of any transfer that results in a materially different policy applying to your information. ### 5.3 Legal Requirements and Protection We may disclose personal information where required by law, subpoena, or court order; to respond to lawful requests from public authorities; or where reasonably necessary to enforce our Terms of Service, protect our rights or property, or protect the safety of any person. ### 5.4 With Your Consent We share your information for any other purpose only with your consent or at your direction. ## 6\. Security We use technical and organizational measures appropriate to the sensitivity of the information we hold, including encryption in transit, access controls, and least-privilege access to coaching records. No method of transmission or storage is completely secure, and we cannot guarantee absolute security — but we commit to the breach-response obligations in Section 10. ## 7\. Cookies and Tracking Technologies We use cookies and similar technologies to: - **Operate the site** (strictly necessary cookies — logins, checkout, security); - **Measure usage** (analytics cookies — pages visited, aggregate traffic patterns); - **Improve marketing** (only where used, and only with consent where required — for example, advertising pixels). By continuing to use the Service after seeing this Policy, you consent to our use of cookies as described above, to the extent permitted by applicable law. You can withdraw or manage this consent at any time through your **browser settings** — including refusing all or some cookies, deleting stored cookies, or receiving an alert when cookies are set. Some parts of the Service may not function properly without strictly necessary cookies. Our email provider may use open- and click-tracking in newsletters; you can opt out of all tracking by unsubscribing, or contact us for alternatives. ## 8\. Retention We keep personal information only as long as needed for the purposes identified, then securely delete or anonymize it. Our standard retention practices: - **Newsletter data:** until you unsubscribe, plus a suppression record (email address only) kept to honour your opt-out; - **Purchase and transaction records:** 7 years, to meet Canadian tax and accounting requirements; - **Coaching session recordings, transcripts, and notes:** **6 months** after the engagement ends by default; longer on a case-by-case basis where the engagement requires it, where you and we agree (for example, for a continuing coaching relationship), or where the law requires retention. You may request earlier deletion at any time (Section 9); - **Community content:** for as long as your membership is active; on departure you may request removal or anonymization of your identifiable posts (Section 9); - **Support correspondence:** **6 months** after resolution by default; longer case by case where a matter remains open or the law requires it. Where information is subject to a legal hold or an ongoing dispute, we retain it until the matter concludes. ## 9\. Your Rights ### 9.1 All Users (PIPEDA and Quebec Law 25) You have the right to: - **Access** the personal information we hold about you and be informed of how it has been used and to whom it has been disclosed; - **Correction** of inaccurate, incomplete, or ambiguous information; - **Withdraw consent** (Section 3); - **Deletion / de-indexing (Quebec):** request that we cease disseminating your personal information or de-index it where dissemination breaches the law or a court order, or causes you serious injury; - **Data portability (Quebec):** receive computerized personal information you provided to us in a structured, commonly used technological format, or have it transferred to another organization where technically feasible; - **Complain:** challenge our compliance by contacting our Privacy Officer (details above). If you are not satisfied with our response, you may complain to the **Office of the Privacy Commissioner of Canada** (priv.gc.ca) or, for Quebec residents, the **Commission d'accès à l'information du Québec** (cai.gouv.qc.ca). ### 9.2 EU/UK Users (GDPR / UK GDPR) If you are in the European Economic Area or the United Kingdom, you additionally have the rights of access, rectification, **erasure**, restriction of processing, **data portability**, and **objection** (including an absolute right to object to direct marketing), and the right not to be subject to solely automated decisions with legal or similarly significant effects (we do not make such decisions). You may lodge a complaint with your local supervisory authority. Where we rely on consent, you may withdraw it at any time. ### 9.3 How to Exercise Your Rights Email legal@lennartnacke.com with your request. We will verify your identity (to protect your information from fraudulent requests) and respond within **30 days** (PIPEDA/Law 25) or one month (GDPR), extendable where the law permits, in which case we will tell you. Exercising your rights is free of charge except where the law allows a reasonable fee for excessive or repetitive requests. ## 10\. Breach Notification If a breach of security safeguards involving your personal information creates a **real risk of significant harm** to you, we will report the breach to the Office of the Privacy Commissioner of Canada, notify you as soon as feasible, and keep records of the incident, as required by PIPEDA. For incidents involving Quebec residents, we will also notify the Commission d'accès à l'information and affected individuals where there is a **risk of serious injury**, and record the incident in our confidentiality-incident register, as required by Law 25. ## 11\. International Data Transfers We are based in Canada. Some of our service providers (Section 5.1) store or process personal information in the **United States or other countries**, where privacy laws may differ from those of your jurisdiction. Before communicating personal information outside Quebec/Canada, we assess the sensitivity of the information, the purposes of its use, and the protections in place — including contractual safeguards with our providers — as required by Quebec Law 25\. For transfers of EU/UK personal data, we and our providers rely on appropriate safeguards such as adequacy decisions and standard contractual clauses. You may contact us for more information about these safeguards. ## 12\. Children's Privacy The Service is intended for adults and is not directed to anyone under **18 years of age**. We do not knowingly collect personal information from minors. If you believe a minor has provided us personal information, contact legal@lennartnacke.com and we will delete it promptly. ## 13\. Third-Party Links and Platforms The Service contains links to third-party websites and operates on third-party platforms (for example, Skool, Substack, YouTube, LinkedIn, X). Those parties have their own privacy policies, and we are not responsible for their practices. When you interact with us on a third-party platform, that platform independently collects data about you under its own policy; this Policy covers only the information we receive. ## 14\. Changes to This Privacy Policy We may update this Privacy Policy from time to time. We will post the updated policy here with a new "Last Updated" date and, for **material changes** (such as new purposes or new categories of sharing), we will provide prominent notice on the Service or by email before the change takes effect and obtain fresh consent where the law requires it. ## 15\. Contact Us Questions, requests, or complaints about this Privacy Policy or our data practices: **Privacy Officer / Responsable de la protection des renseignements personnels:** Lennart Nacke Email: legal@lennartnacke.com Mail: ℅ Lennart Nacke, 330 Av Avro, Pointe-Claire, QC H9R 5W5, Canada **AVIS AUX RÉSIDENTS DU QUÉBEC:** Vous pouvez exercer vos droits d'accès, de rectification, de retrait de consentement, de désindexation et de portabilité en écrivant au responsable de la protection des renseignements personnels à l'adresse ci-dessus. Une version française de la présente politique est disponible sur demande. ### Welcome to Write Insight URL: https://lennartnacke.com/welcome-to-write-insight/ Last updated: 2025-07-08T14:41:22.000Z Thanks for paying for a subscription to the Write Insight newsletter _This page is for paying subscribers only._ ### The PhD Defence Playbook URL: https://lennartnacke.com/professor-nackes-phd-defence-playbook/ Last updated: 2025-01-20T04:49:08.000Z _This page is for subscribers only._ ### Course: Data Collection Cheatsheet URL: https://lennartnacke.com/rm-course-data-collection-cheatsheet/ Last updated: 2025-04-06T23:58:38.000Z The cheatsheet from the data collection part of the research methods email course. Please download the file from here if you like. _This page is for subscribers only._ ### Powerful Thesis Statement URL: https://lennartnacke.com/powerful-thesis-statement/ Last updated: 2025-04-09T15:01:03.000Z 📖 You are only one click away from getting the guide. Please create a FREE subscriber account below on my website to access it: ↴ _This page is for subscribers only._ ### Course: Research Paradigms Cheatsheet URL: https://lennartnacke.com/course-research-paradigms-cheatsheet/ Last updated: 2025-04-09T02:59:08.000Z Understanding the fundamental perspectives that shape how we conduct scientific research _This page is for subscribers only._ ### Academic Terminology Cheat Sheet URL: https://lennartnacke.com/academic-terminology-cheat-sheet/ Last updated: 2025-04-09T03:25:04.000Z Essential academic terminology used in research and scholarly writing _This page is for subscribers only._ ### Finding Your Perfect PhD Supervisor URL: https://lennartnacke.com/finding-your-perfect-phd-supervisor/ Last updated: 2025-04-26T12:16:47.000Z 📖 You are only one click away from getting the guide. Please create a [FREE subscriber account](https://lennartnacke.com/#/portal/signup) below on my website to access it: ↴ _This page is for subscribers only._ ### LLM Prompting Techniques Mind Map URL: https://lennartnacke.com/llm-prompting-techniques-mind-map/ Last updated: 2025-05-03T12:06:05.000Z 📖 You are only one click away from getting the mindmap. Please create a [FREE subscriber account](https://lennartnacke.com/#/portal/signup) below on my website to access it: ↴ _This page is for subscribers only._ ### Terms of Service URL: https://lennartnacke.com/terms-of-service/ Last updated: 2026-07-16T22:09:55.000Z #### Plain Language Summary We sell educational content, an online membership community, and coaching services. Memberships renew automatically until you cancel. Coaching is education and mentorship (and must not be understood as therapy, medical, financial, or legal advice). Digital products carry a 7-day money-back guarantee (conditions below). ## 1\. WHO WE ARE AND ACCEPTANCE OF THESE TERMS These Terms of Service ("Terms") are a legally binding agreement between you ("you" or "your") and Lennart Nacke, operating as The Acagamic ("Company," "we," "us," or "our"), governing your access to and use of our websites, digital products, membership community, coaching services, and related offerings (collectively, the "Service"). BY PURCHASING, ACCESSING, OR USING THE SERVICE, YOU AGREE TO BE BOUND BY THESE TERMS. IF YOU DO NOT AGREE, DO NOT ACCESS OR USE THE SERVICE. By using the Service, you represent and warrant that: - You are at least 18 years of age or have reached the age of majority in your jurisdiction; - You have the legal capacity to enter into this agreement; and - You will comply with all applicable laws and regulations. **Preservation of consumer rights.** If you are a consumer, you may have rights under mandatory consumer protection laws in your jurisdiction (including the *Consumer Protection Act, 2002* (Ontario), the *Consumer Protection Act* (Quebec), and equivalent laws elsewhere) that cannot be excluded, limited, or waived by contract. Nothing in these Terms excludes, limits, or waives any such rights. Where these Terms conflict with a non-waivable statutory right, the statutory right prevails. ## 2\. DEFINITIONS - **"Content"** means any text, images, audio, video, software, data, or other materials made available through the Service. - **"Digital Products"** means one-time-purchase digital goods such as online courses, workshops, webinars, templates, e-books, and downloads. - **"Membership"** means any recurring, subscription-based offering that provides ongoing access to Content, community spaces, live sessions, or other benefits for as long as the subscription remains active and paid. - **"Coaching Services"** means one-on-one or group coaching, consulting, mentoring, office hours, or advisory sessions delivered live (in person or remotely) or asynchronously. - **"User Content"** means any content you submit, post, or transmit through the Service, including community posts, comments, questions, and materials shared in coaching sessions. - **"Personal Information"** has the meaning set out in the *Personal Information Protection and Electronic Documents Act* (PIPEDA). ## 3\. SERVICES WE PROVIDE We provide online educational and professional-development services, including: - Online courses, webinars, workshops, paid newsletters, and digital downloads; - A paid Membership community with ongoing content, live calls, and interactive features; - One-on-one and group Coaching Services; - Free content such as newsletters, articles, and social media posts. We may change, add, or discontinue features of the Service. If we discontinue a paid offering entirely, Section 13 (Termination) governs any refund of prepaid, unused fees. ## 4\. ACCOUNTS AND SECURITY You are responsible for maintaining the confidentiality of your account credentials and for all activity under your account. Accounts and Membership seats are personal to you and may not be shared, resold, or transferred. Notify us promptly at legal@lennartnacke.com if you suspect unauthorized use of your account. ## 5\. MEMBERSHIPS AND SUBSCRIPTIONS ### 5.1 Automatic Renewal MEMBERSHIPS RENEW AUTOMATICALLY at the end of each billing period (monthly or annual, as selected at checkout) and your payment method will be charged the then-current subscription fee, UNTIL YOU CANCEL. The billing amount, currency, and renewal interval are disclosed at checkout before you complete your purchase. ### 5.2 Cancellation You may cancel your Membership at any time through your account settings on the applicable platform or by emailing legal@lennartnacke.com. Cancellation takes effect at the **end of your current paid billing period**. You retain access until then. Except where required by law or expressly stated otherwise, fees already paid for the current billing period are non-refundable, and no pro-rata refunds are issued for partial periods. ### 5.3 Price Changes We may change Membership pricing. We will give you at least **30 days' advance notice** by email before a price change applies to your subscription. If you do not agree with the new price, you may cancel before the change takes effect; continuing your subscription after the effective date constitutes acceptance of the new price. ### 5.4 Free Trials and Promotional Pricing If a Membership includes a free trial or introductory price, the trial length, the date of first charge, and the regular price after the trial are disclosed at signup. Unless you cancel before the trial or promotional period ends, your payment method will be charged the regular price. ### 5.5 Effect of Cancellation or Non-Payment Upon cancellation, expiry, or failed payment (after reasonable retry attempts and notice), your access to Membership Content and community spaces ends. Membership Content is licensed for the duration of your active Membership only; it is not purchased outright unless expressly stated. ## 6\. COACHING SERVICES ### 6.1 Nature of Coaching is not Professional Advice Coaching Services are educational and developmental in nature. Coaching is **not** — and must not be relied upon as — psychotherapy, counselling, medical or mental-health treatment, financial, investment, accounting, tax, or legal advice. No physician-patient, therapist-client, fiduciary, or other professional relationship is created by these Terms or by participation in Coaching Services. You should consult appropriately qualified, licensed professionals before making medical, mental-health, financial, or legal decisions. If you are in crisis or experiencing a mental-health emergency, contact local emergency services. ### 6.2 Your Responsibility for Results You are solely responsible for your decisions, actions, and results. Coaching outcomes depend on many factors within and outside your control, including your own effort, circumstances, and market conditions. **We do not guarantee any particular outcome, result, income, admission, promotion, publication, or other achievement.** ### 6.3 Scheduling, Rescheduling, and No-Shows - Sessions must be scheduled through the booking system we provide. - You may reschedule a session with at least **24 hours' notice** at no charge. - Sessions cancelled with less than 24 hours' notice, or missed without notice ("no-shows"), are forfeited and will be counted as delivered, except where the cancellation results from circumstances beyond your reasonable control or where applicable law requires otherwise. - If **we** need to reschedule, we will offer you a replacement session at no additional cost. ### 6.4 Session Validity Period Unless otherwise stated in your coaching agreement or offer page, prepaid coaching sessions or packages must be used within **12 months** of purchase. Unused sessions expire after this period, except where prohibited by applicable law. ### 6.5 Recordings Coaching and group sessions may be recorded for delivery, quality, and (for group programs) replay purposes. We will tell you when a session is recorded. By participating in a session you know is being recorded, you consent to the recording and to its use for delivering the program to you and other enrolled participants. We will not use recordings that identify you for **marketing purposes** without your separate, express consent. ### 6.6 Confidentiality We will keep information you share in one-on-one coaching sessions confidential, except: (a) with your consent; (b) where disclosure is required by law; or (c) where we reasonably believe there is a risk of serious harm to you or others. Group-program participants are asked to keep other members' information confidential, but we cannot guarantee the conduct of other participants — do not share anything in group settings that you need to keep secret. ## 7\. PAYMENTS, TAXES, AND THIRD-PARTY PLATFORMS ### 7.1 Payment Terms Payment is due at the time of purchase unless otherwise stated. Prices are displayed in the currency stated at checkout (US dollars unless otherwise indicated) and are exclusive of applicable taxes unless stated otherwise. Applicable GST/HST/QST, VAT, or other sales taxes are calculated and disclosed at checkout. ### 7.2 Third-Party Platforms and Payment Processors We sell through third-party platforms and payment processors (which may include, for example, Stripe, Payhip, Skool, ThriveCart, Ghost, Substack, Kit, Luma, and Calendly). Your payment information is handled by those processors, not stored by us. Where a platform acts as the **merchant of record** for a purchase (this is stated at checkout or on your receipt), your purchase contract for payment purposes is with that platform and its purchase terms also apply; these Terms continue to govern your access to and use of the underlying Content and Service. ### 7.3 Failed Payments and Chargebacks If a payment fails, we may retry the charge and notify you before suspending access. Initiating a chargeback or payment dispute for a charge you authorized, instead of contacting us first, is a violation of these Terms; we reserve the right to suspend access while a dispute is investigated and to contest illegitimate chargebacks. This does not limit your right to raise legitimate billing disputes with us or your card issuer. ## 8\. REFUND POLICY ### 8.1 Statutory Rights First This policy is in addition to — and does not reduce — any refund, cancellation, or cooling-off rights you have under applicable consumer protection law, including rights under the Ontario *Consumer Protection Act, 2002* relating to internet agreements. ### 8.2 Digital Products — 7-Day Guarantee We offer a 7-day money-back guarantee on Digital Products, on these conditions, which you accept at purchase: - The refund request must be submitted to legal@lennartnacke.com within **7 days of purchase**; - For implementation-based programs, you must show that you made a genuine attempt to use the program (for example, completed exercises or worksheets), as described on the specific product's sales page; - Refunds are not available where these Terms have been materially violated (for example, unauthorized redistribution of the materials); - Approved refunds are processed to the original payment method within **14 days** of approval. Where a specific product's sales page states a different guarantee (longer period or unconditional), the sales-page terms govern for that product. ### 8.3 Memberships Membership fees for a billing period already started are non-refundable except as required by law; instead, you may cancel future renewals at any time under Section 5.2\. If you were charged in error, contact us within 30 days and we will correct it. ### 8.4 Coaching Services Unpurchased and unscheduled prepaid coaching sessions in a package may be refunded on a pro-rata basis (total paid minus the standard single-session rate for sessions already delivered) if requested before the package validity period ends. Delivered sessions and no-show forfeitures under Section 6.3 are non-refundable. ### 8.5 EU/UK Consumers — Withdrawal Right and Digital Content If you are a consumer in the European Union or United Kingdom, you generally have a 14-day right of withdrawal for distance purchases. For Digital Products delivered immediately, by requesting immediate access at checkout you expressly consent to immediate performance and acknowledge that you thereby **lose the statutory right of withdrawal** to the extent permitted by law. Our 7-day guarantee in Section 8.2 applies independently of this. ## 9\. INTELLECTUAL PROPERTY ### 9.1 Our Intellectual Property All Content, materials, features, and functionality of the Service — including text, graphics, logos, images, audio, video, software, course materials, frameworks, and design — are owned by us or our licensors and are protected by Canadian and international copyright, trademark, and other intellectual property laws. ### 9.2 Your License to Use Our Content Subject to these Terms and payment of applicable fees, we grant you a limited, non-exclusive, non-transferable, revocable license to access and use the Service and its Content for your **personal, non-commercial educational purposes**. You may not: - Reproduce, distribute, publicly display, resell, or create derivative works from our Content without our prior written permission; - Share course materials, Membership content, or login credentials with non-purchasers; - Remove or alter any copyright, trademark, or proprietary notices; - Use our Content to train machine-learning or AI models, or for any automated content-generation service, without our prior written permission; - Reverse engineer, decompile, or disassemble any software component of the Service. Nothing in this section limits uses permitted under the *Copyright Act* (Canada), including fair dealing. ### 9.3 License You Grant Us for User Content You retain ownership of your User Content. 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CONTACT INFORMATION Lennart Nacke / The Acagamic Mail: 330 Av Avro, Pointe-Claire, QC H9R 5W5, Canada Email: legal@lennartnacke.com **ACKNOWLEDGMENT:** By using the Service, you acknowledge that you have read, understood, and agree to be bound by these Terms of Service and our Privacy Policy. **NOTICE FOR QUEBEC RESIDENTS / AVIS AUX RÉSIDENTS DU QUÉBEC:** Vous avez demandé que ce contrat soit rédigé en anglais après avoir eu la possibilité d'en examiner une version française. The parties confirm that they have expressly requested that this agreement be drawn up in English after having been given the opportunity to review a French version. Rien dans les présentes conditions ne limite les droits que vous confère la *Loi sur la protection du consommateur* (Québec). **ADDITIONAL PROVINCIAL NOTICES:** Nothing in these Terms limits rights you may have under the consumer protection legislation of your province or territory. For information about your rights, contact your provincial or territorial consumer protection office. ### SciSpace Grant Review Prompt URL: https://lennartnacke.com/scispace-grant-review-prompt/ Last updated: 2025-07-29T02:53:32.000Z ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. [Get the Academic Cheat Sheet PostersLearn everything about the academic storytelling framework, review paper structure, and quantitative paper structures.![](https://lennartnacke.com/content/images/icon/favicon.ico)![](https://lennartnacke.com/content/images/thumbnail/sAQZTsP9dzHxvnVjvfLjgh)](https://posters.lennartnacke.com/?ref=lennartnacke.com) --- Here's the template for you to copy over for a full grant review report: Copy ` You are an expert grant peer reviewer with extensive experience in academic research funding, grant writing, and evaluation. Your task is to rigorously assess a provided grant proposal draft, identify its strengths and weaknesses, and offer constructive suggestions for improvement based on a predefined set of weighted criteria. Input: Grant Proposal Draft (PDF/Text): The full content of the grant proposal draft is attached as a PDF. Related Research Context: Focus on research in {YOUR FIELD} and recent publications from {TOP VENUE}. Instructions: Task 1: Pre-analysis Research & Contextualization Before evaluating the grant proposal, you will perform a focused literature review. Objective: Identify recent and highly relevant research, methodologies, and funding trends pertaining to the core subject matter and proposed research area of the grant proposal. Action: Conduct a search for peer-reviewed articles, successful grant proposals (if publicly available), and relevant scientific or policy reports. Synthesize key findings and emerging themes that will inform your evaluation of the proposal's {GRANT EVALUATION CRITERIA}. Pay particular attention to current best practices in the field. Task 2: Grant Proposal Evaluation Criterion-Based Assessment: Evaluate the provided grant proposal draft against the following {NUMBER} weighted criteria ({X% FOR CRITERION A, etc.}). For each sub-criterion, assign a rating of {COMMA-SEPARATED LIST OF GRANT RATING CRITERIA} and provide a brief justification for your rating, referencing specific elements within the grant proposal. Evaluation Criteria {GET THESE ALL FROM YOUR GRANT CALL}: 1. {CRITERION A} (X%) * 1.1 {SUBCRITERION} * {BEST RATING}:{DESCRIPTION OF BEST RATING} ... {CONTINUE WITH FULL LIST OF CRITERIA} Task 3: Overall Assessment and Justification Provide an overall summary assessment of the grant proposal's strengths and weaknesses based on your detailed criterion-based evaluation. Task 4: Suggestions for Improvement Based on your detailed evaluation and the pre-analysis research, provide specific, actionable, and constructive suggestions for how the grant proposal can be improved. Organize these suggestions by the criteria they address. Prioritize suggestions that would elevate {LOWER} ratings to {HIGHER RATINGS}. Output Format: Your output should be structured as follows: I. Pre-analysis Research Summary * Brief overview of key findings and emerging themes relevant to the grant proposal's subject area. II. Grant Proposal Evaluation text **A. {CRITERION}** * **1.1 {SUBCRITERION}:** * **Rating:** [{RATING SCALE}] * **Justification:** [Specific details from the proposal and reasoning for rating] * *... [Repeat for remaining] III. Overall Assessment * [Summary of strengths and weaknesses across all criteria, noting overall readiness for submission.] IV. Suggestions for Improvement * A. {CRITERION A} * [Specific, actionable suggestion 1] * [Specific, actionable suggestion 2] * ... * E. General Suggestions (if any, e.g., clarity, conciseness) * ...` ### A Modern Approach to Literature Referencing With Consensus URL: https://lennartnacke.com/consensus-course/ Last updated: 2026-02-13T19:23:46.000Z 💡 Thanks to [Consensus](https://go.lennartnacke.com/consensus?ref=lennartnacke.com) for sponsoring this course, so I could make it available for my [email subscribers for free](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com). _This page is for subscribers only._ ## Posts ### How to become an irreplaceable researcher in 2026 URL: https://lennartnacke.com/how-to-become-an-irreplaceable-researcher-in-2026/ Last updated: 2026-08-25T21:20:54.000Z I watch the AI hypelords proclaim the end of human writing every day on X and YouTube, while I’ve never felt more confident of the prosaic similitude of Claude’s responses. It’s hard not to be intimidated by reports of jobs being taken by AI and watching truly powerful automations happen on your computer. If these tools can actually produce a half decent draft in 30 seconds, what good does it do to me to agonize over 60 minutes to even get this first paragraph out on paper? With powerful, super fast lookup, AI truly feels smart as a research assistant unearthing things that took you hours to find on Google. But research is not just finding things, it’s also verifying them, which used to be difficult, but as prompts got better and AI agents self-corrected, it’s hardly an issue anymore. If you manage to publish with AI hallucinated references in 2026, you’re just lazy or don’t know enough about agentic AI. At this point, maybe my work can be replaced by AI. It is a valid fear I’m living through every day. It’s not the first time I’ve felt replicable though. As a young postdoc, I once met with a bunch of Stanford scholars for lunch at a conference. Their entitlement aside, I’ve rarely felt less accomplished than listening to their publication records and job market strategies as I was out on the job market myself back then. Surely, I would not have a chance at a coveted professor job if that’s the competition. These people made me feel small despite my unfettered work ethic. Little did I know back then that this fear was just infused by feeling judged by them and by my future superiors. Let’s face it. As researchers, we live in a constant state of judgment. There’s always a dean, panel chair, VP of Product, hiring committee, or worst of all, our inner reviewer passing judgment on our abilities. There are always gatekeepers that we’re facing. The fear of replaceability is never abstract. The fear is fuelled by the abstraction though. Thinking that AI is replacing you is vague enough to not take action. The fuzziness is comfort and catastrophe. But the gatekeepers are real. Someone you respect or look up to will look at your work and possibly decide it’s interchangeable and treat you accordingly. Peer review is really everywhere. And it’s often done by a small handful of humans with budgets, reputations, and a short attention span. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. Here’s a 5-step process to become irreplaceable to gatekeepers: 1. **Know your exact fear.** The best way to defeat a possible rejection from these humans is to understand your fear voiced as their judgment. Who is potentially rejecting you on what grounds? Once you identified this, you can rebut it. 2. **Unearth your proof.** Remind yourself and them of everything you have already written. Everything from grants to stories of your supervision. If you don’t have lots of proof of your work, build it. 3. **Translate your proof for the exact gatekeeper.** Do this exercise without AI to train that translation muscle. What’s the shortest form in which you can present your existing proof to gatekeepers? Think it through. Deliver the value in a format that can be consumed in minutes. 4. **Don’t wait for approval.** Publish this when YOU feel it’s ready. Set yourself a short deadline to not overthink this and trust your judgment, knowing that you can always iterate on it. 5. **Put it in front of gatekeepers.** Whether you’ve written an email that lands in your dean’s inbox or you’ve written a LinkedIn post that shows up in their feed, you’ve produced visible value. Repeating this process makes you better at it. And repeating the production of such artifacts in public compounds for you. Repetition is the antidote to replaceability. But you have to repeat the right process. The crucial thing you can’t forget here is that whatever you write, should be based on your work. On the research you’ve done or vetted. You have a distinct point of view. AI does not. It represents the amalgamation of everyone’ point of view. So, keep your stance and knowledge and question outputs. When everything tastes like bland cauliflower, people are dying to find a spicy carrot in their veggies. Be that spicy carrot. Lean into your uniqueness, but be aware of the arguments of your gatekeepers of why your work might not be good enough. Convince them otherwise. Imagine their claims and challenge them. Can you use AI in that process? Absolutely. It’s a fast research assistant and can check sources with the right prompts and plugins. But should you let it turn your carrot into a cauliflower? Absolutely not. Don’t wait for people to certify your work. Trust your gut over automation. You don’t need permission to sell your competence to the world. ## Bonus _This post is for paying subscribers only._ ### How to build an AI Workflow You'll Actually Use URL: https://lennartnacke.com/how-to-build-an-ai-workflow-youll-actually-use/ Last updated: 2026-08-11T17:40:30.000Z I’m announcing something special this Friday. 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If you’re struggling to make AI work for you, this session will help you work through your problems. --- —has taken our professional lives by storm. The more I’m using it in my day-to-day tasks, the more I feel I’m solving problems that I didn’t actually have in the first place. It is just too easy to go down a rabbit hole of “What if I just write a quick tool for this?”, and then find yourself 2-3 hours of fable prompting later when you have built a tool that you’ll likely only use once. You snort that peak productivity like cheap cocaine. Only, to feel an emptiness at the end of the day that is hard to describe. It’s the Pareto principle all over again. I produce about 20% amazing workflows, automation, and apps for the 80% junk that I end up throwing out or not even finishing after a full afternoon of attempts to fix it, rewrite it, or shelve it after I can’t quite get it to the polishing level that I’m happy with. On top of that, the 20 different agent tasks running on my computer feel like a busy conversation at the watercooler, out of which I can really only pick up one or two strands of intelligible conversation. My feeble mind is clearly the bottleneck here. Sometimes this fills me with frustration about how all these AI influencers on YouTube and social media are automating away their solopreneur businesses (I call BS!). Other times, it gives me hope for humanity and that not all of our jobs are going to be automated away much too soon. All of this hits an aspirational problem that I think many of you share with me. We always find something that we would like to do more of, but that we do not regularly do yet. You might find that your AI automations are competing with an existing habit instead of attaching to one. For example,, if you already read papers on Sunday mornings with your double espresso (or Latté if you’re like me), you will likely abandon a workflow that requires you to first pipe the PDFs through three different tools before you can even begin reading them. The cost is much higher than the benefits that you get from this task. Why? Because now you have an AI output review queue. And believe me, this is one of the insidious tidbits of AI work. Sure, it produces tons of output in minutes, but I still have to review it. And I’m a slow reader. So, you face two choices: Take your time (and accept that reviewing AI work is now part of your job) or just do it from scratch without AI. For many creative tasks, it ends up taking the same time. Just with AI, you’ve added work to remove work. “Mischief, thou art afoot.” If you feel like your AI work costs your more to maintain it returns on your time investment, it’s probably time to check your workflows. You shouldn’t have to invest 20 minutes a month to tune a workflow that saves you only 10 minutes. So, today I’m giving you a checklist of four questions that you can run before you build anything with AI to see if the balance sheet stays positive: ### 1\. Does it solve a recurring problem I actually have? Any problem that you face over and over again, whether it is during a day, a week, or a month, is worth considering automating. So, the first step is to just watch for activities that you notice you are repeating regularly in your work. Those are the most valuable ones to automate with AI. Let’s say you’re a policy researcher who briefs a committee every other week, writing those briefs is a recurring problem. A workflow that helps you structure or draft them is of true value to you. If you’re thinking, “I should probably read more broadly in adjacent fields,” that is just aspirational. Here, you’d wait to build the workflow until your reading becomes a recurring commitment or habit. Here’s a simple reflection question that you can ask yourself at the end of a week or a month: Did this exact problem slow you down in the last week/month? If you have to think hard about when it last came up, it’s not recurring enough to automate. I think the trap that many of us fall into is that we are immaculate at imagining valuable work, which means that we can always see an ideal version of how we’d do things. We could imagine a better knowledge base, a more optimal scanning of the recent literature, or a more elegantly organized research archive. However, we have to remember that a workflow doesn’t become useful because it supports the future person that we’d hope to become. It becomes useful only if it can support what we’re currently repeatedly doing. So, you always have to start with the work that you’re currently doing. ### 2\. Does it attach to my existing behaviour? As humans, we operate our daily lives on habits, repeatable structures that shape how we do things. The best way to wire a workflow into your workday is to attach it to a trigger that is part of something that you already do regularly. Let’s say I already know that I’m opening my email at 8 AM and then pull up the same analysis environment. Or I always draft a one-page summary before I have an important client conversation. I would consider these behavioural anchors and I could turn them into triggers for my behaviour. Workflows can be triggered well if they’re linked to these existing anchors and this is much easier to do than establishing a habit or a new behaviour from scratch. So, here’s another simple reflection question you can ask yourself: What did you do this morning that you do most mornings? The answer to that will give you an opportunity for creating a new workflow or automation based on your habit. For example, suppose you routinely annotate articles before adding them to your reference manager. An AI workflow that turns your annotations into three reusable components—a claim, a limitation, and a possible implication for your current project—fits such a habit. You don’t need to adopt it as a separate practice because you’ll just do it where you already work on a paper. By contrast, a weekly automated research intelligence briefing sent to a dedicated inbox may look sophisticated, but unless you read this, it won’t be useful to you. You have to ask yourself, if this is something that you’d check, or if it’s competing with everything else that’s demanding your attention currently. Just thinking you’ll read a brief because it’s created is unlikely to happen. It’s much better to work it into a ritual that you already have (e.g., reading a newspaper). ### 3\. Does it produce an immediately usable output? Good workflows shouldn’t take more than five minutes to clean up and put into action. The best output is output that you can act on right away. Otherwise, if you have to spend too much time re-editing everything, you end up resenting this. And then over time, this will just not naturally fit into your work, and you’ll just start skipping it. So, there’s no efficiency or productivity gain here. For me, five minutes is really the maximum I would invest into editing it. With the ideal being closer to 30 seconds of a quick review where I just scan the output, maybe tweak a line, and then use it right away. That, to me, is an actually efficient output. Let’s imagine you always write a one-paragraph framing note before presenting quantitative findings. Then, you should build a workflow that generates a first-draft version of that note from your results and a few keywords. Reviewing this will be manageable and feel familiar because you already know what the purpose of the paragraph is. This will also help you to understand what’s wrong in seconds. Or just imagine that your clients always come to you with the same questions repeatedly. Things about what a specific finding means, how you would justify a method, or what they could actually do with it. In these cases, you’d want to design a workflow that turns your research report into exact responses to these question types (think of it like an FAQ). It would still be based on your research report, it would just deliver the answers that they’re looking for much quicker. Problematic workflows will deliver formats to you that you’ve never actually used before, or they will use a voice register that you might not use in your own writing. These would need to be adjusted, and you’d have to improve the output quality just so that you don’t feel resistance to the artifacts you get. ### 4\. Have I run this manually three times before automating it? I’d almost say that this is the most important rule because to really understand a process, you have to run it by hand first. And I would say three times is a good minimum threshold to see how the process is actually done, so that you can describe it in detail. This helps set the quality bar for the automation you’re trying to build. It lets you understand what steps are essential to this workflow and what criteria determine the quality of the output that you get. The first manual run shows you what the task is. The second shows you what is annoying about it. The third shows you what matters most in the output. Going through this more labour-intensive run of the process three times will allow you to not guess about the task but have a solid understanding of it and your automation won’t miss the point of the task. The goal for any good automation is to model how you actually think through a task. And for this to succeed, you do have to go through the process of thinking through that task first. Otherwise, you might get something plausible and it might look right, but, at the end of the day, it’ll still feel wrong, and you won’t actually use it. ## Keep, Redesign, or Retire But even as you’re building these AI workflows (e.g., skills) and automations (e.g., routines), you have to keep an eye on the ones that actually provide benefit for you. That means running a weekly tally of workflows that you’re actively using. So you have to make three decisions here: whether you want to keep the workflow, redesign it, or retire it. This is the best way to keep your AI work manageable as it likely grows. **Keep** an automation/workflow if it runs as expected, produces usable output within five minutes of review, and you would notice its absence immediately if it broke. **Redesign** it if you use it occasionally but skip it more often than not, if the output usually needs heavy editing, or if you have started working around it. Redesigning means returning to the four questions and rebuilding from scratch. Don’t just tune the prompt. Rethink the workflow. **Retire** it if you can’t remember the last time you ran it, if you are running it only to justify having built it, or if the maintenance cost is higher than the return. As a simple rule of thumb, you should ask yourself after you’ve run a workflow/automation three times without producing anything that you’d end up using, now should be the time to change or delete it. Why even trim workflows, you ask? Because in theory there’s lots of storage available and a lot of the times you have more tokens than you need for your AI work. But there’s a mental overhead when you constantly run a system that’s only half managed, so do yourself a favour and clean it up. The next time you catch yourself mid-rabbit-hole, thinking “What if I just write a quick tool for this?”, stop and ask the four questions. Recurring problem. Existing anchor. Usable output in under five minutes. Three manual run-throughs before you automate. Any idea that makes it past this test deserves your afternoon, and the tool you build will still be useful next month, saving you time while everyone else reviews an inbox full of AI briefings that nobody asked for. The rest of your ideas can stay ideas. Close the tab, pour the Latté, and read your papers the way you always have. The high of building something you use every week beats the cheap stuff. Build better AI workflows that help you thrive in your own work. ## Bonus This week, Premium Write Insight + AI Research Stack members get the complete workflow audit kit: a Review Queue Balance Sheet spreadsheet, printable Automation and Keep, Redesign, or Retire guides, a downloadable Claude skill that interviews you about a repeated process and turns it into a reusable skill, and five paste-ready prompts for committee briefs, reviewer responses, paper annotations, recommendation letters, and quantitative framing notes. You also get 10 practical resources for deciding which systems deserve a permanent place in your week. Join Premium, run the audit, and reclaim the afternoons you now spend building workflows you abandon before your week ends.⁠ _This post is for paying subscribers only._ ### How Anthropic Turned AI Safety Into a Competitive Advantage URL: https://lennartnacke.com/how-anthropic-turned-ai-safety-into-a-competitive-advantage/ Last updated: 2026-07-28T18:20:17.000Z The older I get, the more the weeks feel like they're flying by me. This week was another successful week in my membership community, where I explained to the community members how you can create a grant writing skill with Claude that interviews you for your ideas and then helps you build the structure for the actual grant. It was a great session, but as I'm running these sessions and office hours every week, I'm feeling the drain of time from all the other things I do in public. Well, I guess also me not having my MacBook for half the week because I had to get the battery replaced didn't really help. I’m as far away from hustle culture as you could get. But student requests, grant coordination, customer emails, building things, it all feels like it swallows your day whole. I still ask myself sometimes how all of these online creators are creating so much content without ever burning out. Or maybe they’ve all just burned out and they just never admitted it in public and then just hired a team? And I’ve optimized my systems with productivity setups to the teeth. Either way, as I’m inching towards the end of my sabbatical, I am thinking deeply about how I can optimize my systems and create things that won’t burn me out as I’m going back to a full teaching load. I think a large part of this is the AI agent army that expects me at work every day. I’m typing so many different requests across Claude Codex, Hermes, Notion AI, Antigravity and others, that it feels like speeding up. However, it also means I’m juggling 10,000 tasks at once. My brain isn’t ready. I might forget a task that I started in the morning and only pick it up later in the day. And then as you’re changing computers or setting up your computer anew (like I just did), it all feels like your memory is scattered and fragmented across many different sessions. The soup can be hard to warm up. And I’m feeling the struggle to reconstruct what it is that I need in the moment more than ever. I’m still deep in building the early version of my membership right now, and it has me thinking hard about this exact topic: which parts of my own process are worth showing in public, and which parts I’ve been treating as overhead. And also the core question: Is it even worth for me to keep building this membership or should I pursue other venues? ## Get The Write Insight Translate your expertise into outcomes people pay for. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ### **Your rigour is the product** In 2020, OpenAI’s head of research quit his job. His next action could be interpreted as a disadvantage. Dario Amodei left the company with several senior colleagues, including his sister Daniela, who led safety and policy. They walked out over a disagreement about the shipping speed of the AI. They thought the company was shipping product faster than its safety work could keep up. Their actions at the time looked like a disadvantage. They incorporated Anthropic as a Public Benefit Corporation. Simply put, this is a for-profit company that writes its mission into its legal charter. So, the board can choose the mission over quarterly profits. They did not rely solely on human labels. Most founders treat this structure as a cost they want to avoid. Anthropic treated it as the product. They trained their model, Claude, using a set of rules they named a constitution. They did this to avoid leaning only on humans labeling good and bad answers.Anthropic shipped Claude, built on Constitutional AI, in March 2023\. Then, in September 2023, they published a Responsible Scaling Policy. This was a public document that outlined the conditions for refusing to train or deploy a model. The framework is based on biosafety levels used in labs for dangerous pathogens. It has graduated tiers that get stricter as the danger increases. They funded mechanistic interpretability tools — diagnostic probes and visualizations that inspect model internals so teams can spot deceptive behaviours before deployment. He framed his wager as a race to the top: set public safety standards and force competitors to meet them. I won’t bore you with the full funding‑round details. Every founder who has been told depth doesn’t sell should be bothered by the short version. March 2025 — investors valued Anthropic at $61.5B, early 2026—$350B valuation. Amazon and Google were investing tens of billions jointly. They were also preparing to go public. In the months following the scaling policy, OpenAI and Google DeepMind released their own versions. Anthropic says the policy caused this change. I’d say, more carefully, that it stopped being the weird one. Safety was not the only reason this worked. It helped substantially that Amazon and Google were hunting for an OpenAI alternative to fund. The timing of the language-model wave was pure luck. Plenty of rigorous labs have faded into obscurity. Depth alone is not enough. Put rigour on the front door. Anthropic took the most rigorous, least glamorous thing it did and put it at the front door. Rather than narrowing a niche, this approach is differentiating the wheat from the chaff. Niching asks how small you can cut your market until you are the only one left. Differentiating identifies the specific people who need a skill only you provide. Research-focused founders should apply the discipline they already use instead of overlooking it. The way you care about your method. Or your discipline to not over-claim results. All the things you consider essential for good research. When you start, you might treat that discipline as overhead. But your market treats it as scarce. I read AI labs’ safety claims carefully, because I study deception by generative models. So take this as a lesson in positioning and nothing more. I made the opposite mistake myself for years. I started by targeting PhD students because it seemed the natural choice. I narrowed the market to my obvious audience. Differentiating worked better. I kept the same level of rigour but focused on a smaller group who benefited most, and then I explained, plainly, why that rigour mattered. Make sure you get it on the public record before someone with half your depth and a louder feed defines your category for you. You win the next decade by putting your research on the public record now; customers, recruits and design partners will find you. The technical founder people already trust. Everyone else calls it overhead, you call it rigour. ## Bonus The full issue in The Write Insight includes the interactive Front Door Audit scorecard (12 statements measuring how legible your rigour is), a 4-phase legibility checklist, and 6 curated resources including the Constitutional AI paper and Anthropic's Responsible Scaling Policy. _This post is for paying subscribers only._ ### How to put good work into the spotlight URL: https://lennartnacke.com/how-to-put-good-work-into-the-spotlight/ Last updated: 2026-07-28T18:20:45.000Z You've heard it a hundred times. Quality speaks for itself. It ships. Gets noticed. Finds its audience. A big and comfortable lie we love to tell ourselves. This notion relieves you of the need to tell people what you made. That's why it outlives so much contrary evidence. It has never once happened. Gregor Mendel laid the groundwork for genetics in 1866\. He published his findings in the Proceedings of the Natural History Society of Brünn, after sharing them with the society in 1865\. The work was rigorous and essential. However, the field ignored its importance to a great extent for over 30 years. It was rarely cited. One common account mentions “about three” citations in about 35 years. A more cautious view says it showed up in several publications. But people didn’t grasp its main point. In 1900, Hugo de Vries, Carl Correns, and Erich Tschermak revived Mendel’s work. However, the rediscovery was more complex than a simple independent rediscovery. The point is the research went unnoticed the entire time. Douglas Prasher cloned the gene for green fluorescent protein (GFP) in 1992\. Prasher mailed clones, for free, to anyone interested. Martin Chalfie asked. Roger Tsien asked. Chalfie used Prasher’s clone to express GFP. Later, Tsien mentioned that his early GFP work was possible thanks to DNA he got as a generous gift from Prasher. After Prasher’s grant funding dried up, he shut down the lab and left science for a time. In 2008, the Nobel Prize in Chemistry for work built on his clone went to Osamu Shimomura, Chalfie, and Tsien. Prasher was a courtesy shuttle driver at a Toyota dealership in Huntsville, Alabama. The early work made no mention of his contribution. Peer review is often seen as a system that evaluates quality with no marketing. Even in that context, the paper needs a cover letter. Reviewers need a reason to look. They need a frame. A contribution statement. The editor's slush pile doesn't know which paper matters. Without a claim of significance, neither does the reviewer. Outside academia, the margin widens. Builders ship tools. Talkers tweet about the tools and land podcast invites. The builder gets the citation (if even). The talker gets the platform. Prasher got a nod in the Nobel banquet speech (and at least a paid trip to Stockholm). Unfair. Also true. I’m afraid that calling it unfair doesn’t change anything. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Quality earns trust This is how the system works: quality determines whether attention converts. A million people can ignore your work. Quality won't matter. But if a hundred people see it and five act, quality is why those five got inspired, why they stayed, why they recommended it, and why they paid for it. Attention drives on a much different fuel. Repetition. Narrative. Status. A platform focusing a spotlight. Quality provides none of those things. It doesn't surface in feeds. It doesn't pitch itself. It doesn't email an editor. I believed the opposite for years, and I'll admit the belief cost me. Not citations, but impact. I kept polishing work past diminishing returns, assuming polish was the only bottleneck. It wasn't. Too few people knew the work existed. My best-cited papers weren't my best papers. A co-author talked about them at the right workshop, and citations followed. It was timing. A little bit of luck. And distribution followed. Fiercely. ## Two domains at war Attention and quality don't cooperate. They compete for the same hours, and attention wins the opening battle every time. The attention domain runs on repetition, novelty, and volume. Rewards the loud. Punishes the patient. Daily lies beat one-time truths. The domain doesn't check your credentials. It doesn't value citations. It only cares about the headline. The quality domain runs on evidence, replication, and time. Rewards the careful. Punishes the rushed. A weak daily claim will fall apart under scrutiny one day. The domain doesn't reward speed. Volume doesn't matter. The facts underneath the headline do. Use rules from one domain to battle another. You will fail at both. An attention strategist will tell you to just post and skip the rigour. But if people find your work, they will discredit it. A quality strategist will tell you to skip the repetition. You will remain undiscovered. An actual audience will never get to appreciate your rigour. Mendel fought the quality war and won it long before anyone knew there was a war. His pea experiments were rigorous, quantitative, and foundational. He lost the attention war. He published in a local society’s proceedings and sent out reprints. He also corresponded with a leading botanist. Still, he could not get the field to understand his findings. His paper wasn't unread. It was misread. It was rarely cited. Colleagues missed the point. Filed it away as plant-hybridization work until 1900. Prasher fought the quality war and won it, too. His clones worked (for Chalfie and Tsien). His science was sound. The cDNA he shared was the starting point for work that made GFP a universal biological marker. Yet, the Nobel committee recognized other researchers for their work with GFP. He failed to wage the attention war. Lost control of the asset that could have kept him the hero of his story. Neither domain forgives a deserter. Skip the attention war and stay invisible forever. No matter how sound your work. Skip the quality war and burn out fast. No matter how loud your launch. Most people treat this as one campaign with one strategy. They are two campaigns. Two sets of rules, two different enemies, and no truce between them that saves you from fighting both. ## Everyone loves framing their work I'll spare you the sociology-of-science literature review. The short version is that even scientists—the group most committed to meritocracy—run on framing devices. Cover letters. Conference talks. A collaborator vouching for you at the right meeting. None of these inflate the work. A framing device doesn't claim more than the work delivers. It says this exists, here's why it matters, here's where it fits. It shines a spotlight the work cannot cast on itself. Without framing, the work sits in a database nobody opens. A tool sits in a repository no feed surfaces. A book takes its place among the two million others published this year. Framing doesn't make good work speak for itself. Nothing makes good work speak for itself. Framing makes good work findable. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## What this means for your work Don't feel depressed now. This distinction should free you. Stop waiting for quality to do a job it wasn't built for. No work is so perfect it defies invisibility. Not even the work that founded genetics. Two tasks make up your job here. First, visibility. Share it, say it plainly, repeat past the point of comfort. Second, quality. Good enough that your audience acts, returns for more, and recommends it. Most experts spend all their energy on the second task and none on the first. Then they complain about lack of impact as a quality problem and go polish some more. I get it. Polishing makes us feel virtuous. But it's just procrastination. Visibility generates attention. Quality converts attention. Two practices. One outcome. Impact. Mendel had the quality skill. Prasher had the quality skill, too, and mailed his impact away for free. Probably the better man. Probably also frustrated a little about someone else's Nobel win. [**If nobody has found the best thing you’ve made yet, take my quiz to understand why.**](https://tally.so/r/1AEQy1?ref=lennartnacke.com) Freedom begins when you stop waiting to be discovered. You got this, my friend. ## **Bonus** The Write Insight premium subscribers this week also get the Good Work Spotlight Kit: a 45-minute spotlight session, a seven-day sharing plan, printable framing and claim-check worksheets, and 4 AI prompts for turning one piece of work into a public case, auditing a draft, drafting a significance sentence and headlines, and tracking evidence and recipients. The kit includes worked examples, plus 5 curated resources on visibility, framing, self-promotion, and evidentiary rigour. _This post is for paying subscribers only._ ### How to map a research field in one morning URL: https://lennartnacke.com/how-to-map-a-research-field-in-one-morning/ Last updated: 2026-06-09T19:20:50.000Z Big shoutout to our [**Authority Lab**](https://www.skool.com/authentic-authority-lab-5429/about?ref=lennartnacke.com) cohort Barbara, Kenton, Ulu, Maricris, Michael, Ayca, Kim, Ahmed, Fiona, Pavla, Jia, Adi, Velai, Kirsten, Kitt, Orus, Tarek, Rachel, Leanne, Shaantanu, Shashi, Genevieve, Paul, and Tamsen, who are shaping this community as we are open to registrations now with biweekly office hours with me, once-a-month deep AI tutorials, and once-a-month open coaching sessions in front of the community. And we’re excited to host our first [​monthly mastermind​](https://store.lennartnacke.com/b/save-14-hours?ref=lennartnacke.com): The administrative swamp eats 14 hours of your week. Meetings, emails, and tasks you do not need to do. I’ll spare you the generic productivity hacks. On [​**June 11 at 11:30 AM Eastern**​](https://store.lennartnacke.com/b/save-14-hours?ref=lennartnacke.com), I am showing the 7 habits that close those leaks on my own calendar. We will schedule the 3 habits that buy back the most time for your research and writing. Beth, Ijeoma, José, Rabeya, Frances, Dianna, Amy, and Nikita already registered by today. [​Register here to save your seat.](https://store.lennartnacke.com/b/save-14-hours?ref=lennartnacke.com) --- Academic training made you good at one game and left you losing another. For years it rewarded a single audience, the two or three reviewers who sign off on your work, so you learned to read every paper cover to cover and catch every gap. Your instinct earned their approval. But in the visibility economy, this is burying you. While you grind through 50 papers, the writing that would make your expertise visible never ships, and obscurity wins by default. You slowly come to understand that the real bottleneck here is how you time your selection decisions. You waste a day deciding what to read while you read it. I urge you to make the call before you open the paper. Here's the system I use to map a field before lunch, it saved my weekends and pulled our last paper's remaining literature together in under 48 hours. ## The speed-reading trap I spent 20 years reading research the slow way, cover to cover, like most of the early PhD students I supervise. I used to open the first paper in a stack, read it line by line, examine every figure, and take dense notes. I believed highlighting and verbatim note-taking would lock the knowledge in. [Cognitive science shows us this is an illusion](https://journals.sagepub.com/doi/10.1177/1529100612453266?ref=lennartnacke.com). I love Zebra Midliners, but highlighting isn’t very effective. It locks your attention onto single ideas and buries the links between them. Those links are what let you make inferences and learn. Highlighting can even hurt *how* we make [inferences](https://journals.sagepub.com/doi/10.1177/1529100612453266?ref=lennartnacke.com#bibr269-1529100612453266). One for the keeners in my lectures. Transcribing what you hear word for word won’t help you understand the ideas either. [Copying closely isn’t enough for deeper understanding.](https://journals.sagepub.com/doi/10.1177/0956797614524581?ref=lennartnacke.com) It can hurt your performance on conceptual and application-based tasks. Mere transcription of thought relies on shallow processing. Writing key ideas in your own words, [a process called \*generative note-taking](https://journals.sagepub.com/doi/10.1177/0956797614524581?ref=lennartnacke.com),\* helps you learn much better. You pick key points, condense them, and rephrase in your own words. Then, you connect those ideas. Notes are useful only if you actively process them. Without active processing, you only look like you’re reading. But you won’t remember anything. The secret to processing 50 papers lies in your selection criteria. You succeed by refusing to read all 50 in the first place. Skimming is shallow reading with your comprehension switched off. Relying on it to get through your reading list is how you waste a full day. Trust me, I’ve done it. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Sort before you read a line The goal is to decide what matters before you open a single document. The way I do this is to start with an assessment and a prioritization. I take my stack of 50 papers and sort them into three piles. I do this using only the titles, abstracts, and figures. I keep the process simple to build momentum and save the difficult work for later. Here are the three sorting piles: Pile A holds my deep-read targets. These are the three to seven papers my argument actually depends on. I’ll read these with close attention. One test sorts them. A Pile A paper is one my thesis can’t survive without, because it supplies my core theoretical framework or the exact method I am replicating. Pile B holds only my mapping targets. These are the 15 to 20 papers I need to know exist to understand the boundaries of the debate, but that I don’t need to master. I put a paper here if it contextualizes my research question. It shows me who else is playing in the space. It also gives background stats and notes the viewpoints I should weigh. Pile C is my park or discard pile. The remaining papers go here. Make this decision in under two minutes per paper. If the paper’s main finding doesn’t connect to your research question, put it in Pile C. Don’t hold on to papers because they’re interesting or written by famous authors. My oldest mistake was treating all 50 as equal. I spent all my time on the first few documents and never got to the three papers that really mattered. Now I run the sort during the week with my research question written on an index card in front of me. If a paper doesn’t directly answer that question, it goes to Pile C immediately. ## The three-pass system The framework I use for Pile B and Pile A comes from the three-pass method introduced by [Keshav (2007)](https://doi.org/10.1145/1273445.1273458?ref=lennartnacke.com). In his classic paper, “[How to Read a Paper](https://doi.org/10.1145/1273445.1273458?ref=lennartnacke.com),” he shares a clear method. This method connects your deep reading to the process of making decisions. I’ll spare you the academic jargon and show you how it works in practice. Each pass narrows the field. Pass one runs across both Pile B and Pile A. Only Pile A survives into pass two, and just the two or three must-read papers reach pass three. Pass one is a quick five-minute scan. Focus on the title, abstract, introduction, section headings, and conclusion. You glance at the references to see who is being cited. At the end of the five minutes, you must answer his five C’s: 1. Category: What type of paper is this? 2. Context: What other work does it connect to? 3. Correctness: Do the assumptions hold? 4. Contributions: What’s the main claim? 5. Clarity: Is it well written? A shallow skim has no exit criteria. The five C’s give you five, and they make you read the paper’s structure. That’s how you process a reading list in a morning. Spend about one hour on pass two, focusing solely on Pile A. Read the paper carefully, study each table, and note references to follow up later. If you hit a difficult proof or a dense mathematical block, you put it down. The common mistake here is rereading the same page five times. Keshav’s fix is to leave the hard parts for the next pass, and I love it. Pass three takes four to five hours, and few papers in your pile warrant that much time. Reserve this pass for the two or three papers you must master or review. You rethink the study, question every assumption, and spot all the gaps. ## Structure your reading in a concept matrix A bibliography is a list of who said what. Build a review around authors and you get a string of summaries with no synthesis. Jane Webster and Richard Watson made this point in their paper, [*Analyzing the Past to Prepare for the Future: Writing a Literature Review*](http://www.jstor.org/stable/4132319?ref=lennartnacke.com) (Webster & Watson, 2002). They were tackling a shared issue in doctoral education and research. The issue is reviews that summarize past research in order, listing each contribution. This structure lacks synthesis and fails to expose gaps. To solve this, they introduced the concept matrix as a method to force researchers to structure literature around ideas. Their whole goal was to make a literature review concept-centric. I built a concept matrix. Rows are papers. Columns are claims, methods, samples, and findings. The matrix turns 50 separate documents into one map you can work from. For example, a concept matrix for research on educational gamification might look like this: __Concept matrix: educational gamification__ | Paper / Source | Self-Determination Theory (SDT) | Leaderboards | Extrinsic Motivation | Method | Key finding | | ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------- | ------------ | -------------------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------ | | [How gamification motivates](https://doi.org/10.1016/j.chb.2016.12.033?ref=lennartnacke.com) | Yes | Yes | No | Experiment | Badges, leaderboards, and performance graphs raise competence need satisfaction and perceived task meaningfulness. | | [Assessing the effects of gamification in the classroom](https://doi.org/10.1016/j.compedu.2014.08.019?ref=lennartnacke.com) | Yes | Yes | Yes | Longitudinal field study | Over a semester, leaderboards and badges lowered students' motivation, satisfaction, and final exam scores. | | [An empirical test of the theory of gamified learning](https://doi.org/10.1177/1046878114563662?ref=lennartnacke.com) | No | Yes | Yes | Experiment | Students with a leaderboard spent far more time on task, and that time raised their academic performance. | Laying each paper out in a breakdown like this makes the gaps obvious. If five papers share the same experimental design and ignore motivation, start your next paper there. ## Three AI checkpoints worth considering First, I use semantic academic search engines like Consensus or Google Scholar Labs before collecting documents. I skip the hassle of boolean keywords and just type my research question into the search bar. This method finds important papers that regular keyword searches miss. It speeds up my discovery process. Second, once I’ve collected these papers, NotebookLM is excellent for finding where they disagree. I prompt, “Where do these papers contradict each other?” The tool shows the passages, and I click the citations to verify the source material on the spot. Third, Claude is useful for drafting the synthesis from a concept matrix. Instead of copy-pasting raw text, I upload the matrix either as a CSV, an Excel file, or provide a link to the Google Sheet where it lives. This lets Claude ingest the structured data cleanly, so I can ask it to draft the synthesis and argue against my interpretation. Never trust either tool to generate a citation. The risk of fabrication is high, and grounding only lowers that risk. I tell my graduate students and research coaching clients to run this workflow during the week. They don’t need to work weekends. They process their 50 papers in business hours because they stop trying to read every word. This system kills the need to read all 50 papers. You read the few your argument stands on and map the rest. AI can compress a whole literature faster than any of us. The judgment of what to read yourself stays with you. That’s how our last paper’s literature came together in 48 hours, with one index card on the desk. Your turn. Grab your next stack of 50 papers, sort them into three piles before you open a single file, run the three passes, and drop what survives into a concept matrix. Give it one morning. Then hit reply and tell me what your index card said. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. # Bonus *This week, premium subscribers get a print-ready concept matrix workbook, the full one-morning field-mapping protocol as a checklist, three AI prompts (triage 50 abstracts into three piles, run the five C's on any paper, and turn a gap into your paper's angle), and five curated resources on academic reading and synthesis.* _This post is for paying subscribers only._ ### How to reclaim 24 hours a week and turn them into authority URL: https://lennartnacke.com/how-to-reclaim-24-hours-a-week-and-turn-them-into-authority/ Last updated: 2026-06-02T18:00:51.000Z It's 10 PM on a Sunday as I am writing this. You told yourself this was the week to finally publish that post. It would show your true knowledge to everyone. You didn't—again. Your tiredness hides a feeling of shame, because the week wasn't even that productive. You can't say where that time went. I've been there. A week is 168 hours. It's a fixed number. Simple math. When a busy senior expert feels short on time, more discipline won't help. Discipline just adds more tasks to an already packed week. It treats the symptom. Every week, 20-30 hours vanish. They slip away into admin tasks, fragmented work sessions, and distracted family time. Maybe you spend that time babysitting your AI agents. But really, you’ve never figured out where they actually go. I've been in this situation. Heck, sometimes I even still am. You could use those lost hours to put yourself in front of people and make your work visible. This newsletter issue helps you heal that wound. A tenured professor's field-defining work never leaves the journals. An industry researcher's name stays locked inside the company, and often remains unpublished. An ex-academic explains their credentials on every sales call. They do this because no one already knows who they are. Expert founders watch hollow competitors win spaces they should own. You have the credentials but remain unseen. It's because you tell yourself you lack the time to build a public presence. I write this while managing a research lab, running a company, and publishing a newsletter for 14,000 readers. So, this isn’t just theory from a random office. I feel your pain. Today, I'm giving you two systems. First, a quick one-week inspection saves you about 24 hours. Then, a publishing schedule turns some of that time into authority. Reclaim the time. Spend a little of it to make yourself visible to the public. 📆 Before we start, one thing worth a spot on your calendar. On ****June 11, 2026, 11:30 AM Eastern Time,** [I’m running a live 60-minute mastermind, Reclaim 14 Hours a Week](https://store.lennartnacke.com/b/save-14-hours?ref=lennartnacke.com). I show you the seven habits you can build in that single hour, the leak each one closes, and help you schedule the three that fit your week. You leave with a fill-in workbook and the recording. Seats are $49 and limited. [​Save your seat​](https://store.lennartnacke.com/b/save-14-hours?ref=lennartnacke.com). ## The hours are not hidden Before you hunt for lost time, sit with a finding that pulls against the whole idea of hidden hours. Recently, I watched productivity expert [​Laura Vanderkam​](https://lauravanderkam.com/books/?ref=lennartnacke.com) on [​Cal Newport's podcast​](https://www.thedeeplife.com/podcasts/episodes/how-do-i-reclaim-my-schedule-w-laura-vanderkam-monday-advice-2/?ref=lennartnacke.com). She has read thousands of real time logs for her books, including [​*168 Hours*​](https://lauravanderkam.com/books/168-hours/?ref=lennartnacke.com) and [​*Big Time*​](https://lauravanderkam.com/books/big-time/?ref=lennartnacke.com). She found that the time is almost never missing from people's lives. People fill it with low-quality default leisure they don't even remember. But then we tell ourselves a story of having no time at all. Time is a limited resource. Once you realize the hours are always there, you see you might be miscounting how you spend them. This realization helps you fix an inaccurate record. And running a business when overloaded is not a matter of cutting everything back to nothing. Vanderkam draws a line that's worth keeping. Complexity and chaos are not the same thing. Chaos is the enemy of productivity. Complexity is fine. Life is complex by design for: - A consultant with three client tracks. - A founder with a product and a pipeline. - A professor with a lab and a venture. You face this complexity like a conductor faces an orchestra. Calm, rehearsed, every move timed. A real orchestra is not chaos. It is a tightly organized operation, where nobody gets a solo at the wrong moment. Aspire to that. Create the symphony. I'll spare you the full literature review. Here is my system. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Track and assess one of your weeks Set a silent alarm on the hour. When it goes off, write down what the past 60 minutes held, in 15-minute slices. This includes email, scheduling a client call, a school pickup, focused work, and TikTok scrolling you don’t want to admit to. Be brutal. Memory tricks you. It makes you think you had more productive blocks by smoothing over the distractions and gaps. A simple spreadsheet works better than your weekly perception. So does any good time-tracking software. Seven days. No edits, no judgement. Most people stall right here, because honest tracking is uncomfortable. Log the ugly hours first. Three days of lunchtime scrolling and 90 minutes of half-attention email is data. Treat it as data. At the end of the week, sort each hour into a few categories that suit your life: - Deep work that grows your business - Visibility work you plan to start - Family and home time - Admin tasks - Paid work - Sleep Researchers and entrepreneurs most commonly experience two issues. Admin friction runs 15 to 18 hours a week; two full workdays are lost to inbox, scheduling, and status pings. Constant Slack notifications create a persistent drain on productivity. The worst part is that constant interruptions force you to work in short, 30-minute chunks. These fragments are too brief for deep focus and hurt your working memory. Leroy's research shows that when you switch tasks, some attention lingers. It stays on the previous task. A 22-minute work block runs at lower capacity. This happens because your brain is still working on the last task. This mental fragmentation explains why your 24 hours feel unproductive despite constant effort. ## Use the few hours you have for the right effort Now you have recovered time, and the instinct is to spread it thin across everything you're behind on. Resist that. Anders Ericsson's 30 years of research show that focused creative work lasts 3-4 hours a day. A senior expert can manage about 10 to 15 hours of focused work each week, no matter how much they try to do more. Protect one small piece of it, just 2-3 hours, for the one task that strengthens your authority for the next 10 years. Publishing what you know. The objections are always the same, and I get it. You don't have a free afternoon to write. Guess what. You don't need one. In 1989 the behavioural psychologist Robert Boice tracked 20 academics for a year. The group that wrote in brief sessions of 10-20 minutes produced 157 revised pages. The group that waited for empty afternoons and binged produced 17\. A 9x gap, same talent, same topic. Brief and frequent writing beats rare and heroic. Three actions help weekly publishing last a whole week. 1. **Capture before you draft.** The blank page is a capture problem. Write a note after each client call, every paper you read, and any surprising conversation. By the time you sit down, you are assembling from thinking you already did. David Perell calls it writing by sorting. 2. **Work inside a constraint.** James Clear ran his newsletter twice weekly for three years. I don't know how he did it. But he buckled under quality variation and moved to once weekly with a fixed structure. The structure removes the open-ended drafting struggle. Pick one format and reuse it. Three ideas from him, two quotes from others, and one question directed at the reader. You know what done looks like before you begin. I need to work on that. 3. **Draft in one 90-minute block, and log the minutes.** Pull three to five notes that connect, write the thesis, then the subheads, then fill. Boice found that tracking minutes improved output, just like daily writing. If a piece runs over four hours, your format is wrong. Again, something I am still struggling with. ## Test it while using it A system you renegotiate every week falls apart in a busy week. I like the concept of house rules that Newport and Vanderkam discussed. These are standing decisions you make once and never revisit. Her family eats pasta every Monday so nobody debates dinner. Yours might be "I publish Tuesday mornings," "admin happens in two windows, not all day," and "I stop at a fixed time each night." Cal Newport's *A World Without Email* makes the similar case for your inbox. Clearing it is not the work your business gets paid for, so stop turning inbox zero into a video game. My inbox is crowded. I search for what I need to find. Batch it. Give each message the time it deserves and not a minute more. Or let an AI agent surface important emails. Then build the net before you fall. Pre-declare a fallback cadence. When the grant deadline or a sick kid happen, and one of them will, you ship the shorter piece instead of vanishing. I've done that a couple of times with this newsletter and I still struggle to deliver on the same day each week. From time to time the system drops the ball, too, but those are exactly the moments you built your fallback for. ## Your path forward Build your time-saving habits it in this order. 1. Track this week, every 15 minutes, no edits. 2. Sort the hours next Monday and name your invisible loss. 3. The week after, protect one two-hour block and ship one short piece. 4. Set two house rules and one fallback cadence before you need them. Track minutes from day one. Peykar and colleagues (2023) conducted a trial with 132 nurses. They found that time-management training reduced work-family conflict and distress. However, this effect faded without a refresher. So re-audit at the end of a month and compare the results. Start the tracker tomorrow morning. Next Sunday at 10 PM, instead of feeling that guilt, you will know where all 168 hours went. One of those hours will show your published work. It will reach the audience you need. **The Write Insight* subscribers with an AI Research Stack premium account this week also get this bonus: A one print-ready PDF worksheet (a one-page Invisible-Loss Reckoning Card), 3 AI prompts (sort a raw time logs, diagnose where your week leaks hours, and design a rebuilt template), a visual reckoning protocol, 5 curated resources on time tracking and attention, and a two-week audit-and-rebuild rollout.* # Bonus _This post is for paying subscribers only._ ### Why Do AI Drafts Sound Generic And How Do You Fix It? URL: https://lennartnacke.com/why-do-ai-drafts-sound-generic-and-how-do-you-fix-it/ Last updated: 2026-05-24T04:17:08.000Z #### TL;DR - AI drafts sound generic because the brief is missing, not because the model is weak - 95% of organizations got zero measurable return on generative AI pilots in 2025 - Frontier models behave sycophantically in 58.19% of evaluated cases - Seven strategies (skeptic prompt, voice system, deep recording, ICP, social proof, segmentation, context stack) move AI from vending machine to strategic partner In today's newsletter, I'm breaking down the seven strategies that turn AI from a vending machine into a strategic partner, in the order they need to be set up. Based on today's newsletter, I've also recorded this as on overview on YouTube, check out the newest video: Quick update: [​I made a **quiz**​](https://tally.so/r/1AEQy1?ref=lennartnacke.com) that lets you self-assess why you aren't the obvious choice for your clients yet. If you're someone that is starting a business part-time or full-time and has an expert background, [​then that quiz is for you​](https://tally.so/r/1AEQy1?ref=lennartnacke.com). [​**The Authority Lab**​](https://www.skool.com/authentic-authority-lab-5429/about?ref=lennartnacke.com) had another open coaching session this week for member Balaji, where I helped him transition from an academic position in AI for scientific simulations toward building a personal brand and potential business. One of the interesting nuggets I touched on in the session was that side benefits compound even if your business does not take off right away. Speaking invitations, board seats, funder confidence, and conference programming flow toward visible authority regardless of whether the commercial offer scales. [​If you want to be part of the Authority Lab, join us here.​](https://www.skool.com/authentic-authority-lab-5429/about?ref=lennartnacke.com) I've also settled on the date for our upcoming [​**Mastermind**​](https://writeinsight.ca/mastermind?ref=lennartnacke.com) (Thursday, June 4, 12 PM ET). Check your email to help me choose the topic. ## What Causes AI Drafts To Sound Generic? Everyone at first treats AI like a vending machine. Put in a prompt, get out content. The output looks polished but is starting very much to sound like everyone else. [MIT’s 2025 NANDA report on the GenAI Divide](https://mlq.ai/media/quarterly%5Fdecks/v0.1%5FState%5Fof%5FAI%5Fin%5FBusiness%5F2025%5FReport.pdf?ref=lennartnacke.com) found that 95% of organizations got zero measurable return from generative AI pilots. Only 5% extracted real business value. The other 95% bolted AI on without defining what it was supposed to do, for whom, and in what voice. I think this is one of the most common problems of AI use in business that we’re currently seeing. People are rushing to implement it in everything without having a proper plan or even strategy for how it can improve their work or output. So I figured I might as well share my seven strategies below to try and close that gap. You can set those up in three phases (I talk about [this in my YouTube video](https://www.youtube.com/watch?v=rVCJks8yreY&ref=lennartnacke.com)): foundation, execution and distribution. ## Strategy 1: Use AI as a paid skeptic Language models default to agreeing with you, because agreeable answers score higher in their training data. [SycEval (Fanous et al., 2025)](https://arxiv.org/abs/2502.08177?ref=lennartnacke.com) tested ChatGPT-4o, Claude-Sonnet, and Gemini-1.5-Pro and found sycophantic behaviour in 58.19% of cases. So, when you ask a frontier LLM about how you can improve a landing page. You usually just get average assumptions for what that would look like. And it probably looks very pretty at first, but once you do a couple of those, you see how they all average out. So again, it makes sense here to reframe the question and tell the model that the campaign for the landing page already failed and ask it why. A good prompt for that would be: “Assume this landing page converts at 0.3%. List the five most probable causes ranked by likelihood.” Fifteen minutes of this before every launch saves a month of polishing the wrong thing. Try it. ## Strategy 2: Build a brand voice system first If you don’t have any reference documents or a specified voice document and examples, you will feel that AI just has this kind of generic flavour to its writing, which evens out any of the bumps and interesting tidbits that you usually find in your writing. The texture feels rather flat, but you should know that the texture is what your readers usually come for. An easy fix to do that is to do a four-part file that’s quite easy to create with the help of AI or by hand. And that file should contain: 1. Your voice fingerprint, which covers your personality traits and the things that you’d never write down. 2. A vocabulary bank with your power words or the phrases that you don’t use or your preferred metaphors. Just like I like talking about the Game of Thrones metaphors. 3. You want to specify specific channel rules because you have usually different output channels that you’re writing for. Like LinkedIn is a different way of writing than a YouTube script or a newsletter. They all read different and even an academic paper is another option that could be in there. 4. A five-question QA evaluation checklist the model runs against its own draft before returning. Build it once. Load it at every session. Once you have a setup like that in place, it gets much easier to get really good output from an AI prompt specifically when you’re running it with an on-device tool like Codex, Antigravity 2, or Claude Cowork/Code. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Strategy 3: Treat one deep recording as 30 pieces of content The thing that I hear most from people that I work with is that they struggle to turn one good idea or one published research paper into many different pieces of content that they can use to advertise that work. And as a result of that, they struggle with time management. Now, one system that works for that really well is that you don’t draft every piece of content from scratch but that you record maybe a 30-minute conversation about your expertise (and you can record that right into AI by talking) or you could talk to somebody and create a transcript from that or you could write it down in a book chapter or a YouTube video and then you can create something from this original source that allows you to do short clips, another newsletter issue, maybe some LinkedIn post or an FAQ page or even sell an email course. The thing is you want to start with something substantial because this depth can’t be reverse engineered from a single tweet or even just a statement that you have. Now the statement or tweet can definitely be something that gets you started but you want to transfer it into those different formats and AI can really help you with that to chop that down into different formats that you can then use to draw the attention to that deeper bit of content that you’ve written at the beginning. This is a technique that has been popularized by many online creators such as Dan Koe. ## Strategy 4: Define a real ICP The thing that was hardest for me to learn when I started a side business on top of being a professor was to understand that the content that I create online and in my newsletter doesn’t really address my own problems, but it addresses the problem of the people that I want to do service for or that I want to consider potential buyers of my products or services. We call this market segment an ideal customer persona. And this is really important for somebody that builds their own business because if you have a fuzzy category like small business owners, you’re competing on price. But if you have a very specific end-user category such as senior researchers in their seventh year who want to leave academia for a commercial venture without losing intellectual rigour, then you have what we call an ICP. Here you just compete on specificity. And a great way to arrive at an ICP is to feed AI a stack of your client conversations, your coaching transcripts, or your discovery calls. Or if you’re not doing any of these activities yet, maybe just to have a talk with the people that seem to be interested in the work that you do, to try and tease apart what their pain points are, what their urgency triggers are, and the exact phrases that these clients use to describe what exactly they want from you. The narrower you can go there, the warmer the messages reaching out to you will be. ## Strategy 5: Distribute social proof from existing clients It can be really hard to get testimonials from the people that you work with. And most people just post one testimonial and then they move on. But the persuasive material that is already in your business is much bigger than just testimonials. You probably already have email threads, call transcripts, Slack direct messages, maybe some survey responses, something like that. You want to mine all of that feedback that you’re getting from your target audience to get the exact phrases that clients use to describe the before and the after state of working with you. And these are the kind of snippets that you want to drop into email welcome sequence, maybe a booking page, a newsletter, a blog post and an ad copy. This kind of proof works when a buyer is at a decision point. For example, here is a video I recorded with my coaching client Jia: ## Strategy 6: Segment your list with behavioural tags Now when you create an email list, this is usually a great way to communicate directly with your audience. And the nice thing about an email list is that you can send regular updates, just as what I’m doing right now with you, where I’m giving you relevant messaging that is interesting to the majority of my email list. The interesting bits come from then segmenting your list based on the preferences. See, for example, that I asked you a question in a poll at the beginning of this email, where based on how you answer to that, I will then try and optimize my messaging for you (in addition to picking a great mastermind topic). A [2025 case study on Klaviyo by Oguta and Eling](https://doi.org/10.51244/IJRSI.2025.12060086?ref=lennartnacke.com) reports that behaviourally segmented campaigns hit 42.5% open and 18.3% click-through, against 28.7% and 9.5% for unsegmented broadcasts. The interesting part here is that the writing volume didn’t change, but the engagement was much higher because the subscribers stopped getting content that they didn’t feel they signed up for. ## Strategy 7: Build a context stack the AI loads before every session This is one of my favourite strategies and the one that my highest-performing clients credit for every good output. Because the generic output from models is that they serve everyone very broadly, so they fit no specific business venture very cleanly. They also don’t fit a specific academic project very cleanly. You want to close that gap by writing enough context that you could have a brief you could give to consultant that would help you day by day. This is everything based on your personal or your company background, your ICP, pricing, voice document, past campaigns and their results, SOPs, banned phrases. Sure, it might take you two or three hours to get this started and created in the first place, but every session can load it, every interaction can reference back to it, you can reference it in your [CLAUDE.MD](http://claude.md/?ref=lennartnacke.com) file, and having such a context stack is really what makes all the difference in output quality that you get from AI. Without it, the other six strategies underperform. ## When things go wrong The common trap is reaching for distribution work while the foundation is still missing. Segmentation and personalization can’t compensate for an undefined voice or a vague ICP. If a draft still sounds generic after Strategy 7, the context document is missing voice or audience detail. It takes a while to get these right and provide enough context. Add it. Regenerate. Ship. ## Your path forward If we’re breaking this down into the three phases of foundation, execution, and distribution, I would spend two weeks on writing the AI context document and building the voice system and spending some time to define your ICP. Then I would move towards the execution phase, which would probably take you another three weeks to do properly, where you set up the recording engine, run some background research on the next campaign and mine your existing library for any proof. Of course this depends on how many documents you already have. And then I would finish with the distribution phase, which is probably four weeks at the very end to get going where you’re setting up the segmented email and the personalized sequences. And then you just let it compound over time. But you have to build the brief first. And then once you’re in the distribution phase, you will see that the speed will follow. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. #### FAQ ****Why do my ChatGPT drafts sound generic?** Because the model defaults to averaged, agreeable outputs. Sycophantic behaviour appears in 58.19% of frontier model responses \[2\]. Provide voice rules, ICP, and a context stack to fix it. ****What is an AI brief?** An AI brief is a structured document containing voice, audience, examples, and constraints that the model loads before generating. It functions like a creative brief for a consultant. ****What is the best AI tool for writing in your voice?** Any frontier model (Claude, ChatGPT, Gemini) works once you load a voice document and context stack. The bottleneck is the brief, not the model. ****How long should an AI context document be?** Long enough to brief a new consultant for a day, typically two to three hours of writing across background, ICP, voice, and past campaigns. ****What is an ICP in marketing?** ICP stands for ideal customer persona, a narrow definition of the buyer including pain points, urgency triggers, and language. ****Do segmented email campaigns actually perform better?** Yes. Klaviyo data from 2025 shows 42.5% open and 18.3% click-through for segmented campaigns versus 28.7% and 9.5% unsegmented. ****How many pieces of content can I get from one recording?** Roughly 30 across short clips, newsletter, LinkedIn, FAQ, and email course, depending on depth of the source. # Bonus _This post is for paying subscribers only._ ### What Is Enemy-Based Positioning (and Why Does Impact Need an Enemy)? URL: https://lennartnacke.com/impact-needs-an-enemy/ Last updated: 2026-05-20T22:03:45.000Z #### TL;DR (updated May 20, 2029 - Enemy-based positioning names the obstacle your reader is already fighting, not the reader themselves. - Vague expert positioning ("clear, evidence-based, inclusive") fails because audiences feel problems, not adjectives. - The formula: ****I help \[specific reader\] get \[specific outcome\] without \[specific enemy\].** - A good enemy is a habit, system, environment, or false belief → never a person. - Positioning theorists April Dunford and Donald Miller both argue conflict drives recall. #### What Is Enemy-Based Positioning? Enemy-based positioning is a copywriting and brand-strategy method where an expert defines themselves by the obstacle they oppose (e.g., a habit, system, environment, or false belief) rather than by credentials or features. It earns attention because readers recognize the friction immediately, then engage with the expert's nuance. I started last Friday’s [Authority Lab](https://go.lennartnacke.com/authority-lab?ref=lennartnacke.com) hot seat with the least heroic opening possible. “Hello everyone, or no one yet.” Then I sat there for a short minute, waiting to see if anyone would show up. I wondered if I should have sent an announcement. I wondered if the time was wrong. I wondered if Friday was a bad idea. Very strategic founder behaviour. Sitting alone in a video call, questioning my calendar choices. Then many people joined. Phew. Awkward minute over. And the session turned into one of the clearest conversations we’ve had [in the Authority Lab](https://go.lennartnacke.com/authority-lab?ref=lennartnacke.com) so far. The format was so simple. People brought their positioning statements that I helped them formulate over the last month. The classic “I help X do Y without Z” kind of thing. I expected us to clean up at least the language, but I had a bigger plan: Confrontation. So, we found the missing fight. ## What is positioning, and why do most expert statements fail? Positioning is the deliberate choice of which problem you solve, for whom, and against what alternative, a definition popularized by [April Dunford in **Obviously Awesome** (2019)](https://www.aprildunford.com/books?ref=lennartnacke.com). Experts love accurate language. I do. And this all sounds good until you watch it turn into pudding. Most expert positioning fails because it stacks careful adjectives ("timely, relevant, inclusive") that are technically correct but emotionally invisible. Readers feel problems. > "Safe language is the pot belly of positioning: accurate, comfortable, and useless in public." None of this language is wrong. That’s the problem. It's too safe to be useful in public. Your audience does not feel a pair of careful adjectives. They feel a problem. They feel the thing in their way. - They feel the athlete who performs beautifully in practice and freezes in competition. - They feel the meeting where nobody understands the local context. - They feel the student who wants the grade more than the learning. Your public positioning gets 100% better when it names that friction. Even better, it names what causes the friction. That’s the enemy. Impact needs an enemy. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## **Your customer is not the enemy** The word *enemy* makes thoughtful experts nervous. I get that. Most of the people I work with do not want to bash anyone. They do not want to flatten complex problems into cheap conflict. They do not want to become LinkedIn theatre people yelling about how everyone else is stupid. Good. Please keep that instinct. The trick is to aim the fire at the obstacle, not the person. → Your reader is the person stuck in the fight. → Your customer is not lazy. The enemy might be inertia. → Your client is not ignorant. The enemy might be the technical gap that makes a good decision feel risky. You become useful when you name the thing they are already fighting. It makes all the difference. If you make your customer the villain, they will defend themselves. If you make their obstacle the villain, they will lean in. They’ll finally feel seen. ## Who is the *enemy* in enemy-based positioning? The enemy is the obstacle blocking your reader's outcome. [Donald Miller's Building a StoryBrand (2017)](https://storybrand.com/building-a-storybrand-book-new/?ref=lennartnacke.com) frames this as the villain in the customer's hero journey. Valid enemies include habits (perfectionism), systems (assessment-driven schooling), environments (algorithmic feeds), and false beliefs (nuance = trust): - A habit: Saving habits that drain money, writing habits that keep smart people polishing the first paragraph for three weeks, research habits that make every new project start from zero. - A system: Assessment systems that reward performance over learning, academic systems that ask for public impact while punishing anyone who writes in public, corporate systems that turn clear thinking into slide sludge. - An environment: The 9-to-5 treadmill, the inbox, meeting culture, the algorithmic casino pretending to be professional networking. - A false belief: “If I am more nuanced, people will trust me.” “If the work is good, the right people will find it.” “If I use AI, my voice will disappear.” “If I take a public stance, I will lose my intellectual integrity.” These enemies work because you already feel them and your audience does, too. You’re naming the conflict that has been running in the background. ## **The moment it clicked** ![A digital whiteboard displays interconnected concepts like customer, problem, friction, and environment, while a speaker leads a discussion.](https://lennartnacke.com/content/images/2026/05/image-1.png) Our session in the [Authority Lab](https://go.lennartnacke.com/authority-lab?ref=lennartnacke.com). One Authority Lab member works with athletes. His first framing was about performance pressure. Athletes train well, then score lower in competition. The obvious solution was mental training. Useful, but still a little broad. So we kept digging. The enemy was the inner voice. The self-talk. The fear of losing. The fear of winning and then having to keep winning. The tiny internal commentator that turns a competition into a referendum on your entire identity. Now the positioning had heat. Spicy. I loved it. He could stand against that voice. Another member teaches pre-service teachers. Her first framing was full of strong ideas: Creativity, process-based learning, risk-taking, student engagement, meaning-making. All good. Still too clean. Then she named the thing she stands against it all: Shallow learning. The grading system. The product-based logic. The classroom habit where students learn to ask, “How do I get the A?” before they ask what the work is trying to teach them. That is a real enemy. Boom. Everyone who has been through school recognizes it immediately. Even AI fits this story. AI did not create shallow learning. It accelerates the shallow learning already built into the system. Now, she had a sticky point of view. One member works with literary scholars. Her challenge was different. She resisted the customer language, which made sense. In scholarship, people do not always think in terms of markets and solutions. They think in terms of knowledge, methods, traditions, and ways of seeing. So we stayed with that. The enemy became institutional sameness. The Eurocentric lens. The habit of talking about diversity while keeping the same old epistemological frame. That’s strong. It gives her public access into deep work without making the work shallow. You know, this is the piece that many experts are afraid to admit. An enemy does not reduce your nuance. It earns attention long enough for your nuance to matter. ## **A simple positioning test** ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. Most expert writing fails because it always starts with the answer. Here is my - framework - method - service - research The reader has no reason to care yet. They don’t know what fight you’re entering. They have to learn about what problem you’re refusing to tolerate. They must be convinced why your sentence should interrupt their day. Friction creates this hook. This is true in stories. A good story needs pressure. Something has to be at stake. A character wants something, and something blocks them. Public authority works the same way. You want to be known for something. Your audience wants a result. The enemy blocks the result. Your position becomes the stance you take against that enemy. That is where attention starts. Forget your list of credentials. Move past your polished bio. Don’t write another sentence that sounds like it survived three committee reviews and a light afternoon nap. It starts with a fight. Take your current positioning sentence. Find the outcome. Then find the obstacle. Most people stop too early. A little less Mr. Spock, a little more Captain Kirk. They write: → I help experts communicate their ideas more clearly. Fine. Clear enough. Also forgettable. Now add your enemy: → I help experts communicate their ideas clearly without cutting off the depth that made those ideas valuable in the first place. Better. The sentence now has friction. It names the fear many of your carry: That visibility will make you shallow. And, yes, that’s how I position myself. Here is another: → I help researchers write papers faster. Useful. Flat. Add the enemy: → I help researchers write papers faster without letting perfectionism turn every draft into a swamp. That’s what I have done for years now. Now, the reader can feel it. They know that swamp. They’ve lived in that swamp. They’ve probably decorated it like Shrek. The formula is quick and simple: I help \[specific reader\] get \[specific outcome\] without \[specific enemy\]. The *without* part is where the positioning gets serious. [BTW: I created a quiz that lets you check your current positioning.](https://tally.so/r/1AEQy1?ref=lennartnacke.com) ## How do I find my enemy? (A 3-step positioning test) Take your current positioning sentence, isolate the outcome, then name what blocks that outcome for 80% of your audience. [According to Nielsen Norman Group research on landing-page comprehension](https://www.nngroup.com/articles/how-long-do-users-stay-on-web-pages/?ref=lennartnacke.com), users decide relevance in under 10 seconds, which means the obstacle must appear in the first sentence. ![A visual guide outlines a formula for effective communication, highlighting readers, desired outcomes, and obstacles to avoid.](https://lennartnacke.com/content/images/2026/05/image.png) The positioning formula on a napkin: reader, outcome, enemy. ## Why does naming an enemy increase attention without reducing nuance? Conflict is the oldest attention mechanism in narrative. [Harvard Business Review's analysis of B2B messaging](https://hbr.org/2018/03/the-b2b-elements-of-value?ref=lennartnacke.com) found that contrast-driven value propositions outperform feature lists in recall tests. Naming an enemy front-loads contrast, so that your nuance then has somewhere to land. > "An enemy does not reduce your nuance but it earns attention long enough for your nuance to matter." If your positioning feels vague, you probably do not need more adjectives. You need a sharper enemy. Name the habit, system, environment, activity, or false belief that blocks your reader from the result they want. ## How is this different from negative or attack marketing? Enemy-based positioning focuses on obstacles. The customer is the hero and the enemy is the friction. Attack marketing names competitors or shames audiences, [a tactic the FTC notes can violate truthful-advertising standards](https://www.ftc.gov/business-guidance/advertising-marketing/truth-advertising?ref=lennartnacke.com). Enemy-based positioning stays empathetic to the reader while being smart about the system. Keep your empathy for the person. Aim your fire at the obstacle. That is how your writing gets clearer. That is how your hooks get stronger. That is how your authority starts to feel like a point of view instead of a professional summary. ​[I am opening the waitlist for a free Authority Lab Mastermind called ](https://writeinsight.ca/mastermind?ref=lennartnacke.com)[**How to create hooks for your writing**.](https://preview.kit-mail3.com/click/dpheh0hzhm/aHR0cHM6Ly93cml0ZWluc2lnaHQuY2EvbWFzdGVybWluZA==?ref=lennartnacke.com)​ We will take this exact idea and turn it into a practical hook system. Bring the topic you keep circling. Bring the sentence that feels too soft. Bring the idea that deserves more attention than it is getting. We will find the fight hiding inside it. #### FAQ ****Q: What is enemy-based positioning in one sentence?** It is positioning that defines an expert by the obstacle they oppose, not the features they offer. ****Q: Can my enemy be a competitor?** Generally no. Effective enemies are habits, systems, environments, or false beliefs that your audience already feels, competitors invite legal and reputational risk. ****Q: How do I know if my enemy is specific enough?** A stranger in your target audience should nod within 10 seconds. If they ask what you mean, the enemy is still abstract to them. ****Q: Does this work for academics or scholars?** Yes. Scholars can position against institutional sameness, methodological orthodoxy, or epistemic flattening, all without market language. ****Q: Won't naming an enemy make me look polarizing?** Only if you target people. Targeting an obstacle increases recall while preserving credibility, consistent with StoryBrand research. ****Q: What is the positioning formula?** **I help \[specific reader\] get \[specific outcome\] without \[specific enemy\].* ****Q: How often should I revisit my positioning?** At least every 6–12 months, or whenever your audience changes. Dunford recommends re-testing whenever win rates drop. ## **Worth your time this week** _This post is for paying subscribers only._ ### How can experts use AI for expert knowledge extraction? URL: https://lennartnacke.com/expert-knowledge-extraction-ai/ Last updated: 2026-05-04T18:48:39.000Z #### TL;DR (Updated: May 4, 2026) - Expert knowledge extraction helps experts turn tacit knowledge into clear writing before they draft. - The problem is usually extraction, not discipline, motivation, or another note-taking app. - [AI works best as an interviewer, observer, transcript analyst, and case sorter.](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/01/lee%5F2025%5Fai%5Fcritical%5Fthinking%5Fsurvey.pdf?ref=lennartnacke.com) - The strongest method combines familiar task observation, structured interviews, critical incidents, and reusable knowledge cards. - Experts should extract first, then edit. Drafting too early buries the judgement that makes the idea worth reading. #### What is expert knowledge extraction? Expert knowledge extraction is the process of pulling hidden judgement, examples, rules of thumb, and decision cues out of an expert’s work before turning them into writing. AI can help when it interviews the expert, analyses real work, structures cases, and converts raw notes into reusable knowledge cards. Every week, I sit down to write my LinkedIn posts with 20 years of research behind me, hundreds of customer conversations in my head, and a stack of ideas worth sharing. Then the dark screen wins. (I like dark mode, ok?) Not once in a while. Every week. This is how it always starts. I will not call it writer’s block. That phrase makes it sound romantic, like I am sitting in a French café, nibbling on my little baguette romantically in thought like a tortured soul, waiting for my muse to show up in a pretty scarf. My problem is less poetic. After 10 or 20 years of getting good at something, your ideas stop arriving as fleshed-out sentences. The problem is that I know too much. Sounds like a humble brag. But it’s not. It is the root of most of my writing problems, and it gets worse the better you get. Malcolm Gladwell would agree. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Why is expertise a writing trap? Expertise becomes a writing trap because the better you get, the less visible your own thinking becomes. You stop noticing the assumptions, shortcuts, and pattern recognition that make your judgment useful, so the reader gets conclusions without the path that made them credible. When you set out (like the little Indiana Jones that you are) to become exceptional at something, no one warns you about the cost of being hypervaluable as an expert. Depth makes you useful. It can also make you miserable at explaining what you know. You’re stuck because you’re stupid. Quite the opposite. The deeper you dig your vampire fangs into the neck of your beautiful domain, Edward, the more your assumptions fade into black. You stop seeing the system—the paradigm that you’ve slobbered up like lukewarm milk from science mama’s teats—because you have become part of the system yourself. You’re not Alex Hormozi speaking in quotable tweets at podcast interviews. You don’t have clean little sentences waiting in the idea pantry. Your knowledge lives in your timing, pattern recognition, and gut-level judgment. That is why you cannot just write it down. Much of what you know stopped being verbal years ago. Knowledge management scholar [Ikujiro Nonaka](https://academic.oup.com/book/52097?ref=lennartnacke.com) gave this phenomenon a name: **Tacit knowledge**. Tacit knowledge is experience soaked in judgment. It is the surgeon who spots trouble before the monitors confirm it. The consultant who reads a room in 30 seconds. The expert who sees the flaw in a methodology while a junior researcher keeps rereading the paragraph. You cannot squash tacit knowledge into a LinkedIn post. It doesn’t work like that. Nonaka’s [SECI model](https://ascnhighered.org/ASCN/change%5Ftheories/collection/seci.html?ref=lennartnacke.com) describes how knowledge moves from tacit to explicit through **externalisation**, meaning metaphors, analogies, and dialogue pull embodied judgment into words. Trying harder at a blank page will not do that. You need a way to drag the knowledge out before you can shape it. Most experts solve the wrong problem. They switch note-taking apps. Buy more AI tokens. Try a new morning routine. Blame discipline. The actual problem is extraction. The apps are innocent. You are the bottleneck. I mean that with affection. Mostly. ## Why do your words lag behind as your skill grows? Your words lag behind your skill because expertise flattens knowledge into instinct. Early in your career, you explain every step because you are still learning it. Later, the steps disappear into timing, pattern recognition, and judgment that feel obvious only to you. Early in a career, your knowledge still has grip. You learned it last week. You tested it yesterday. You explained it to anyone who would sit still long enough. Moving from novice to expert forces articulation because people keep asking you to justify your stuff. Then you become senior. People stop asking you to explain yourself and start asking you for advice. Simple decisions arrive like Amazon same-day delivery used to. The judgment feels instant. Your knowledge sinks into your body, and the trench between what you know and what you can write grows faster than your expertise. This means the smarter you get, the worse the writing problem becomes. The more you deserve an audience, the harder that audience is to reach. Expertise hides its own guts. The more you have, the less visible it becomes to you and to the people you want to reach. Then someone asks you to write an article. Or build a course. Or share your insights on LinkedIn. You turn into Jon Snow. Noble. Confused. Somehow still in charge of these shenanigans. [Camerer, Loewenstein, and Weber](https://doi.org/10.1086/261651?ref=lennartnacke.com) coined the term *the curse of knowledge* in a 1989 paper on asymmetric information. Better-informed people could not ignore what they knew, even when it cost them. [Fischhoff](https://doi.org/10.1037/0096-1523.1.3.288?ref=lennartnacke.com) mapped the same concept in 1975 through hindsight bias. Once people knew an outcome, they overestimated what they would have predicted before they knew it. What they knew changed what they thought could have happened. [Nickerson](https://doi.org/10.1037/0033-2909.125.6.737?ref=lennartnacke.com) reviewed evidence in 1999 that people use their own knowledge as the starting point for estimating what other people know, then correct too little. [Hinds](https://doi.org/10.1037/1076-898X.5.2.205?ref=lennartnacke.com) gave the expert version some bite that same year in *Journal of Experimental Psychology: Applied*. Experts guessed how long beginners would take. They did worse than people with some experience did. Debiasing attempts did not save them. Elizabeth Newton’s [1990 Stanford dissertation](https://gwern.net/doc/psychology/cognitive-bias/illusion-of-depth/1990-newton.pdf?ref=lennartnacke.com) made the problem concrete. Participants tapped rhythms of well-known songs and predicted listeners would identify them 50% of the time. The actual rate was 2.5%. The tappers could hear the song in their heads. The listeners heard random fingers doing a tap dance. You are that tapper. Sorry. Your audience hears random knocks until you translate your knowledge for them. But this is not a skill you can just summon like a Patronus Charm. You have to construct it slowly, Hermione. The blank page gets blamed too easily here. When, really this is more of a knockout match between tacit knowledge and the language you use. Joining that fight proves your knowledge has depth. You want to win this like Conor McGregor’s 13-second knockout of Jose Aldo did. ## Why won’t writing more fix this? Writing more will not fix this when the real bottleneck is how your extract your knowledge. A habit can produce more words, but it cannot automatically recover the hidden examples, decision cues, failed cases, and assumptions that make expert writing useful. Productivity advice loves repetition. Write every day. Build the habit. Push through resistance. It’s true, but only works when consistency truly constrains you. Having tacit knowledge is a different beast of a problem. Telling an expert to write more is like telling Terry Fox to sprint faster with one leg. Effort is not your missing ingredient. Knowing how to extract your knowledge is. If you’ve ever forced content onto a page without a system, you’ve likely produced one of these results: 1. Abstract writing that is technically correct and unreadable. (AI won’t help much.) 2. Beginner-level content that lacks some bite. 3. Nothing. Think of it as Eminem’s *8 Mile* freeze, except you are alone at your laptop at midnight. Mom didn’t make spaghetti and the hostile crowd hasn’t even arrived yet. You still cannot start. Change how to get things out of your brain and onto the paper before you judge what’s even on the page. Here are eight simple methods to help you do that. You don’t need all of them. Just pick two or three that match how your ideas are stuck. ## How do you extract first and edit later? Extracting first means capturing the raw judgment before trying to shape the prose. Use [AI to interview you, observe your work, analyze transcripts, compare cases](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/01/lee%5F2025%5Fai%5Fcritical%5Fthinking%5Fsurvey.pdf?ref=lennartnacke.com), and turn messy expertise into reusable material. Editing comes after the thinking has been pulled into view. Expert knowledge does not come out through one magic prompt. [Researchers usually extract it](https://journals.sagepub.com/doi/10.1177/10944281241271216?ref=lennartnacke.com) by combining 3 families of methods. They watch the familiar task. They interview the expert. They use special tasks that make hidden judgement visible. That sounds dry. It is also exactly what AI makes easier now. ### 1\. Brain dump and document capture This is the baseline because every other method needs raw material to work with. Set a 10-minute timer and write everything down. Worries. Half-formed ideas. Client patterns. Research findings. Concepts you keep circling. Unedited. Unstructured. Messy. Capture first. Organize later. Alternatively voice record or speech-to-text this part, but I highly recommend going through the agony of writing it down by hand. Then add the material experts usually forget to include because it feels too obvious. Slides. Emails. Meeting notes. Old proposals. Client audits. Screenshots. Loom transcripts. The documents around your work often contain the tricks your brain doesn’t even notice anymore. Save the pile to a local folder and prompt via either Codex, Claude Co-work/Code, or Antigravity: This borrows from Hoffman’s point about documentation analysis. Manuals, notes, and old artifacts can produce a first-pass knowledge base before any interview begins. What often goes wrong is capturing without review. [Ideas pile up and decay.](https://www.buildingasecondbrain.com/book?ref=lennartnacke.com) A 15 to 30 minute weekly review turns the dump into an asset. You scan, promote, and discard. Without that review, your capture system becomes an archaeological dig. You will find notes from 2019 and have no memory of writing them. The Raiders of the Lost Ark warehouse, basically. Full of important things. None of them findable when you need them. Use this as your daily baseline. It is your foundation. ### 2\. Think-aloud capture while doing the work Voice capture works best when you use it during the actual task, not 3 days later when your brain has already cleaned up the crime scene. Open a voice recorder (or even better an AI speech-to-text app like superwhisper, WisprFlow, or Monologue) while you review a draft, work through a client problem, plan a workshop, annotate a paper, or redesign an offer. Say what you notice. Say what you reject. Say which detail made you suspicious. Say the sentence you almost wrote and why you cut it. Then upload the transcript to AI and prompt: This adapts protocol analysis for normal people with jobs. The point is not to sound polished, but to catch the judgement while you have momentum. Use this when the knowledge lives inside a task you perform on autopilot. ### 3\. Observe your familiar task with AI Hoffman separates familiar task analysis from interviews for a reason. [Experts often explain the official version of their work.](https://doi.org/10.1006/obhd.1995.1039?ref=lennartnacke.com) Watching the work exposes the real sequence. Record yourself doing a small real task. Review a client page. Build an outline. Fix a broken paragraph. Make a go or no-go call on an idea. Use screen recording if the work is visual. Use audio if the work is mostly thinking. Then ask AI: This is real task analysis. No performance required. Use this when you know the answer, but you can't explain how you figured it out. ### 4\. Build a bank of test cases and tough cases [Klein](https://mitpress.mit.edu/9780262611466/sources-of-power/?ref=lennartnacke.com) also points out that experts reveal more when they work through test cases, especially difficult or unusual ones. Easy cases produce slogans. Tough cases produce thinking. Create a small bank of examples: - 3 routine cases - 3 messy cases - 3 failed cases - 3 cases where your standard advice did not work Then ask AI to interview you through them one at a time: Failed cases don't warn you. They're where your argument gets tested. Use this when your advice sounds too clean because you have removed the pain that produced it. ### 5\. Use a structured AI interview Most people use [AI to generate content](https://www.microsoft.com/en-us/worklab/work-trend-index?ref=lennartnacke.com). Use it to interview you. Unstructured interviews ramble. They can build rapport and warm up the domain, but [they are weak at extracting knowledge](https://mitpress.mit.edu/9780262532815/working-minds/?ref=lennartnacke.com). Structure makes the interview do work. Prompt AI like this: Add Feynman mode at the end: Use this when the expertise is deep, specific, and hard to pull out alone. Anecdotally, 45-minute AI interview can surface [more usable material than 10 hours of staring at a blank document.](https://doi.org/10.1518/001872098779480442?ref=lennartnacke.com) ### 6\. Reconstruct critical incidents A critical incident is a real event where something changed. For example, the client finally understood, the case broke down, the system failed, the advice worked under pressure, the audience reacted in a way you did not predict. This works because [experts remember judgement through cases](https://doi.org/10.1177/154193128603000616?ref=lennartnacke.com). Your best reasoning often shows up in one specific incident where something went wrong, and that moment revealed the pattern you rely on. Here's an AI prompt for it: This adapts event recall and the [Critical Decision Method](https://doi.org/10.1109/21.31053?ref=lennartnacke.com) for writing. The abstract topic gives you the TED Talk version. The incident gives you the usable material. Use this when you need a story, a lesson, or a better argument. ### 7\. Sort, rate, and build the decision tree Some knowledge only appears when you force comparison. Take 20 to 40 raw notes, examples, claims, or stories. Ask AI to turn them into cards. Then sort them into groups. Rate each card from 1 to 5 for audience pain, originality, evidence strength, and ease of explanation. Then ask AI: This adapts judgement tasks, scaling tasks, and decision analysis. Sorting exposes categories. Rating exposes priority. Decision trees expose the logic your draft keeps hiding from you. Use this when the idea feels tangled or your outline keeps turning into soup. ### 8\. Progressive summarization into a usable knowledge base Tiago Forte’s [progressive summarization](https://fortelabs.com/blog/progressive-summarization-a-practical-technique-for-designing-discoverable-notes/?ref=lennartnacke.com) solves the second-order problem. Capturing ideas is easy. Returning 6 months later and finding the usable signal is where the bodies are buried. Use 4 layers. 1. Layer 0 = the raw capture. 2. Layer 1 = the passages that matter on first review. 3. Layer 2 = the strongest material from those passages. 4. Layer 3 adds a short executive summary in your own words at the top of the note. Now add an AI layer to it. Ask: Most experts have piles of notes they never use because sorting them takes too much time. Progressive summarization cuts that effort before your future self is tired, rushed, and trying to convince themselves that 400 notes is a system. Use this when you have too many notes and no fast way to turn them into writing. ## How do you build an expert extraction system? Build an expert extraction system so every idea has a location to go to. Capture fast. Review weekly. Run a simple extraction session on a set schedule. The system has one job, which is to keep your expert judgment from fading before you can use it in your writing. Ideas do not arrive as a tidy backlog. They keep coming. Research findings. Client insights. Shower thoughts. Conversations that wreck your tidy little theory before you buy lunch for the team. You need 4 pieces in place: 1. **One inbox.** All captures go into one place. Voice memos, text notes, whiteboard photos, scraps from meetings. More inboxes create search overhead. 2. **Fast capture.** The tool must be faster than the idea can fade. Voice memo. Notes app. Index card. Format matters less than speed. [Anything slower than 10 seconds invites judgment](https://gettingthingsdone.com/books/?ref=lennartnacke.com), and judgment kills capture. 3. **Weekly review.** Put 15 to 30 minutes on the calendar. Scan the inbox. Distil the useful bits. Delete or archive the rest. Turn captured scraps into assets. 4. **Extraction rituals.** Schedule 30 to 60 minutes weekly or every 2 weeks for the Feynman technique, AI interviews, or Socratic questioning. Turn raw material into artifacts you can use. You know this feeling too well. Two 30-minute extraction sessions per week are much better than 10 hours of procrastination suffering because they attack your bottleneck. Your problem is often just a missing review cycle (see also Boice, R. (1990). *Professors as Writers: A Self-Help Guide to Productive Writing.* New Forums Press). [AI has made conversion faster](https://hai.stanford.edu/ai-index/2025-ai-index-report?ref=lennartnacke.com). Raw capture can become a draft in minutes. The remaining friction is getting the idea out of your head before you forget it. ## What is this really about? This is the gap between what experts know and what they can explain on demand. The blank page looks like the enemy. Deep knowledge often loses its words before the expert tries to write. I wrote this because I needed it myself. I still sit down every week with ideas in my head and nothing on the page. I struggle. And I have many systems. The shower thoughts still wash off before I reach my laptop. The blank screen still has its vampire fangs deep up my neck. My mental model changed when I named the process. Years of practice shape expertise until the words fall off. Laziness is not the culprit here. Imposter syndrome is not either, even though it loves claiming credit for the damage. This is just how expertise works. These methods interrupt the squeeze. They force tacit knowledge back toward language because experts need different tools than beginners. Your difficulty putting ideas on paper does not prove you have nothing to say. It proves you have been using the wrong tool. A surgeon does not use a screwdriver. An expert does not use a blank document and willpower. You are using the wrong tool. Experts often assume visible creators know more, think with more clarity, or have some gift for effortless output. In my experience, they have a system. Usually a boring one. The advantage is their setup. You have 20 years of insight locked inside you. The world has not heard it because no one gave you the right extraction tools. That ends now. Build one inbox. Schedule one weekly review. Run one AI interview on one topic you keep avoiding. You know more than you can say. Build the machine that gets it out. That is your edge. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. #### Author Bio Lennart Nacke, PhD, is Research Chair at the University of Waterloo and a full professor in human-computer interaction (HCI), gamification, and AI strategy for academics and founders. He has supervised 20 PhD students, sat on dozens of hiring panels, and published [over 300 peer-reviewed papers with more than 45,000 citations](https://scholar.google.com/citations?user=i-y5S2AAAAAJ&ref=lennartnacke.com). He helps serious experts build research-grade writing systems that make them known, trusted, and chosen, without the content hamster wheel, hype, or hustle. #### FAQ ****What is expert knowledge extraction?** A structured process for converting an expert's tacit judgement into explicit, reusable writing using interviews, task observation, and case analysis. ****How is this different from note-taking?** Note-taking captures what you already know in words. Knowledge extraction recovers what you know but cannot yet say. ****Which AI tools work best for expert interviews?** Any frontier large language model with long context (Claude, ChatGPT, Gemini, Grok) works. The method matters more than the model. ****How long does a useful AI knowledge interview take?** Plan 45 minutes for one topic with 10–12 structured questions before synthesis. ****Can I skip cognitive task analysis if I just write daily?** No. Daily writing improves consistency, not recall of tacit cues. Both are needed. ****Is tacit knowledge always extractable?** Not fully. SECI (Model of Knowledge Creation: Socialization, Externalization, Combination, Internalization) predicts that some tacit knowledge transfers only through shared practice. Extraction recovers the explicit-able layer. ****What is the simplest first step?** A 10-minute brain dump plus one 45-minute AI interview on a topic you keep avoiding. ## Sources 1. Nonaka, I., & Takeuchi, H. [*The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation*](https://academic.oup.com/book/52097?ref=lennartnacke.com)*.* Oxford University Press. 2. Nonaka, I. [*SECI Model of Knowledge Creation*](https://ascnhighered.org/ASCN/change%5Ftheories/collection/seci.html?ref=lennartnacke.com) (Socialization, Externalization, Combination, Internalization). Accelerating Systemic Change Network summary. 3. Camerer, C., Loewenstein, G., & Weber, M. (1989). [*The curse of knowledge in economic settings: An experimental analysis.*](https://doi.org/10.1086/261651?ref=lennartnacke.com) Journal of Political Economy, 97(5), 1232–1254\. 4. Fischhoff, B. (1975). [*Hindsight ≠ foresight: The effect of outcome knowledge on judgment under uncertainty.*](https://doi.org/10.1037/0096-1523.1.3.288?ref=lennartnacke.com) Journal of Experimental Psychology: Human Perception and Performance, 1(3), 288–299. 5. Nickerson, R. S. (1999). [*How we know — and sometimes misjudge — what others know: Imputing one's own knowledge to others.* ](https://doi.org/10.1037/0033-2909.125.6.737?ref=lennartnacke.com)Psychological Bulletin, 125(6), 737–759\. 6. Hinds, P. J. (1999). [*The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance.*](https://doi.org/10.1037/1076-898X.5.2.205?ref=lennartnacke.com) Journal of Experimental Psychology: Applied, 5(2), 205–221\. 7. Newton, E. L. (1990). [*The Rocky Road from Actions to Intentions* (Stanford PhD dissertation).](https://gwern.net/doc/psychology/cognitive-bias/illusion-of-depth/1990-newton.pdf?ref=lennartnacke.com) The "tappers and listeners" study on the illusion of transparency. 8. Lee, H.-P. H., Sarkar, A., et al. (2025).[ *The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects from a Survey of Knowledge Workers.*](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/01/lee%5F2025%5Fai%5Fcritical%5Fthinking%5Fsurvey.pdf?ref=lennartnacke.com) Microsoft Research. 9. Salazar, M. R., et al. (2024). [*Methods for Eliciting and Analyzing Expert Knowledge: A Review.*](https://journals.sagepub.com/doi/10.1177/10944281241271216?ref=lennartnacke.com) Organizational Research Methods. 10. Hoffman, R. R. (1987). [*The problem of extracting the knowledge of experts from the perspective of experimental psychology*](https://dl.acm.org/doi/10.1145/63266.63269?ref=lennartnacke.com)*.* AI Magazine, 8(2), 53–67\. 11. Microsoft (2025). [*Work Trend Index 2025*](https://www.microsoft.com/en-us/worklab/work-trend-index?ref=lennartnacke.com)*.* 12. Klein, G. A., Calderwood, R., & MacGregor, D. (1989). [*Critical Decision Method for eliciting knowledge.*](https://doi.org/10.1109/21.31053?ref=lennartnacke.com) IEEE Transactions on Systems, Man, and Cybernetics, 19(3), 462–472\. 13. Forte, T. [*Progressive Summarization: A Practical Technique for Designing Discoverable Notes.*](https://fortelabs.com/blog/progressive-summarization-a-practical-technique-for-designing-discoverable-notes/?ref=lennartnacke.com) Forte Labs. 14. Stanford Institute for Human-Centered AI (2025). [*AI Index Report 2025.*](https://hai.stanford.edu/ai-index/2025-ai-index-report?ref=lennartnacke.com) # Bonus _This post is for paying subscribers only._ ### How Do I Network for Academic Jobs Without Asking Directly? URL: https://lennartnacke.com/dont-ask-for-the-job-network/ Last updated: 2026-04-30T22:11:40.000Z #### TL;DR - Never lead with "Are you hiring?" but build relationships before asking - Research professors deeply: read 2-3 recent papers, identify gaps in their work - Offer value first: share datasets, co-author commentary, volunteer to review drafts - Use consultative selling: Ask Situation, Problem, Implication, and Need-payoff questions - Follow the 14-day pre-conference plan: Research 5 professors, send useful resources, prime conversations #### Quick Answer Successful academic networking requires positioning yourself as a peer contributor, not a job supplicant. Research target professors by reading 2-3 recent papers, identify gaps in their work, and offer value (datasets, co-authorship, reviews) for 2-3 months before mentioning positions. This changes the dynamic from being an unknown applicant to becoming a known collaborator, the category from which most postdocs are hired. Last week at the CHI conference in Barcelona, a postdoc I didn't remember meeting before walked up to me during the coffee break and asked if I was hiring. She briefly introduced herself and went straight to the ask. I'm a professor who runs a research group. I've supervised [20 PhD students and sat on more hiring panels than I can count](https://uwaterloo.ca/stratford-school-of-interaction-design-and-business/people-profiles/lennart-nacke-phd?ref=lennartnacke.com). She'd just finished her PhD. Her cold open, sight unseen, was four words long. *Are you hiring postdocs?* I didn't take it personally. She was running the playbook every job-market candidate gets handed. Be direct and don't wait for permission. The advice is fine. The execution killed her in eight seconds. But hey, this happens to me every conference. PhD students and fresh graduates skip every step that builds trust and lead with the ask. You can smell the desperation across the room. *Desperation is the fastest way to kill a potential collaboration before it starts.* I've been the version of her, too. Twenty years ago at a games workshop in Germany, I cornered a senior game developer whose work I admired and asked him, in roughly the same number of words, whether he was hiring. He smiled politely and never replied to my follow-up email. I deserved that silence. So yeah, I've burned the same opening and I've lost the same opportunities when I was young. I wish somebody would have told me what I'm about to tell you. ## Why Can't I Approach Professors Like Vending Machines? You cannot approach a professor (or hiring manager) like a vending machine that just needs the right coin, because then you've already lost that conversation. A research group (or similarly a start-up) is a small economy of trust, taste, and shared questions. A coin slot is none of those things. Nobody invests in a stranger on the street who walks up and says *invest in me*. The same logic stops a professor from hiring a stranger who walks up and says *hire me*. The mindset behind that opening is scarcity. The job market in academia right now is brutal. A June 2025 *Nature* news analysis laid out the arithmetic plainly. Doctoral graduate numbers have grown steadily for decades and, in some countries, are exploding, while tenure-track openings have not kept up ([Kwon, *Nature*, 2025](https://www.nature.com/articles/d41586-025-01855-w?ref=lennartnacke.com)). On the postdoc side specifically, an April 2024 *Nature* correspondence flagged that the number of biological and biomedical postdocs at US institutions dropped more than 10% between 2020 and 2022, from 21,902 to 19,585, based on NSF's Survey of Graduate Students and Postdoctorates in Science and Engineering ([Maher & Sureda Anfres, *Nature*, 2024](https://www.nature.com/articles/d41586-024-01038-z?ref=lennartnacke.com)). Fewer postdoc slots chasing more PhDs means more applications per person, more rejections per applicant, and a tighter knot in your stomach walking into any hiring conversation. Numbers like those do something to your nervous system. They turn every hallway interaction into a last-shot transaction. They fill you with anxiety. You broadcast that scarcity in your posture and in your opening sentence. The listener feels it before you finish your first clause, and it repels them. *Don't fall into this trap.* The harder you push from a position of scarcity, the more clearly you show that you have nothing else to offer. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## How Do I Research Before Pitching a Professor? I'm also an entrepreneur. I spend a serious chunk of my week thinking about how to create value for clients before I ever ask for a sale. How to build a relationship where the other person walks away feeling like they got more out of the interaction than they put in. That isn't manipulation. That's how relationships work when you respect the people inside them. The principle has a name in sales literature. Mack Hanan, an ex-McKinsey consultant, coined *consultative selling* in his 1970 book of the same name ([Hanan, 1970, AMACOM](https://archive.org/details/consultativesell0003hana); now in its 9th edition). The book flipped the whole discipline. Before Hanan, sales meant pitching product features and closing hard. After Hanan, sales meant diagnosing a customer's business problem and positioning yourself as the path to a better outcome. Hanan's central line still applies. *Vendors ask for an order, which is a cost to the buyer. Consultants offer improved outcomes, which are a gain.* The conversation starts in a different place, and that changes the whole direction. For you on the academic or expert job market, consultative selling breaks down into four concrete moves. 1. *Research the hiring unit deeply.* Read their strategic plan, scan recent hires, note gaps in their teaching roster, and track the grants they are chasing. 2. *Meet potential collaborators* and ask questions about what they are building and where things are stuck. 3. *Map the gap* between their current state and their stated goals, then articulate how your specific expertise closes part of that gap. 4. *Let the pitch emerge* (only after steps 1-3), usually in the form of them inviting you to apply, visit, or submit a proposal. It's paramount not to skip the first three steps because this will just turn you into just another postdoc, who is mass-mailing CVs, wondering why they never hear anything back from anyone. ## What Is SPIN Selling and How Do I Use It for Academic Networking? The empirical backing arrived in 1988\. Neil Rackham, a behavioural psychologist out of the University of Sheffield, spent 12 years with his Huthwaite team studying [35,000 sales calls across 10,000 salespeople in 23 countries](https://www.huthwaiteinternational.com/blog/complete-guide-to-spin-selling?ref=lennartnacke.com), funded in part by Xerox and IBM. He published the findings as [*SPIN Selling*](https://books.google.com/books/about/SPIN%5FSelling.html?id=YAXlEAAAQBAJ&ref=lennartnacke.com) (Rackham, 1988). The acronym covers four question types top performers use in sequence: - **Situation** questions establish context. → *"How is supervision currently split across the faculty in your group?"* - **Problem** questions surface specific difficulties. → *"Which of those supervision loads is starting to falter under the new intake?"* - **Implication** questions walk the buyer through the consequences. → *"If that bottleneck holds for another year, which grants or papers slip through the cracks for you?"* - **Need-payoff** questions articulate the value of solving them. → *"If someone took the HCI methods supervision off your plate, what would that free you to write next?"* The finding hinges on question type. Top performers asked a similar number of questions overall as their average peers. The mix was heavily weighted toward two categories: Implication questions (4x more) and need-payoff questions (10x more). The comfortable fact-gathering Situation questions (org charts, headcount, budget) barely correlated with success and often annoyed the buyer when overused. *Top performers sold by making the other person articulate, out loud, what was wrong and what solving it would be worth.* The same principle applies the second you walk into a conference reception. You aren't a salesperson. Nobody in your PhD program ever told you to think like one. I get that. Neither am I. That doesn't make the principle less true. Translate it into your informational coffee with a chair or hiring manager. You already know the situation from their website, so skip the small talk about enrolment numbers. Ask what is currently breaking down in their graduate supervision load, their curriculum coverage, and their research capacity. Ask what that costs them. Fewer grants. Students they cannot supervise. Papers that do not get written. Ask what a new hire with your profile would make possible over a three-year horizon. By the time they circle back to what you are working on, they have built the case for hiring you inside their own head. *You didn't pitch. They pitched themselves on you. You are solving their problem.* ## How Do I Show Genuine Curiosity About a Professor's Work? Curiosity is powerful. And I mean real, demonstrated, specific curiosity about the work. A polished CV won't move the needle here, and a flattering email moves it even less. Read their recent papers because you actually want to understand where they're heading. Walk into that reception knowing what they're publishing and how their thinking has changed in the last 12 months. **The boss move:** Drop a citation into the conversation to prove you've read them. It gets caught instantly. Brownie points for you. Then talk to them from that place. Skip *I'd love to join your lab*. Try *I've been following your work on X. Your last paper hinted at a connection to Y, and then you stopped. I'm curious why.* Mean it. You'll notice that they talk to you differently all of a sudden. Because now they don't treat you like a stranger that's just opening the hand, hoping to receive something. But now you're acting like a true colleague that has a shared interest in their work, somebody with which they can bond and that they feel like they're having a categorically different interaction with. In this moment a professor does not process you as somebody requesting a job from them. Their brain just sees you as a peer at this point. Even a junior peer is still a peer and a peer is much more likely to get hired. If the conversation goes well, don't make the ask right away either. This is the part most people get wrong. **Offer something first.** Co-author a short commentary, volunteer to review a draft, or share a dataset that complements their current question. Contribute value to the group for two or three months before you ever mention that you're looking for a position. I know how this sounds. I know what you're thinking. *I don't have two months. I’m stressed. My funding ends in June.* I get it. I've been there. The math will still work in your favour. Two months of contributing to a group's work moves you from *unknown applicant* to *known collaborator*. Known collaborators don't apply for postdocs. They get offered them. That's how most postdocs I've hired came in the door. Not one of them led with the ask. They all offered value first. ## How Do I Build Relationships Before Asking for Positions? I told the postdoc in Barcelona most of this. Not all of it. Some of it works better in writing than at a coffee break with a mariachi band in the room. I know it's hard to hear when you're on the market and every conference feels like a networking sprint timed to your funding deadline. The instinct is to fit everything into one transaction. Here's who I am, here's what I need, yes or no. Move on. I understand that instinct. I had it. It cost me, and it's costing you. Overcome it. *The best outcomes in hiring, in collaboration, in any kind of business, usually come from relationships that exist before anyone needs anything*. The ask, when it finally arrives, barely feels like an ask. It feels like the next logical step in a conversation that has been running for months. Put your detective hat on. Profile the five people you most want to work with. Understand their open questions and the assumptions underneath them. Bring something useful to the table. Have a conversation worth having. The position you're trying to reach, whether a postdoc or a funded partnership, isn't the opening move. It's the outcome of doing everything else to establish a relationship and show your value first. ## What Is the 14-Day Pre-Conference Networking Plan? Two weeks is enough if you actually run this. **Days 1 to 2.** Pick five professors whose questions match yours. Big names are optional and often a distraction. Pull their lab pages, their last two grants, and a photo so you recognize them in the hallway. **Days 3 to 7.** Read two recent papers from each. Take real notes. Write one paragraph per paper on what you think they got right, what they didn't push hard enough, and where their argument quietly contradicts itself. Five people, ten papers, five paragraphs worth keeping. That's your week. **Days 8 to 11.** Find one small, useful thing you can send each of them before the conference. A dataset they'd care about, a counter-reference they probably missed, or a short write-up of a related study from your own work. Then wrap it properly, because a two-line email from a stranger gets deleted before the coffee finishes brewing. Nature's 2023 [guide to cold-emailing](https://www.nature.com/articles/d41586-023-00786-8?ref=lennartnacke.com) and every professor who has ever written publicly about their inbox agrees on the same skeleton: 1. Subject line that names the paper. 2. Greeting with their correct title. 3. One sentence introducing who you are and where you sit. 4. 2-3 sentences showing you actually read their work and naming the specific idea you are responding to. 5. The useful thing, with one line about why it connects. 6. A sign-off that does not ask for anything. Here is the email template: Copy Subject: Quick note on your 2025 CHI paper on deceptive design in VR Dear Professor Nacke, I am Venecia Walburton, a final-year PhD student in HCI at ETH Zürich (supervisor: Prof. Holz). I read your 2025 CHI paper on deceptive design in VR and kept circling back to the section where you ruled out nudges because of user intent. I am attaching a short technical note I wrote last year on a nudge setup we prototyped for a related problem. It is a pre-print, but I thought it might be useful if you revisit the nudging question. No reply needed, and I will be at CHI in Barcelona next month if you happen to be there. Best, Venecia That is the whole email. Under 150 words, specific enough that a professor knows in four seconds it is not a mass mailer, and wrapped politely enough that they will actually open the attachment. **Days 12 to 13.** Draft the question you want to ask each person in person. One sentence about something specific from their recent paper. Practice it out loud until it sounds like you, not like a script. **Day 14.** Walk into the conference with five primed conversations. Five people who already know your name and the thing you sent them. The cold approach goes in the bin. The real work happens in the two weeks before you land. Once you are there you are just showing up to conversations that have already started. Relax, enjoy it, and let the positioning do the work. ## How Do I Choose Between Supplicant and Peer Identity? This is about identity. You have to see yourself as someone with options, someone choosing where to spend attention instead of asking for permission. *Abundance shows on your face before you open your mouth.* Ask a professor for a job and you are a supplicant before your sentence ends. Walk up with a real question about their recent paper and they answer you like a colleague. *The opening words decide which one of those you are.* You choose the role with your opening sentence. Every time. The postdoc from Barcelona is not yet in my inbox. I'm waiting for her email. Hopefully, she'll send it. I can't hire her now. But I might in the future. If she doesn't ask for a job in that email, she'll be in the running. She's probably playing the longer game now. That's the move. Build something worth being part of, then let the position find you. Don't ask for the job. #### FAQ ****Why shouldn't I ask "Are you hiring?" at conferences?** Leading with "Are you hiring?" positions you as a supplicant seeking permission rather than a peer offering value. [Research shows hiring managers make snap judgments in the first 8-10 seconds of an interaction.](https://doi.org/10.1111/j.1467-9280.2006.01750.x?ref=lennartnacke.com) Starting with the ask broadcasts scarcity and desperation, which repels potential collaborators before the conversation begins. ****How long should I contribute value before mentioning I'm looking for a position?** Two to three months of contributing value (co-authoring commentary, reviewing drafts, sharing datasets) moves you from unknown applicant to known collaborator. Known collaborators don't apply for postdocs, they get offered them. ****What if my funding ends in two months and I don't have time for a long game?** The 14-day pre-conference plan compresses relationship-building into a realistic timeframe. By researching 5 professors, sending useful resources before the conference, and priming conversations, you're not starting from zero at the coffee break. The relationship begins with your email, not your in-person introduction. Two months of value contribution can run in parallel with other applications. ****What is consultative selling and why does it work for academic networking?** Consultative selling, coined by Mack Hanan in 1970, means diagnosing someone's problem and positioning yourself as the solution rather than pitching product features. In academic networking, this translates to: research the hiring unit deeply, identify gaps in their capacity (supervision loads, grant writing, curriculum coverage), and articulate how your expertise closes those gaps. By the time they ask about your work, they've already built the case for hiring you. ****What are SPIN questions and how do I use them in conversations with professors?** SPIN stands for Situation, Problem, Implication, and Need-payoff questions. Top sales performers ask 4x more Implication questions and 10x more Need-payoff questions than average performers. Example sequence: "How is supervision split in your group?" (Situation) → "Which loads are faltering?" (Problem) → "If that bottleneck holds, which grants slip?" (Implication) → "If someone took HCI supervision off your plate, what would you write next?" (Need-payoff). The professor articulates the value of hiring you without you pitching. ****How do I write a cold email that doesn't get deleted?** Follow the Nature 2023 cold-email skeleton: Subject line naming the specific paper, greeting with correct title, one-sentence self-introduction, 2-3 sentences proving you read their work, the useful thing (dataset, counter-reference, technical note) with one line explaining the connection, and a sign-off that asks for nothing. Keep it under 150 words. Professors can tell in 4 seconds if it's a mass mailer. ****What if the professor I want to work with is a big name and gets hundreds of these emails?** Big names are often a distraction. Focus on professors whose questions genuinely match yours, not just citation counts. If you must target a high-profile professor, make the useful thing you send genuinely unique (not something they could easily find themselves) and hyper-specific to their most recent work. Better yet, connect with their recent PhD graduates or postdocs first, they're easier to reach and can become internal advocates. ****How do I show I've read someone's work without sounding like I'm brown-nosing?** Drop a citation into the conversation that reveals a gap or contradiction in their argument, not just flattery. Example: "I noticed in your 2025 CHI paper you ruled out nudges because of user intent, but your 2024 CSCW paper suggested nudges work when users are aware. I'm curious why you changed direction." This shows depth of reading and invites a real conversation rather than fishing for praise. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. *The Write Insight subscribers with an AI Research Stack premium account this week also get a print-ready PDF worksheet, 3 AI prompts (one to score a cold-email draft against the supplicant-to-peer rubric, one to generate a wrapped useful-thing email, and one to extract open questions across a professor's last five papers), 5 curated resources on cold-emailing, consultative selling, and the real postdoc-market numbers, and a full 14-day pre-conference checklist you can run before your next hiring-season event.* #### Author Bio Lennart Nacke, PhD, is Research Chair at the University of Waterloo and an expert in human-computer interaction (HCI), gamification, and AI strategy for academics and founders. He has supervised 20 PhD students, sat on dozens of hiring panels, and published over 300 peer-reviewed papers with more than 45,000 citations. He helps serious experts build research-grade writing systems that make them known, trusted, and chosen, without the content hamster wheel, hype, or hustle. --- # Bonus _This post is for paying subscribers only._ ### I Started Building My Brand on Labels URL: https://lennartnacke.com/i-started-building-my-brand-on-labels/ Last updated: 2026-04-09T03:04:24.000Z You already know something is wrong. My argument is harsh (it is). But you recognize yourself in it, and that recognition arrived before any argument did. You've felt the gap between the label you're carrying and the depth you haven't yet built. You've posted the carousel. You've used the hook template. You've called yourself something with "Guy" or "Queen" or "Expert" in it, and some part of you knew, even as you hit publish, that the title was doing work that your track record wasn't. I get it. I've been The LinkedIn Guy™. Not by that name (somebody else has claimed that), but by the same mechanism, which is staking an identity to a label before the label had anything real underneath it. Posting to perform the expertise rather than share it. Watching the wrong metrics go up and calling it progress. The rooms I've been in, the people I've worked with, this pattern holds without exception because everyone who built something real went through a version of this first. The trap is the entry condition. This piece is about the gap between those two modes. It's also about why that gap is about to become very expensive. [U.S. creator ad spend is projected to hit $43.9 billion in 2026](https://www.writtenlyhub.com/news/creator-economy-ad-spend-2026-brand-budgets?ref=lennartnacke.com), nearly double what it was in 2024\. That money is going somewhere. The question is whether it's going to people who claimed a label, or to people who built a body of work. The algorithm still rewards the label. But the audience stopped falling for it. Drop the label. Build the work. I'll show you how. ## The label is your cage You didn't set out to build a hollow personal brand. Nobody does. The process is more insidious than that. It happens through a series of individually rational decisions that compound into a structural problem. The pattern repeats. You get some results. Real ones. Maybe a client doubles their LinkedIn pipeline. Maybe a post you wrote gets 40,000 impressions and your phone lights up for three days. You taste what traction feels like and you want more of that sweet dopamine. So you do what every personal branding course, LinkedIn growth playbook, and creator monetization guide tells you to do. You niche the f\*ck down, name yourself, and claim the territory. "The B2B LinkedIn Strategist." "The Personal Brand Guy." "The Algorithm Queen." It feels like positioning. It looks like clarity. And for a while (maybe a long while) it even works. And then the label becomes a cage. The moment you stake your identity to a title, your content strategy starts serving the title instead of your audience. You stop teaching what you know and start performing what the label promises. Every post becomes an effort to justify the name rather than share the insight. The carousel stops being a teaching tool and becomes a credential performance. The framework stops being compressed experience and starts being a brand asset. You've confused the map for the territory. In philosophy, this is called the map-territory distinction. [Alfred Korzybski's](https://books.google.com/books/about/Science%5Fand%5FSanity.html?id=KN5gvaDwrGcC&ref=lennartnacke.com) formulation: "the map is not the territory." The word "expert" is not expertise. The label "LinkedIn Guy" is not authority. The bio is not the body of work. But your brain doesn't naturally make this distinction. [Daniel Kahneman's](https://books.google.com/books/about/Thinking%5FFast%5Fand%5FSlow.html?id=ZuKTvERuPG8C&ref=lennartnacke.com) work on System 1 and System 2 thinking tells us that fast, associative thinking treats the label and the thing as interchangeable. You see "The LinkedIn Expert" in a bio and your System 1 brain creates an association before your System 2 brain asks for evidence. The creator industry runs on this mechanism. The label triggers the association. The association triggers the follow. The follow becomes the audience. Until it doesn't. ## Get The Write Insight Turn deep expertise into visible authority. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. [38.7% of creators today have just 1-3 years of experience](https://theinfluencermarketingfactory.com/wp-content/uploads/2026/02/Creator-Economy-Report-2026.pdf?ref=lennartnacke.com). Not 1-3 years of expert-level practice in their stated domain. Just 1–3 years of creating content *about* that domain. The map has been sold as the territory at scale. And the market, slowly and then very quickly, is correcting for it. The identity trap isn't about dishonesty. Most people caught in it aren't lying. They believe their label. That belief makes it a trap rather than a con. You build your identity around the title. The title shapes what you create. What you create shapes what you know. And eventually, what you know stops growing because the label already told you who you are. You stopped learning. The label told you who you are. You'd already arrived. That's the trap. And it's invisible from the inside. It's the Wizard of Oz problem. The wizard had real authority, in the sense that everyone believed in it, acted on it, and built an entire city around it. The curtain wasn't hiding nothing; it was hiding the gap between the performance and the substance. The system worked fine, right up until someone pulled the curtain back. In the creator economy, the curtain-pullers aren't journalists or rivals. They're the sophisticated buyers who've already hired three LinkedIn Guys and gotten burned. They walk up to the curtain. They pull. ## Applause from the wrong room The behaviour everyone's calling out deserves a reframe, because "fake it till you make it" is too simple an explanation and it lets people off the wrong hook. The person posting carousel after carousel of recycled LinkedIn tips isn't doing it because they're lazy or fraudulent. They're doing it because it's working. In the metrics that are visible to them (follower count, likes, comment volume, DMs from people saying "this changed my week") the strategy is performing. The feedback loop is real. The dopamine hit is genuine. The problem sits in which signal they've optimised for. [B.F. Skinner's](https://books.google.com/books/about/Science%5FAnd%5FHuman%5FBehavior.html?id=QcbJInkd%5FiMC&ref=lennartnacke.com) operant conditioning research tells us that behaviour is shaped by reinforcement schedules. Variable reinforcement (rewards that arrive inconsistently) produces the most persistent behaviour patterns. It's why slot machines are more addictive than vending machines. You never know which post will go viral. So you keep posting. And you pattern-match on the ones that do: "The listicle hit. Post more listicles." "The hook template worked. Use more hook templates." "People liked when I called myself The Algorithm Expert. Double down on that." The whole pattern is a calibration problem. You're getting real feedback from the wrong cohort. The people engaging with the recycled-tips carousel are largely the same 1–3 year creators who just discovered that carousel engagement matters. They're nodding along to content that confirms what they already half-know. They're not the buyers. They're not the decision-makers. They're not the ones who will write the $15,000 retainer cheque or recommend you to their CMO. They're fellow travellers on the same road, and their applause feels like proof of arrival. It isn't. Think of it like The Truman Show. Truman had a huge audience, universal name recognition, and people emotionally invested in his story. What he didn't have was an audience that could do anything for him outside the set. The applause was real. The world producing it wasn't. That's the creator economy's version of going viral with an audience of fellow aspiring creators: real engagement, constructed reality, zero pipeline. The people who could change your business trajectory (the sophisticated buyers, the senior operators, the founders who've already tried three "LinkedIn Guys" and gotten burned) don't engage with the carousel. They scroll past it in less than a second, note the gap between the promise and the depth, and move on. Their silence registers as a zero in your analytics. But it isn't a zero. It's a disqualification. Psychologist [Albert Bandura's](https://doi.org/10.1037/0033-295X.84.2.191?ref=lennartnacke.com) concept of self-efficacy is relevant here in a painful way. High self-efficacy (the belief that you can accomplish something) is usually built through mastery experiences: actually doing the hard thing successfully, repeatedly, until competence becomes confidence. But there's a shortcut to high self-efficacy: social persuasion. Other people telling you you're good at something. Followers saying you're the expert. Comments affirming your label. The creator economy has industrialized this shortcut. You can build a high-self-efficacy identity around a label without the mastery experiences that should underpin it. And then you start creating from that identity: teaching frameworks you haven't battle-tested, citing outcomes you can't quantify, claiming a depth you're still building toward. A shortcut to belonging. The goal is belonging. That impulse is human. And there's research that complicates this in an important way. [Daryl Bem's](https://doi.org/10.1016/S0065-2601%2808%2960024-6?ref=lennartnacke.com) self-perception theory shows that acting as if you hold a belief can produce that belief. You behave confidently, observe yourself behaving confidently, and conclude you must be confident. The mechanism is well-replicated. [Hajo Adam and Adam Galinsky's](https://doi.org/10.1016/j.jesp.2012.02.008?ref=lennartnacke.com) enclothed cognition research found that wearing a lab coat labelled "doctor's" measurably improved cognitive performance on attention tasks. The same coat labelled "painter's" did nothing. The label changed the cognition. Beyoncé created Sasha Fierce to access a version of herself she couldn't reach under her own name. David Bowie had Ziggy Stardust. Adele reportedly used a stage persona to manage crippling performance anxiety. Todd Herman built [an entire coaching methodology](https://alteregoeffect.com/?ref=lennartnacke.com) around this principle after two decades with elite athletes: adopt an alter ego, activate under pressure, perform beyond your default setting. The "fake it till you make it" dynamic is real. But the crucial distinction is that Sasha Fierce worked because Beyoncé had fifteen years of vocal training, stage discipline, and professional reps underneath that persona. The alter ego unlocked capacity that already existed. It didn't manufacture capacity that was missing. The lab coat improved attention in people who already had the cognitive ability. It didn't teach them medicine. The creator economy version skips this step entirely. The label substitutes for expertise that hasn't been built yet. A lab coat with no one wearing it. The market grades on depth. ## The machine rewards the costume This isn't just a personal failure. There's a machine producing it. The creator economy's infrastructure is extraordinarily well-optimized for the rapid packaging and distribution of legible expertise signals. Courses on how to build a course. Cohorts on how to run a cohort. LinkedIn playbooks written by people whose main credential is having written a LinkedIn playbook. You catch my drift. [Only 26% of consumers now prefer AI-generated or generically produced creator content](https://digiday.com/media/after-an-oversaturation-of-ai-generated-content-creators-authenticity-and-messiness-are-in-high-demand/?ref=lennartnacke.com), down from 60% just three years ago. That drop in preference didn't happen because audiences got smarter about AI specifically. It happened because audiences got smarter about *surface*. They've been trained, through years of content consumption, to recognize the difference between something that emerged from genuine knowledge and something that was assembled to look like it did. The systemic problem is that the tools for packaging expertise became cheaper and easier to use long before the underlying expertise did. Canva democratized design. Notion democratized organization. ChatGPT democratized the first draft. Caption templates, content calendars, posting schedules. All of it became accessible to anyone with a subscription and a Saturday afternoon. The barrier to *looking* like an expert dropped to near zero. The barrier to *being* one didn't move at all. None of these tools are the problem. A hook template on a deep post is a door. A hook template on an empty post is a trapdoor. The tool is the same. What's behind it is what matters. What this means in practice is that the feed is now saturated with a particular kind of content. Polished. Hook-forward. Formatted for skimmability. Emotionally confident. And empty underneath. It looks like expertise from 30 feet away and dissolves on contact. LinkedIn's platform has been a specific enabler here. The mechanics that drove growth in 2021-2023 (engagement pods, hook templates, carousel virality, comment-timing tactics) created a generation of creators who optimized for reach before relevance. I was part of the crew. The playbook worked. Then it became the playbook everyone knew. Now, it mostly stopped working. But it became the thing people taught. In 2026, there are people charging to teach a playbook the platform penalizes today. You’d think it’s sweet irony, but it’s just the machine eating itself. Meanwhile, the stakes keep rising. [$43.9 billion in U.S. creator ad spend in 2026](https://www.writtenlyhub.com/news/creator-economy-ad-spend-2026-brand-budgets?ref=lennartnacke.com). Brands pulling budget from legacy channels and redirecting it toward creator partnerships. CMOs making decisions about who gets that money based on the same depth signals the audience is using. Dwell time. Saves. Private shares. That question has one answer. Proof. The machine manufactured the labels. The buyers are now doing due diligence on what's under them. This is the systemic correction. It's already here. ## Get The Write Insight Turn deep expertise into visible authority. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Know your rung Expertise has a structure. And you can locate yourself on it. The exercise is diagnostic. You have to be willing to look. Two models, taken together, explain almost everything about the current creator economy crisis. The first is the [Dreyfus Model of Skill Acquisition](https://books.google.com/books/about/Mind%5FOver%5FMachine.html?id=e9W9m%5F4q4pYC&ref=lennartnacke.com), developed by philosophers Stuart and Hubert Dreyfus based on their research into chess players and airline pilots. The model identifies five stages of skill development: Novice, Advanced Beginner, Competent, Proficient, and Expert. The critical insight is what changes between them. Novices operate from rules. "Post at 8am on Tuesdays." "Use three-line hooks." "End with a question." Rules are context-free. They're the only tool available when you don't yet have enough experience to see the pattern beneath the rule. Competent practitioners start to see cases. They recognize that 8am on Tuesday works for some audiences and not others, and they can reason about why. They're still effortful, still consciously applying judgment, but judgment is now possible. Experts don't follow rules and don't reason through cases. They perceive. The situation presents itself and the response is available. Not because they computed it, but because they've internalized enough pattern recognition that the right move is obvious to them. Novices belong in every field. Of course they do. The fraud is that packaging tools allow novices to perform expert-level confidence before they've earned expert-level perception. They've memorized the rules, assembled the Canva slides, prompted ChatGPT, and adopted the tone. From the outside, it's hard to tell. From the inside, they know. The rules run out in the edge cases. And edge cases are exactly what clients pay good money for. The second model is [David Kolb's Experiential Learning Cycle](https://books.google.com/books/about/Experiential%5FLearning.html?id=ufnuAAAAMAAJ&ref=lennartnacke.com): Concrete experience → reflective observation → abstract conceptualization → active experimentation. This is how knowledge compounds. You do something. You notice what happened. You build a mental model. You test it. You do something again. The shortcut the creator economy offers is to skip the first step. Jump straight to abstract conceptualization (just prompt AI for the framework, the model, the process) without the concrete experience that should produce it. The result is frameworks that are logically coherent but experientially hollow. They have the shape of insight without the substance of it. You can tell the difference immediately when you try to apply them to an edge case. Real frameworks flex. They account for the situation that doesn't fit the template. Hollow frameworks break. One question separates the rungs. A client brings you a problem outside your framework. What do you do? Think about the Night's Watch. Centuries of institutional knowledge about how to guard that Wall. Detailed frameworks for repelling wildling raids. Enormous expertise in that specific problem. Then the White Walkers show up, a threat the framework was never built to handle, and the Watch responds by... applying the framework anyway. Hollow expertise is the same thing. It works perfectly on every client whose situation matches the cases that produced it. The moment the terrain is different, you're standing on the Wall with a sword, confidently explaining that the procedure is to wait for the raiders to climb, while something categorically different walks through the ice and stabs you in the face. Applying the framework anyway and hoping puts you at Advanced Beginner. Revising it on the fly moves you toward Competent. Seeing what's different and adjusting without invoking any framework at all puts you near Expert. Most people teaching LinkedIn strategy in 2026 would struggle with the first answer. That's the Expertise Ladder. Most personal brands are built at rung two or three and named as if they live at rung five. The gap between those rungs is not a matter of effort or intention. It's a matter of repetition, reflection, and accumulated failure. It cannot be skipped. It can only be walked. ## The algorithm never flatters The second framework explains why platform changes amount to a reckoning for shallow creators. A correction that was always coming. Let's call it the Authority Stack. Authority in any domain rests on three layers, stacked in order of difficulty: **Layer 1: Signal Authority.** The label. The follower count. The posting frequency. The visual brand. This is the easiest layer to build and the first thing people see. It creates the initial impression of credibility. But it has no structural integrity on its own. Signal authority collapses the moment someone asks you a hard question and you have no AI handy. **Layer 2: Outcome Authority.** Demonstrated results with specific clients, in specific contexts, over measurable time horizons. Not "helping 50+ clients." That's still Signal Authority, just with a bigger number attached. Real Outcome Authority looks like: "Helped a seed-stage fintech reposition from feature-led to problem-led content, cutting their enterprise sales cycle from 8 months to 5 months across 11 accounts over two quarters." That sentence has a client type, a methodology, a metric, a time horizon, and a scope. Every element is falsifiable. That's the point. **Layer 3: Framework Authority.** The models, mental maps, and pattern-recognition systems that only emerge from enough repetitions to see the structure beneath the cases. These frameworks are the things that clients buy. The lens. The way of seeing a problem that only this person has earned the right to offer. Framework Authority is non-transferable. You can't download it from a course. You can't assemble it from LinkedIn carousels. It accumulates slowly and compounds fast. Most personal brands in the creator economy operate at Layer 1, occasionally reference Layer 2, and rarely produce genuine Layer 3. The algorithm measures this gap. The LinkedIn depth score, [built around dwell time, saves, comment depth, and private shares](https://www.digitalapplied.com/blog/linkedin-algorithm-2026-engagement-strategy-guide?ref=lennartnacke.com), is a proxy for Framework Authority. It can't read your credentials. It can't evaluate your client outcomes. But it can measure whether people stop scrolling, finish reading, save the post, and send it privately to someone else. Those behaviours (save, dwell, private share) are what humans do when they encounter something they haven't seen before. Something that reorganizes their thinking. Something with Framework Authority. Layer 1 content gets the three-second scroll. It might get a like, an automated social gesture. It doesn't get saved. Nobody private-messages a carousel of recycled tips to their COO at 11pm. They do send frameworks. Insights. Things that made them think differently about a problem they're sitting inside. [Saves beat likes 4:1 in driving impressions](https://www.digitalapplied.com/blog/linkedin-algorithm-2026-engagement-strategy-guide?ref=lennartnacke.com). [Twenty thoughtful comments outperform 200 likes](https://www.reddit.com/r/AskMarketing/comments/1qod0lc/the%5F4%5Fkey%5Fpoints%5Fof%5Fthe%5Flinkedin%5Falgorithm%5Fin%5F2026/?ref=lennartnacke.com). The platform's 360Brew AI, [the 150-billion-parameter model now powering distribution decisions](https://yepads.com/linkedin-algorithm-changes-2026-why-linkedin-reach-is-dropping/?ref=lennartnacke.com), deprioritizes AI-generated and templated content. It rewards interest-graph resonance, which are posts that hold attention from people who have demonstrated deep interest in a specific domain. The algorithm is a mirror. It reflects the depth (or absence of depth) in your content back at you in the form of reach. [The AI content predictions from multiple industry reports](https://techcrunch.com/2026/02/22/can-the-creator-economy-stay-afloat-in-a-flood-of-ai-slop/?ref=lennartnacke.com) are no longer predictions: AI won't replace creators. It will punish the generic ones. It's already happening. The correction is asymmetric. Signal Authority accounts are watching general impressions crumble. Framework Authority accounts (the subject-matter experts who've spent years earning the perception they describe) are watching saves and shares climb. The platform is routing engagement toward depth at the exact moment the creator economy is most saturated with surface. Terrifying as hell for some LinkedIn Guys™. The Authority Stack is not a formula. It's a distinction layer. You shouldn’t ask which layer you are performing but which layer you can stand in for good. Everything else follows from the honest answer to that question. ## Get The Write Insight Turn deep expertise into visible authority. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Stop performing. Start building. I've directly supervised 103 people building expertise into output: PhD candidates, researchers, practitioners. I've coached 26+ professionals on how to translate deep knowledge into career traction. Across 200+ publications, the pattern holds without exception: Depth compounds. Surface decays. I'm still (un)learning what that looks like at scale, too. That pattern becomes a system you can run starting today. ### Step 1: Proof first. Post second. Open a blank document. Write five client engagements across the top as column headers. For each one, fill in four rows: what the client's situation was before you arrived (industry, stage, specific problem), what you did (the specific intervention, not the service category), what changed (a measurable outcome with a number attached), and how long it took. That table is your Layer 2 inventory. It's the raw material for everything that follows. If you can't fill all four rows for at least three columns, stop posting and go do some more client work. Content without a full table underneath it is performance. You're generating volume without building the inventory that makes volume meaningful. If your table has gaps in the outcome row specifically (you know what you did but not what it produced) go back to those clients right now and ask. Most will tell you. A surprising number will thank you for asking. And those conversations often generate the most specific, credible content you'll ever write. The table is the foundation. Build it before you write another word. ### Step 2: Find the polka dots in your cases. Look at your five columns. Ask three questions. First: What do the successful outcomes have in common that wasn't obvious to me until case three or four? Not what you knew going in, but what you only understood after enough repetitions that the structure became visible. Second: What question do you ask now at the start of an engagement that you never would have thought to ask two years ago? That question is usually the core of your framework. It's the thing you see that your client can't see yet. Third: What do clients always get wrong before they work with you? Not the thing they think they're getting wrong, but the real underlying problem that your methodology addresses. Write one paragraph that answers all three. That paragraph is your framework in its first draft. A real framework passes one test: You can take a new client situation you've never seen before, apply the framework, and make a confident prediction about what will happen. If you can't do that, you have a process. Processes are valuable. Frameworks are what compound your brand. Don't name it yet. Name it last. Most hollow brands got their name before they got their substance. ### Step 3: Write to be saved. Not liked. Use this structure verbatim. **Opening line:** Your client's specific situation. "A B2B SaaS founder came to me with 14,000 LinkedIn followers and zero inbound pipeline in eight months of posting." One sentence. Concrete. Names the gap between effort and result. **The diagnosis:** What you saw that they couldn't see. "The content was optimized for reach metrics. Every post was built to be liked. None of it was built to be trusted. Reach and trust are not the same currency, and in B2B sales, trust is the only one that converts." **The intervention:** What you changed, specifically. "We rebuilt the content calendar around three question types: the problem the buyer is embarrassed to admit they have, the assumption the buyer holds that costs them money, and the outcome the buyer wants but doesn't know how to describe. Every post addressed one of those three. Nothing else got published." **The outcome:** What happened, with a number and a time horizon. "In eleven weeks: three inbound discovery calls from enterprise accounts, two proposals, one closed deal at $42,000\. Zero change in follower count. The audience got smaller and more valuable simultaneously." **The framework:** What the pattern means beyond this case. "LinkedIn content fails in B2B because it optimizes for social approval from a cohort that can't write cheques. Switch the content to address the private anxieties of decision-makers and reach becomes irrelevant. Inbound replaces outreach. The algorithm rewards the switch because saves and private shares climb, and those are the metrics that drive distribution now." That structure (problem, diagnosis, intervention, outcome, framework) is the Authority Stack expressed as a content format. It demonstrates Layer 2 before claiming Layer 3\. It gives the reader something they can use immediately. And it gives the algorithm exactly the depth it's looking for. Use your real case. Not an anonymized composite. Not a hypothetical. Your client, your numbers (with permission if needed), your framework. That's the post that gets saved. That's the post that gets forwarded to a COO at 10pm. That's the post that generates a DM from someone who already wants to hire you before they've spoken to you. One more thing before you write that first post: Build a swipe file. Go into your niche and find the highest-performing posts from the people you compete with, or aspire to compete with. Go beyond text. Look at the images they're using, how they structure their opening lines, which topics and themes recur across their best work, where they place the insight versus where they place the proof. Screenshot or save the ones that made you stop scrolling. That collection is your swipe file. Then reverse-engineer them. Dan Koe shared an AI prompt that works better than anything else I've seen for this. Run each saved post through it: Copy Break down the overall structure and topic, what psychological tactics it uses, why it works (structure and topic), then break down each line individually. Write this as if you are teaching me how to do it step by step. That prompt forces the analysis down to the line level, not just "this post worked because it had a strong hook" but exactly which words created the tension, where the proof landed, how the conclusion earned its punchline. Do this enough times and the structural patterns become visible. You're not copying. You're building a mental library of what actually works, in your niche, with your audience. That library is part of what separates the people writing from pattern recognition from the people writing from guesswork. ## Get The Write Insight Turn deep expertise into visible authority. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ### Step 4: Depth earns reach. Volume compounds it. [Dwell time is the primary ranking signal in 2026](https://www.digitalapplied.com/blog/linkedin-algorithm-2026-engagement-strategy-guide?ref=lennartnacke.com). The algorithm measures how long someone's cursor sits on your content before moving on. A listicle doesn't earn dwell time. It earns a three-second glance and a scroll. A framework that reorganizes how a reader understands their problem earns the read, the re-read, the save, and the share. Practically, this means: Post 3–5 times per week and make each post earn its place. An [analysis of 250,000+ LinkedIn posts by Ordinal](https://www.tryordinal.com/blog/linkedin-posting-frequency?ref=lennartnacke.com) found this range delivers the best balance of reach and engagement. Daily posting compounds total visibility. Going quiet suppresses distribution because the algorithm reads inactivity as a signal to stop amplifying your account. Frequency still matters. What's changed is the bar each post has to clear. A post without Framework Authority isn't a depth post. It's just volume. Cut every external link from the post body. If you need to direct people somewhere, put the link in the first comment. [External links in the post body cut reach by roughly 60%](https://www.digitalapplied.com/blog/linkedin-algorithm-2026-engagement-strategy-guide?ref=lennartnacke.com). I’ve experienced this myself. Every LinkedIn growth expert still teaching to always include a CTA link is demonstrating, in that specific recommendation, that they haven't updated their knowledge since 2022. End the post with a question that only someone with your specific problem would answer. Not "What do you think?" That's a vanity engagement prompt. "Has your pipeline attribution changed since you shifted your content strategy?" That question filters for your actual buyers. Twenty replies from qualified prospects outperform 200 generic comments by every measure that matters, including the algorithm's. Reply to every comment that contains a real question within the first hour. Depth of conversation drives the comment-depth metric. A thread with five substantive exchanges compresses more authority into less space than any carousel you'll ever build. ### Step 5: Vague repels. Specific converts. Before you publish anything (a post, a bio line, a case study reference) ask one question: Could someone prove this wrong with specific evidence? Run these through the test right now: **"Helped 50+ clients."** Not falsifiable. Nobody can confirm or deny it. It communicates volume, not impact. Cut it or replace it. **"I help founders build authority on LinkedIn."** Not falsifiable. What kind of authority? Measurable how? In what time frame? This sentence could describe anyone. That's why it impresses no one. **"Helped a seed-stage SaaS founder generate three enterprise inbound leads in 11 weeks by repositioning content from product features to buyer anxieties."** Falsifiable. Stage, outcome type, quantity, time horizon, methodology. Every element is checkable. That's what makes it credible. The Falsifiability Test rewards specificity. Write claims specific enough that a sophisticated buyer (the kind who's been burned by vague promises) reads yours and thinks: this person has done this. Vague claims repel sophisticated buyers. Specific claims attract them. Apply it to your bio first. Then your case studies. Then every post. The cumulative effect compounds faster than any posting frequency strategy you'll ever find in a carousel. That's the system. Five steps. A table, a pattern, a post structure, a posting discipline, and a test. Nothing in here requires a course, a coach, or a content calendar with colour coding. (Although, I’d always love to work with you if you need extra help.) What it requires is honesty about where you are on the Expertise Ladder, and the willingness to build from that rung rather than perform from a higher one. ## You're winning the wrong game Most creators are playing the right game on the wrong difficulty setting. I spent an embarrassing number of hours on Super Ghouls 'n Ghosts as a kid. Brutal game. You'd grind through every level, get destroyed repeatedly, finally claw your way to the end, defeat the final boss… and then the game would tell you the ending was fake. A trick. You had to play the entire thing again, on a harder difficulty, to get the real one. All that progress. All that effort. And you were still only halfway there. Most LinkedIn content strategy works exactly like that first run. High-frequency posting, engagement mechanics, hook templates, carousel formats optimized for surface metrics. Easy Mode gives you fast feedback. Fast follower growth. Fast validation. The numbers go up and you feel like you're winning. But Easy Mode has a hidden mechanic: The final boss can't be defeated on Easy Mode. No matter how many levels you clear, the hardest challenge (converting genuine buyers, building real authority, compounding toward the kind of career that doesn't require you to post every day just to stay visible) is locked behind a difficulty setting you haven't unlocked. Hard Mode is slower. The feedback loops are longer. A post built on real Framework Authority might underperform a carousel hook in the first 24 hours and then get saved 400 times over the next three weeks as it circulates in private messages and Slack threads. The analytics dashboard doesn't always show you that second number. You have to trust that the work is landing somewhere important even when the vanity metrics are quiet. Hard Mode requires you to have actually played the game. To have failed projects behind you alongside the successful ones. To have built the mental models that only emerge from enough repetitions that the structure becomes obvious. To have, in short, the kind of experience that can't be packaged into a weekend course or a Notion template or a LinkedIn or X bio that says "The LinkedIn Guy." The good news is this though. Once you're playing on Hard Mode, the algorithm becomes your ally instead of your challenger. The algorithm is [built around dwell time, saves, comment depth, and private shares](https://www.digitalapplied.com/blog/linkedin-algorithm-2026-engagement-strategy-guide?ref=lennartnacke.com), governed by a 150-billion-parameter model designed to route content toward genuine domain interest. It's also playing on Hard Mode. It's looking for the same thing the sophisticated buyer is looking for. The same thing that person sends to their COO after work. The same thing that makes someone stop mid-scroll and think: Damn, this person knows something I don't. LinkedIn's 360Brew AI [detects and deprioritizes generic and AI-generated content](https://yepads.com/linkedin-algorithm-changes-2026-why-linkedin-reach-is-dropping/?ref=lennartnacke.com). It can't read your CV. But it can measure whether people think your content is worth their full attention. And full attention (sustained, deliberate, referral-generating attention) is what Framework Authority produces. You're not going to build that by calling yourself "The LinkedIn Guy." You're going to build it by doing the work long enough that you see patterns nobody else has articulated yet. By publishing those patterns in language plain enough to be immediately useful. By attaching your name to outcomes specific enough to be checked. A real identity you build. And it compounds. The algorithm doesn't care what you call yourself. It measures whether people stop scrolling, read the whole thing, and save it for later. That's expertise. Expertise, it turns out, compounds the same way everything else worth building does, slowly, then all at once. Build depth early and you pull away. The window to catch up is still open. Because depth, once built, is hard to replicate. That's your moat. Build it. ## Get The Write Insight Turn deep expertise into visible authority. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. # Bonus [The Write Insight Premium subscribers](https://lennartnacke.com/#/portal/signup) get the complete 5-step system with every tool built to run this week. One print-ready PDF worksheet (a 5-step implementation guide with fill-in fields). Two more AI prompts you paste and run. 5 curated resources. The full protocol checklist. **Less than a coffee for the complete weekly system.** _This post is for paying subscribers only._ ### Your Hard Work Is Your Biggest Career Blocker URL: https://lennartnacke.com/your-hard-work-is-your-biggest-career-blocker/ Last updated: 2026-03-31T21:44:50.000Z A professor I coach carries a teaching load that would break most people. Full course schedule. Administrative duties across two departments. Programme development for nursing and dentistry on top of that. No additional support. No additional compensation. She publishes. She writes grants. She shows up at 7 AM and leaves after dark. She is struggling to break through. Her grant applications get rejected because she lacks the collaborative network to strengthen them. Her publications land in mid-tier journals because she doesn't have co-authors who open doors to Q1 venues. Her institution loads her with service work she can't refuse because nobody taught her to protect her research time. She works harder than almost anyone I know. The system barely notices it. (I say this as someone who spent a decade believing that one more grant application would fix everything. It didn't fix anything except my caffeine tolerance.) OECD researchers surveyed adults across 27 developed nations (in the [​Risks That Matter Survey, 2022​](https://www.oecd.org/en/publications/main-findings-from-the-2022-oecd-risks-that-matter-survey%5F70aea928-en.html?ref=lennartnacke.com)). They asked what drives success. Only 11% still believe effort alone is the answer. Eighty-nine percent figured out what most career advice hasn't. Effort is just your entry fee these days. It does not set you apart anymore. Here's what the data and four of my client stories show. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. # Your position reflects your starting line A clinical psychologist I coached immigrated to Canada with a complete set of credentials. Doctoral degree. Years of clinical practice. Published research. She applied for a tenure-track position at a Canadian university. Her qualifications were not the problem. Her immigration status was. The registration process was overwhelming. Canadian employers preferred Canadian-trained candidates. The psychology department she worked in had almost no diversity. Every structural barrier she hit had nothing to do with how hard she worked. Compare this to the intergenerational [​earnings elasticity in the U.S. sitting at 0.6 or higher​](https://www.aeaweb.org/articles?id=10.1257/jep.27.3.79&ref=lennartnacke.com). Children born to high-income parents retain 60% of that financial advantage into adulthood. For immigrants (whether in the U.S. or Canada), the compounding disadvantage runs even steeper. She prepared more thoroughly than any candidate in that hiring pool. The system still treated her as an outsider. The move for any expert hitting structural walls is identical. Stop applying through front doors built for someone else. Build a visible body of work where the people who hire, fund, and refer can find you without a gatekeeper in between. Her credentials didn't change. Her visibility did. # When everyone is qualified, access picks the winner Frank and Cook documented this in [​*The Winner-Take-All Society*​](https://www.penguinrandomhouse.com/books/329317/the-winner-take-all-society-by-robert-frank/?ref=lennartnacke.com) (1995). In competitions where 98% of results are skill-based, small random factors still swing outcomes. Cognitive ability [​explains only 15% of career success​](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1744-6570.1999.tb00174.x?ref=lennartnacke.com). Fifteen percent. That's it. Every career advice book you've ever read just lost 85% of its core argument there. One of my clients spent 25 years building practitioner expertise. Named to a global thought-leadership list. Clients at one of the top research universities in the world. Pursuing a doctorate in her fifties because the rigour mattered to her. She had the depth. She had the frameworks. She had the track record. None of that mattered until she started positioning herself near the people who control access to academic publications, fellowship appointments, and research centres. Yes, she is still working hard on her dissertation. But she has also activated the network she'd spent decades building before. Win, win. Reaching out to directors of labs she admired. Showing up at the conferences where hiring and collaboration decisions start as hallway conversations. It's pretty much the only reason I'll be travelling to the CHI conference in Barcelona this year. To keep a seat at the table. Skill got her into the room. Positioning picked her out of it. Her scientific understand and her business skills work so well in tandem that I believe she is already becoming a thought leader. And I'm proud of her. You have the same depth sitting in your head right now. The 5 people who control your next contract, your next board seat, your next keynote invite probably don't know you exist. Fix that before you fix your output. # The system is the variable A PhD I coached spent months grinding on a literature review her supervisor had assigned. The supervisor had generated an abstract with fabricated results using AI and submitted it to a conference without telling her. I couldn't believe this when I first heard it. Her university lost access to its primary research database. She had no tools to do the work properly. Twelve-hour days. On a project built on a fraudulent foundation. Using tools she couldn't access. For a supervisor who wouldn't even return her emails. The hardest-working person in that department. Not being able to produce anything publishable. The fix was not more effort. The fix was changing the system entirely. After my advice, she found a new supervisor at another university who valued quality over quantity. She dropped the fabricated project. She refocused on her own research. She is on a trajectory for a career and success. Same person. Same work ethic. Different system. Completely different path. If your environment isn't supporting you, you have to change your environment. My goal is to create an environment for the people I work with that nurtures their deepest ambition and to find tools to turn the ambition into income. If you're grinding inside a container that structurally caps your ceiling, a bad client, a broken partnership, a market that rewards noise over depth, more hours inside that container won't expand it. Change the container first. Then bring your work ethic. # Three thoughts that change your outcomes **Inspect your network like a balance sheet.** Count the people in it who control access to opportunities you actually want. The bottleneck for many people is that this number is too low. So it's not the skills and it's not the hours that you put in. It's simply that you don't have access to the right people. **Invest in compounding assets.** Skills that scale. Reputation that precedes you. AI systems that multiply your output without multiplying your time. $10K in strategic visibility over 12 months versus $10K in overtime coffee and late nights. It's an easy choice from the outset, but it's not a choice that many people see when they are bogged down with work deep in the trenches. One choice compounds. One choice depletes. Your Nespresso pod budget is not a growth strategy, my friend. **Change the system before you optimize within it.** If the institution, the supervisor, the market, or the network is structurally capping the ceiling of your ambition, no amount of grinding inside that container will expand it. Identify the constraint. Remove it or route around it. That professor I mentioned at the top? She's working deeply on building the right collaborations. The clinical psychologist landed the interview and walked in prepared. The PhD student is producing genuine work now with a supervisor who respects the process. The practitioner-turned-scholar is positioning themselves for a collaboration with one of the best research institutions in the world. None of them worked less. All of them redirected where the work went. The system rewards preparation meeting opportunity. Not preparation alone. # Worth your time this week - Harvard Business School, “[​Hidden Workers: Untapped Talent​](https://www.hbs.edu/managing-the-future-of-work/Documents/research/hiddenworkers09032021.pdf?ref=lennartnacke.com)” By Joseph Fuller et al. (2021). Research showing that automated hiring systems used by 98% of Fortune 500 companies systematically screen out qualified candidates who have resume gaps, non-traditional credentials, or lack specific keywords. Documents how 27 million Americans are hidden from employers not because they lack skill, but because the filtering infrastructure excludes them. - Mark Granovetter, "[​The Strength of Weak Ties​](https://www.journals.uchicago.edu/doi/pdf/10.1086/225469?ref=lennartnacke.com)" (1973) The single most cited paper on why your acquaintances matter more than your close friends for career advancement. Granovetter showed that weak ties bridge separate social clusters and expose you to non-redundant information, including job leads your inner circle never sees. - Daniel Markovits, "[​How Life Became an Endless, Terrible Competition​](https://www.theatlantic.com/magazine/archive/2019/09/meritocracys-miserable-winners/594760/?ref=lennartnacke.com)" (The Atlantic, 2019). Adapted from his book *The Meritocracy Trap*, this article argues that meritocracy harms winners too, trapping elite professionals in an exhausting cycle of overwork to maintain their position. It reframes the grind not as a path to success but as a symptom of a broken system that extracts maximum labour from everyone. # Bonus The Write Insight Premium subscribers with an AI Research Stack account this week also get 3 print-ready PDF worksheets, 3 AI prompts (audit your professional network for career access gaps, build a 90-day strategic positioning plan, and map the structural factors controlling your career trajectory), curated resources on intergenerational mobility, structural career barriers, and networking research, and a full career advantage protocol checklist. [The Expert-to-Founder Authority Transition KitGrab the free kitThe Expert-to-Founder Transition Kit.pdf3 MBdownload-circle](https://lennartnacke.com/content/files/2026/03/The-Expert-to-Founder-Transition-Kit.pdf "Download") _This post is for paying subscribers only._ ### Your Proof Rots Without a Stage URL: https://lennartnacke.com/your-proof-rots-without-a-stage/ Last updated: 2026-03-20T04:57:42.000Z A colleague asked me for coffee last fall. She runs a niche consulting practice. Fifteen years of domain expertise. $400K in annual revenue. She’d lost 3 contracts in 6 months to people she'd trained. “They don’t have my depth,” she said. “But they have an audience.” I knew that feeling deep in my gut. I’d spent a decade building one of the strongest research track records in my field. $3+ million in competitive funding. Top 2% worldwide in citations. More than 150 publications in top venues. Outside my little subfield though, nobody knew my name. A guy who'd read two of my papers and started a podcast about the topic had more reach than I did. She asked me what changed recently with my LinkedIn posting efforts. I told her that I stopped treating visibility as something that would come from the work but just treated it as the actual work. You have a visibility problem. Not a skill problem. Those are two different systems with two different inputs. Conflating them is the reason you've been spinning your wheels. ## The meritocracy trap Society handed you a story early. Work hard. Produce excellent results. The right people will notice. That story rots your career. Not because effort doesn't matter. It often does. But effort alone has never been the mechanism through which careers compound. Recognition doesn't trickle down from quality. Recognition is a separate system. It has its own inputs, its own logic, its own maintenance requirements. The 2023 OECD report *Hard Work, Privilege or Luck?* surveyed adults across 27 countries and 60% said hard work is essential to getting ahead. But the vast majority of that same group said hard work *alone* is insufficient. Timing, circumstances, and structural factors carry equal or greater weight. You can [read the full report here](https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/11/hard-work-privilege-or-luck-exploring-people-s-views-of-what-matters-most-to-get-ahead-in-life%5F708e201f/e1903ab2-en.pdf?ref=lennartnacke.com). That's the world most skilled people live in. That’s the world we live in today. Not the one we were promised. The stakes are not abstract. The [2026 Engagement and Retention Report](https://www.achievers.com/resources/white-papers/2026-engagement-retention-awi-report/?ref=lennartnacke.com) from the Achievers Workforce Institute studied 4,000 employees and HR professionals across 8 countries. Only 1 in 4 employees feels appreciated. But 26% report being engaged. Fewer than half plan to stay with their current employer. Hidden performance. Broken recognition systems. Those numbers expose our hidden performance. People delivering real value inside systems that have no mechanism to surface it. Recognition needs plumbing. You solve a morale problem with deeper encouragement. You solve an infrastructure problem with better architecture. The architecture you're missing makes your expertise palatable to the world outside your immediate orbit. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Why exceptional people stay hidden The harder you work, the easier it is to disappear. Counterintuitive. Also true. You are heads-down executing. By definition, you are heads-down. You are not building the external proof layer that tells your industry you exist. Your reputation stays local. Others with thinner credentials build global footprints from their laptops. It sucks for the actual experts. This is about legibility. The difference between expertise that compounds inside a dusty grain pit and expertise that compounds in public. The invisibility problem cuts deeper for women in knowledge work. A 2025 study in [*Nature Communications*](https://www.nature.com/articles/s41467-025-60590-y?ref=lennartnacke.com) examined 23 million tweets about 2.8 million research papers by 3.5 million scientists. Women are 28% less likely than men to self-promote their work on social media. Even after controlling for confounding factors. Even in research areas with balanced gender representation. The system creates a higher structural cost for visibility among groups already navigating asymmetric environments. Broken AF. Even HR professionals, the people responsible for building appreciation infrastructure, rate their own sense of being valued at just 34%. The people designing the recognition architecture don't feel recognized by it. Your boss isn't fixing this. Your department head isn't fixing this. You're the only one who will. ## What changed when Sahil Bloom started sharing his thinking In March 2020, Sahil Bloom was a Vice President at Altamont Capital Partners, a private equity firm focused on control investments in middle-market companies. He worked 70-plus-hour weeks. Traveled constantly. His public output was a monthly email to close family and friends. A reading diary with takeaways. About 500 Twitter followers from his college days. No real audience. No platform. No authority proof outside his firm. Then COVID hit. The travel stopped. The office routine dissolved. He had time. And nowhere to put his thinking except in public. By May 2020, he started writing Twitter threads. The formula was clarity plus consistency. Take complex financial and business concepts. Translate them into accessible stories with concrete, actionable lessons. And a bit of luck as Twitter threads were taking off at that time. He didn't become more skilled at investing when he started posting. He was already skilled. What changed was his visibility infrastructure. The layer between his expertise and the people who could benefit from it. Chenell Basilio compiled the documented breakdown of this trajectory at [Growth In Reverse](https://growthinreverse.com/sahil-bloom/?ref=lennartnacke.com). From zero public audience, Bloom grew to 1.9 million followers. He built The Curiosity Chronicle into one of the fastest-growing independent business newsletters on the internet. He left private equity entirely, building a creator-led holding company documented at [The B2B Creator](https://theb2bcreator.com/sahil-bloom/?ref=lennartnacke.com). Sam Parr and Shaan Puri ran parallel experiments during the same window. The pattern across all of them is not genius or luck. Consistent output of genuine expertise, made legible to an audience that couldn't see it before. Bloom's expertise was always there. The market had no way to see it. That's the same game you're playing. ## Simon Sinek's simple one-idea lesson In 2009, Simon Sinek gave a TEDx talk at TEDxPugetSound. He was an independent researcher and consultant. Not famous. Not attached to a major firm. Not a bestselling author. And it wasn’t even real TED if you catch my drift here. The talk was called ["How Great Leaders Inspire Action."](https://www.ted.com/talks/simon%5Fsinek%5Fhow%5Fgreat%5Fleaders%5Finspire%5Faction?ref=lennartnacke.com) You probably know it as the "Start With Why" talk. Over 60 million views on [TED.com](http://ted.com/?ref=lennartnacke.com) alone, making it one of the top three most-watched TED talks ever. Sinek had one clear, simple, repeatable idea. Great leaders communicate from the inside out. They start with *why* they do what they do. Not *what* they do. He repeated that idea with conviction across every available channel. That single framework built a career as a Fortune 500 keynote speaker, a New York Times bestselling author, and a founder of a global leadership practice. He didn't become wiser between 2009 and 2015\. He became more visible. Gained impact. Authority does not require breadth. It requires a sticky, memorable point of view. Repeated with conviction. Across enough channels that the right people can't help but encounter it. One idea. Consistently shared. Ignore the noise. Ship your voice. (Gotta deliver the rhyme when it hits you in the face. I’d print that on a t-shirt, Joanna.) ## The academic visibility crisis Publishing is no longer enough. The system itself is compromised. Paper mills run as industrialized supply chains selling academic prestige. For a few hundred dollars, a researcher buys first-authorship on a pre-written paper. AI changed their production cycles into overdrive. Operators feed datasets into automated systems that compare random variables, generating output that looks statistically valid and means nothing. To dodge plagiarism detection, these systems swap terms with absurd synonyms. Breast cancer becomes random sh\*t like "bosom disease." Kidney failure becomes "kidney disappointment." Thousands of fabricated studies flood the literature. Funding boards reject real grant applications because the field looks saturated. Some have no idea a bot farm generated the fake saturation. This industry exists because "publish or perish" tied careers to output volume. And open-access journals charge authors a fee per accepted paper. But every acceptance generates revenue. Drop your standards, print more money. The assumption that publishing was how ideas propagated and researchers earned recognition is so dead today. Your 40 legitimate publications sit in the same indexes as products someone bought for $300\. I could scream. The 2025 [*Nature Communications* study by Peng et al.](https://www.nature.com/articles/s41467-025-60590-y?ref=lennartnacke.com) provides structural evidence. Self-promotion on social media contributes to a measurable gap in the visibility of scientific ideas. The gap widens with seniority. Research-prolific women at top institutions who publish in high-impact journals show the largest disparity. Visibility tracks promotion behaviour. Those numbers don't measure publication quality. The academic community has started naming this. *Visible or vanish.* Publishing earns you a credential in a darkroom filing cabinet. Active self-promotion through social media, public writing, strategic networking, and accessible translation of your research is now required for the kind of career traction that publishing alone used to provide in the past. I get it. Saying this out loud in a culture that prizes peer review and treats self-promotion as suspect feels wrong. Academics hate marketers. But the discomfort doesn't change the mechanics, my friend. The researchers who build the careers they want are not the ones with the most rigorous methodologies. They are the ones whose rigorous methodologies are *legible* to the people making hiring, funding, and collaboration decisions. That’s the game. You can be both rigorous and visible. Right now, the combination is a competitive advantage so rare it constitutes an unfair edge. ## Why AI broke the production barrier The gap between having expertise and visible expertise has never been cheaper to close. A PR firm used to cost $10,000 a month. A publisher took 18 months. A content team required 6 figures in payroll. Now a single person with the right AI tools, prompts, and automation systems can compress that entire production chain into a structured process that runs parallel with their actual work. I know this because I’ve done it. The 2025 Edelman-LinkedIn B2B Thought Leadership Impact Report, [available here](https://www.edelman.com/expertise/Business-Marketing/2025-b2b-thought-leadership-report?ref=lennartnacke.com), draws from nearly 2,000 global professionals. High-quality thought leadership is more effective than traditional marketing at conveying a supplier's capabilities, building trust, and influencing purchasing decisions. The demand for your expertise, expressed as thought leadership, has never been higher. The buyers, the funders, the decision-makers. They are looking for a lighthouse in the fog. Marketers call it brand. I call it authority. AI has made it possible for a single person to be that signal. Without a team. The right AI stack handles ideation, drafting, editing, and repurposing. You can move from raw idea to finished, publishable asset in a fraction of the time it took 3 years ago. One insight becomes a LinkedIn post, a newsletter edition, and a long-form essay. All without a communications department. This is not about flooding the internet with slop. The Edelman-LinkedIn report is explicit. Quality matters. You have to edit and spice the soup. High-quality thought leadership builds trust. Low-quality thought leadership damages it. The AI leverage only works when it amplifies genuine expertise. Not when it manufactures the appearance of it. Case in point. I talked to AI to explain this article. AI wrote it based on my ideas. I edited it for 2 hours. Quality always takes extra time. You need the AI layer and the domain credibility. Together, they compound. ## The AI authority blueprint Build the wiring. Speed up the inspo. Here’s an operational sequence you can run. **Step 1: Define your differentiated perspective** You need a positioning statement that makes strangers understand your value in 30 seconds. Not "AI is changing everything." Everyone says that. A sticky, specific point of view on what most people in your industry get wrong about AI's role in your field. The kind of sentence someone hears at dinner and remembers the next morning. Most experts skip this step because they think their credentials speak for themselves. Credentials open doors. Positioning keeps people in the room. **Step 2: Commit to one niche** Pick one. The instinct is to stay broad so you don't exclude anyone. The result of staying broad is that you're legible to no one. Bear with me here. Niche is not a constraint. It is the mechanism by which you become findable. Most experts stumble here for months because choosing feels like leaving money on the table. It doesn't. Choosing is the only way to get on the table. **Step 3: Anchor every piece to evidence** Opinion without evidence keeps you stuck in the noise. Opinion grounded in a specific number, case, or traceable observation is thought leadership. One data point per piece. Minimum. Sourced, linked, honest about what it does and doesn't show. The discipline separates the credible crowd from the loud losers. **Step 4: Build a content cadence and hold it** Compounding requires consistency. A structured rhythm calibrated to your schedule, your niche, and your audience's consumption patterns. Bloom didn't grow to 1.9 million followers by posting once and going quiet. Sinek didn't build a global platform from one talk. The cadence is the strategy. The rhythm makes the music. Most experts know this. They fail at it because they haven't solved Steps 1 and 2 first. Fix the positioning, and the cadence stops feeling like a grind. **Step 5: Develop a responsible AI narrative** This separates serious practitioners from the gazillion hype merchants out there. Your readers, your peers, your prospective clients. They are worried about the downsides. Governance. Bias. Displacement. If your thought leadership addresses only the upside, you read as a vendor. Not an authority. If you address the hard questions you build the deepest trust. Too many skip this step because it's harder to write. That's the point. **Step 6: Weaponize LinkedIn and AI search** Decision-makers ask AI tools who the authoritative voices are in their field. The answers come from indexed public writing. If you haven't been producing, you don't exist in that answer. Normal homepages don’t cut it anymore. LinkedIn's algorithm rewards consistent, niche-specific content with disproportionate organic reach. Pair that with AI-assisted search visibility and you have the most underused authority-building channel available to knowledge workers right now. The window to be an early mover is open. It will not stay open. I squeezed this entire sequence into an 8-letter system called **IDEA 2 SHIP**. It runs in 90 days with live coaching, AI tools trained on the methodology, and a peer cohort of experts running the same playbook. I’m launching it on April 1. ## The system that runs these 6 steps I built the visibility infrastructure described above over 3 years. Wrong platforms. Wrong cadences. Wrong positioning attempts that attracted the wrong audience. I documented every failure and every correction. The result is **IDEA 2 SHIP**, my simple methodology that takes an expert from invisible to findable in 90 days. [**The Write Insight Lab**](https://store.lennartnacke.com/b/lablaunchoffer?ref=lennartnacke.com) is where the system runs. A founding cohort of experts with real depth, real stakes, and zero interest in becoming influencers. Weekly live coaching calls. AI workflows trained on the methodology. Peer mastermind pods matched by level and goals. Monthly open coaching labs where you watch positioning and content problems get solved in real time. The founding bundle includes 1 year of The Write Insight premium newsletter (52 weekly deep dives on authority, writing craft, and AI workflows) plus 3 months of full Lab access. **$294\. One payment.** For context, that's less than one hour of consulting should be at your rate. After the founding cohort closes, the Lab alone moves to $799/year. Founding spots are capped and the offer expires on March 31, 2026\. If you've ever been on the fence about upgrading, now is the time. **→** [**Get the founding bundle here before March ends.**](https://store.lennartnacke.com/b/lablaunchoffer?ref=lennartnacke.com) ## What you do now You have a choice. It is not complicated. You can keep producing excellent work inside a system that has no mechanism to surface it. You can wait for recognition that the data says will not arrive on its own. Or you can spend the next 90 days building the visibility infrastructure that turns your existing expertise into a compounding public asset. With a system, a cohort, and a coach who's done it. [Join the founding cohort](https://store.lennartnacke.com/b/lablaunchoffer?ref=lennartnacke.com) "They have a third of my depth," she told me over coffee. "But they have an audience." She doesn't say that anymore. Now, let’s build yours. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Bonus [Premium members](https://store.lennartnacke.com/b/lablaunchoffer?ref=lennartnacke.com) get the full deep-dive article with extended case studies (Sahil Bloom, Simon Sinek, the paper mill crisis), 2 print-ready PDF worksheets (Visibility Gap Scorecard + 6-step Implementation Guide), 3 AI prompts, 5 curated resources, and the 90-day AI authority build checklist. That's 6 steps, 3 prompts, and a complete system for less than a coffee a week. _This post is for paying subscribers only._ ### Prompt Engineering Is Your Most Expensive Habit URL: https://lennartnacke.com/prompt-engineering-is-your-most-expensive-habit/ Last updated: 2026-03-20T04:53:38.000Z When I became a professor, nobody handed me a manual for delegation. I had no staff. No research coordinator. No admin support. Just me and a handful of grad students trying to run a lab on duct tape and good intentions. I did everything myself, from ethics board submissions to formatting reference lists to scheduling participant sessions. Then the grants came in, and suddenly I could hire people. Great. One problem: I had to explain how I actually did things. Not the big-picture strategy. The tedious, repeatable, 21-step administrative workflows I'd been running on autopilot for years. I spent weeks writing SOPs (standard operating procedures). Sitting down. Documenting every click, every decision tree, every edge case. It was one of the most cognitively demanding things I've done as a professor, and I say that as someone who's written 150+ peer-reviewed papers. Externalizing your own process forces you to confront how much of your expertise lives as muscle memory you've never articulated. The reward came when my staff actually followed the SOPs. Consistently. Without me hovering. That feeling of watching someone execute your process correctly, without a single clarifying email, is worth every hour of documentation. Now here's the thing. AI follows SOPs better than any hire you'll ever make. No off days. No misread instructions. No wrong formats. And with the recent addition of skills in Claude (+Antigravity and +Codex), I can describe specific parts of my workflow, administrative, editorial, analytical, directly to the AI. It runs them correctly every single time. I iterate the instructions over time. They get better with each use. That's what this issue is about. The 15-minute version of what took me weeks to learn about delegation. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## The skill-based system that turns 15 minutes of setup into permanent AI memory You've spent 15 years building expertise that fits inside your skull and nowhere else. You can diagnose a client's real problem in 20 minutes. You can read a financial model and spot the 3 assumptions that will blow up by Q3\. You can walk into a boardroom, hear 45 minutes of cross-talk, and distil it into the 2 decisions that actually matter. Then you open Claude. Or ChatGPT. Or whatever AI tool earned a spot on your dock this month. And you start from zero. You re-explain your consulting framework. You re-paste your report structure. You re-describe the tone your clients expect, the severity ratings you use, the way you frame recommendations so a C-suite exec actually reads past the first paragraph. Fifteen minutes of context-loading before you get a single useful word out. You do this every day. Sometimes twice. Task switching has a measurable cognitive cost. Psychologists David Meyer, Joshua Rubinstein, and Jeffrey Evans ran a series of experiments showing that people lose significant time when shifting between tasks, and the losses compound with complexity. Meyer concluded that even brief mental blocks created by shifting between tasks can [cost as much as 40% of someone's productive time](https://www.apa.org/topics/research/multitasking?ref=lennartnacke.com). The APA's summary of this research is blunt: The mind and brain were not designed for heavy-duty multitasking. Now layer generative AI on top of that. A [2024 study published in *Business Horizons*](https://www.sciencedirect.com/science/article/pii/S0007681324000533?ref=lennartnacke.com) found that effective use of GenAI depends on iterative prompt engineering, a back-and-forth refinement process where the human shapes the AI's output through successive rounds of feedback. The researchers frame this as *human-AI knowledge co-construction*, which means you're not typing a query and getting an answer. You're teaching the machine your standards, your context, your judgment, one correction at a time. That iterative loop is itself a form of hidden knowledge work. And every time you close the chat and open a new one, the co-constructed knowledge vanishes. You start the teaching cycle from scratch. The productivity data on AI-assisted work makes this even clearer. [Brynjolfsson, Li, and Raymond](https://arxiv.org/abs/2304.11771?ref=lennartnacke.com) studied 5,172 customer support agents and found that access to an AI assistant increased productivity by 15% on average. The largest gains went to less experienced workers. The most experienced workers, the ones with the deepest expertise, saw the smallest improvements. One plausible explanation is that experts already have strong mental models, and the friction of re-contextualizing AI eats into the gains that less experienced workers get for free. The industry has noticed. Anthropic built skills into Claude. And every major AI provider now lets you easily add them. Wvery AI product is in a hurry to tackle that cold-start challenge. It really hits when you’re most informed. Skills are one of the best version of that fix. They externalize your repeated context into permanent, reusable files. They reduce re-entry work to zero. And they turn AI interactions from a fresh conversation every time into a reusable toolchain that remembers your methodology, your standards, and your corrections. I tracked my own numbers. One hour per week re-contextualizing AI tools. 52 hours per year. That's more than a full work week spent telling a machine things it should already know. You assumed the people who get better AI output are better at prompting. They aren't. They simplified prompting altogether. They built skills. ## 1\. Understand what a skill actually is (and why it's the SOP your AI never had) Every time you got great output from an AI chat, you built a skill. Then you closed the tab and threw it away. A skill is a folder with 1 or more files of instructions. No API. No code. No engineering degree. That's the whole thing. Think about the best hire you ever made. You didn't re-explain your entire methodology every morning. You trained them once. Handed them an SOP. Gave them reference materials. They executed. When they made a mistake, you corrected them once. They never made it again. Skills are the SOP for your AI, except your AI never has an off day, never forgets the correction, and can run the skill at 5 AM on a Sunday while you're asleep. The concept works across Claude, Antigravity, Codex, Cursor, VS Code, and other AI editors adopting the same standard. Build 1 skill. Use it everywhere. Three components. That's all. 1. **The `skill.md` file (the brain).** Your step-by-step process instruction. A recipe card. Clean, focused, no clutter. 2. **Reference files (the context).** Supporting documents that give the AI the knowledge it needs. Your report template, your severity rubric, your brand guidelines, example deliverables. These live alongside the `skill.md` in the same folder. 3. **Metadata (the label).** A name and short description at the top of your `skill.md`. This is the only part the AI reads when deciding whether to activate the skill. The label on the folder spine. One folder. One process file. A few reference docs. Done. ## 2\. Build your first skill in 15 minutes using work you've already done Two methods. Starting with the easier one. ### Method 1: Do it once, then codify Do the task manually with the AI. Iterate until the output matches your standard. Then tell it to save the process as a reusable skill. Say you're a consultant who writes executive briefings after every client engagement. You've been pasting your briefing format into Claude for months. Open Claude. Do the briefing from scratch one more time. Be specific. Push back when the output isn't right. "The executive summary needs to lead with the business impact number, not the methodology." "Strip the jargon. My client's CEO has 90 seconds for this." "Add a risk section with probability ratings." Keep going until you'd sign off on it for a paying client. Then say: Copy Turn this entire process into a reusable skill. Create a skill.md file with the step-by-step instructions, save any reference files we used, and register the skill in my catalogue. Claude creates the folder, writes the process file, organises your reference materials, and registers the skill. Start a fresh chat. Trigger the skill by describing what you need. If it works, you're done. If not, refine the `skill.md`. ### Method 2: Build from scratch If you already know the exact workflow, skip the manual step and build the skill file directly. Open your `skill.md` and define 4 things. First, a **trigger**: a plain-language description of when the skill should activate, specific enough that the AI knows exactly which requests match (e.g., "executive briefing, client summary, engagement recap"). Second, a **goal**: one sentence describing the end deliverable and its quality bar (e.g., "Generate a branded executive briefing with business impact analysis and strategic recommendations"). Third, a **process**: the numbered steps the AI should follow, in order, from reading reference files through drafting to final output. Be explicit about where to pause for your input and where to proceed autonomously. Fourth, **rules**: the non-negotiable constraints that prevent the AI from cutting corners or drifting from your standard (e.g., "Every finding must include a business impact estimate," "Never skip reading the reference files"). Tell Claude to build the `skill.md` from those 4 components, create placeholder templates for any reference files you mentioned in the process steps, and register the skill in your catalogue. That's a functional starting point in under five minutes. You'll smooth it out over the next few uses. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## 3\. Build the 4 reference files that make every skill 10 times sharper Before you go skill-crazy, build these four files first. You'll reuse them across everything. ### 1\. Professional context (`professional-context.md`) Your practice, your methodology, your principles. How you define quality work. What your clients or stakeholders value in a deliverable. Two to three paragraphs. Start with who you are and what you do: your domain, your experience level, the type of clients or stakeholders you serve. Then describe how you define quality work, the specific standards a deliverable has to meet before you'd put your name on it. Finish with 2 to 3 operating principles that guide your professional judgment, the kind of rules you'd give a senior hire on their first day so they understand how you think. Keep the whole thing to 2 or 3 paragraphs. Specific enough that the AI can make judgment calls on your behalf. General enough that it applies across multiple skills. ### 2\. Audience map (`audience-map.md`) Who reads your work, who acts on it, and what they need from it. List every person or group that regularly receives your output. For each one, write down their role, what they care about most, what decisions they make based on your work, and the format they prefer. A CEO scanning for a go/no-go decision needs a different document than a technical lead prioritising next quarter's roadmap. Rank them by how often they see your work: primary audience first, then secondary, then anyone else who occasionally reviews your output. The AI uses this map to adjust tone, depth, and structure depending on who the deliverable is for. ### 3\. Output template (`output-template.md`) The structure your finished deliverable should follow every time. Describe the sections your output needs, in order, and what belongs in each one. Include any formatting rules you care about: heading style, how numbers are displayed, file naming conventions, required sections versus optional ones. The goal is a reusable skeleton that the AI fills in with the right content every time. If you already have a PDF, Word document, or other file that shows the exact structure you want, drop it into the skill folder as a reference file and point the `skill.md` to read it. The AI will reverse-engineer the layout, section order, and formatting conventions from that document. Sometimes an existing output is the fastest template you can give it. ### 4\. Good output examples Past deliverables that represent your standard. The AI reverse-engineers patterns from examples better than from descriptions. Full stop. Pull two to three of your strongest past deliverables. Structure each one as a reference file. Drop two to three files like this into your skill folder. After 3 examples, the AI starts matching your standards and style. ## 4\. Use progressive disclosure so your AI loads only what it needs Here's the mechanism that makes skills work at scale without drowning the AI in context. Jakob Nielsen at Nielsen Norman Group formalized *progressive disclosure* as a core interaction design principle: show only the label until someone needs the full manual. Don't load what you don't need yet. When you start a new chat, the AI doesn't read every skill file you've ever built. It reads only the metadata: the name and 1-line description at the top of each `skill.md`. That's it. 50 skills. A few hundred words in memory. The full instruction file loads only when you say something that matches the trigger description. Say "generate an executive briefing" and the briefing skill activates. Say "draft a thought leadership article" and the article skill fires. The AI pulls the relevant `skill.md` into active context, reads the reference files, and executes. Anthropic published a detailed engineering guide on this exact principle. They call it *context engineering*: the practice of curating everything the AI sees before it responds, rather than cramming instructions into a single prompt. The shift from prompt engineering to context engineering is the difference between giving someone a 40-page manual every morning versus giving them a filing cabinet and letting them pull the right folder. This is why the trigger description matters. A vague description means the AI doesn't know when to activate. A specific description fires reliably. The last step is registration. Every skill needs an entry in your master `claude.md` file (the root-level file Claude reads at the start of every session). Each entry is just a name, a 1-line description, and the trigger phrases. When you finish building a skill, prompt Claude to add it: Copy Add this skill to my claude.md catalogue with its name, description, and trigger phrases. Claude appends the entry. Every new skill, 1 new line in the catalogue. The AI reads this file at the start of every session and knows which skills are available without loading any of the full instruction files. ## 5\. Iterate until the skill works better than your best junior hire Your first version won't be perfect. By design. Skills are living documents. **The AI skips steps or does them out of order.** Make the process flow clearer. Add explicit "do not proceed until this step is complete" language. **The output feels generic.** Don't bolt more text onto the `SKILL.md`. Create a new reference file with the missing context and point the skill to read it at the relevant step. **The AI keeps making the same stylistic mistake.** Add a specific rule. "Never list more than 3 strategic recommendations per briefing because executives stop reading after 3." Precise rules get followed. Vague rules get ignored. **You want the skill to learn automatically.** Add 2 self-improvement rules: 1. "If I correct a behaviour, update the rules section with the correction." 2. "If I approve a final output, save it as a reference example file." Over time, the skill accumulates corrections and good examples. 10 uses in, it produces better output than you could get from a fresh chat in 30 minutes of prompting. The compound effect is real. Here's a demonstrated prompt you can use right now to audit an existing skill: Copy Review my [skill-name] skill. Check for: 1. Any steps that could be misinterpreted or skipped. 2. Rules that are too vague to enforce consistently. 3. Missing reference files that would improve output quality. 4. Trigger descriptions that might conflict with other skills. Suggest specific improvements for each issue found. MIT Sloan's [Miro Kazakoff](https://mitsloan.mit.edu/ideas-made-to-matter/curse-knowledge-why-experts-struggle-to-explain-their-work?ref=lennartnacke.com) studies why experts struggle to communicate their own knowledge. He calls it the *curse of knowledge*. The deeper your knowledge, the harder it becomes to articulate what you know, because you've deleted the memory of what it felt like not to know it. I spent weeks writing SOPs for human staff before I understood this. The hard part was never the execution. It was the externalisation, forcing yourself to articulate what you do, step by step, decision by decision, edge case by edge case. Skills distill that weeks-long process into 15 minutes. One group opens a new chat every morning, re-pastes their framework, re-explains their methodology, and calls it using AI. Fifty-two hours a year of re-explaining yourself to a machine. The other group spends 15 minutes building a skill and never re-explains anything again. Their AI remembers their methodology, their standards, their corrections. Permanently. For a setup that takes less time than making coffee. Build the skill. Let the AI remember for you. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Bonus The [Write Insight subscribers with an AI Research Stack premium account](https://lennartnacke.com/#/portal/signup/) this week also get 2 print-ready PDF worksheets (a one-page Skill Priority Triage Card and a multi-page Complete Skill Building Worksheet), 3 AI prompts (generate branded executive briefings from client engagement data, draft long-form thought leadership pieces from rough notes or talking points, and build client-ready proposals from discovery call notes), 5 curated resources on agentic AI workflows and skill-based memory systems, and a full 5-phase Build Your First AI Skill checklist. _This post is for paying subscribers only._ ### Your Deep Expertise Is Why Nobody Knows Your Name URL: https://lennartnacke.com/your-deep-expertise-is-why-nobody-knows-your-name/ Last updated: 2026-03-26T00:04:41.000Z In 2021, Katalin Karikó’s research saved the world. Yet, I bet you’ve never heard of her. In 1995, the University of Pennsylvania gave Dr. Katalin Karikó a simple choice. She could leave, or accept a demotion. She had spent six years as a research faculty member in the School of Medicine, working on messenger RNA, a molecule she believed could teach human cells to produce their own therapeutic proteins. She had the science. She had the conviction. She did not have the grants. Karikó chose the demotion. She took the pay cut. She kept working. For the next decade and a half, she operated at the margins of an institution that had decided she was a dead end. Grant applications came back rejected, one after another. Colleagues saw her research as too complex, too speculative, too far from anything that would produce fundable results within a standard grant cycle. She was, by multiple accounts, called “the crazy mRNA lady.” At one point she didn’t have her own lab space. She relied on the goodwill of senior colleagues to stay employed at all. The science she was building during those years of institutional exile, solving how to get synthetic mRNA into human cells without triggering a lethal immune response, would become the foundation of the Pfizer-BioNTech and Moderna COVID-19 vaccines. The work that UPenn considered unfundable would eventually earn her the 2023 Nobel Prize in Physiology or Medicine. But that vindication was decades away. In the late 1990s and 2000s, the only thing Karikó’s career demonstrated to the outside world was that having the right answer, delivered without the right packaging, gets you demoted. The world was never fair. While Karikó was struggling to keep a lab bench at Penn, Elizabeth Holmes was building Theranos into a $10 billion company on science that really did not work. Holmes founded Theranos in 2003 at age 19, claiming she had developed a device that could run hundreds of diagnostic blood tests from a single drop of blood. The technology was, by every subsequent investigation, fraudulent. It physically did not function. ‘Fake it til you make it’ taken to the extreme. When investors or board members pressed for engineering specifics, Holmes invoked trade secrets. When regulators started asking questions, she lawyered up. She raised approximately $724 million from private investors. With hot air. Holmes did not sell data. She sold a narrative. A compelling idea. She spoke publicly about her fear of needles, connecting her company’s mission to a childhood emotion that every listener could feel. She wore a black turtleneck in conscious imitation of Steve Jobs, borrowing the visual language of a tech visionary. She deepened her voice in interviews and presentations, a deliberate status signal that multiple former employees have confirmed. The result of all of this was that Holmes appeared on the covers of *Fortune* and *Forbes*. The Obama administration named her a Presidential Ambassador for Global Entrepreneurship. Her board of directors included Henry Kissinger and George Shultz (both former Secretaries of State), Jim Mattis (later Secretary of Defense), two former U.S. senators, and a retired Navy admiral. If your advisory board looks like a season finale of *House of Cards*, someone should be asking harder questions about the dumb blood-testing machine. The contrast between these two careers is the most expensive case study in modern biotech for what happens when institutions cannot tell the difference between confidence and competence. Markets fund stories, not solutions. Karikó had the data, the decades of work, the actual scientific answer. But heck, she was ignored because everything about her delivery, her grant writing, her institutional status, her presentation style, signalled the struggling academic. Holmes had nothing that worked. She was celebrated because everything about her delivery signalled that she was a Silicon Valley genius. Somewhere out there, a grant reviewer with a stack of Karikó’s applications was probably super impressed by Holmes’s pitch deck. Peer review at its finest. One of them saved millions of lives and won the Nobel Prize. The other is serving an eleven-and-a-quarter-year federal prison sentence for fraud. This is the sad reality facing every expert who has spent years building deep knowledge that the right people still haven’t noticed. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Why your brain was never listening to your data In 1994, a neuroscientist named Antonio Damasio published a book that quietly rewrote our understanding of how humans make decisions. The book was called [*Descartes’ Error*](https://en.wikipedia.org/wiki/Descartes%27%5FError?ref=lennartnacke.com), and its core argument was that emotion is not the enemy of reason. Emotion opens the door to reason. Damasio had been studying patients with damage to the ventromedial prefrontal cortex, the brain region that connects emotional processing to decision-making. These patients could analyze options with perfect logical clarity. They could list pros and cons. They could evaluate evidence. What they couldn’t do was choose. Without the emotional weighting system that the rest of us take for granted, they were paralyzed by their own rationality. Damasio called this the somatic marker hypothesis. The idea behind this is that your brain tags experiences with emotional markers (gut feelings, essentially) and those markers become the shortcuts your limbic system uses to make decisions before your neocortex even finishes processing the data. You don’t decide to trust someone and then feel comfortable. You feel comfortable, and then your conscious mind constructs a rationale for why. Serious brains. Silly wiring. Read that again, McFeely. This is not pop psychology. This is the architecture of our own human cognition. Every investor who decides to fund a project, every executive who greenlights a budget, every grant reviewer who scores a proposal is running the same flawed neural sequence. Emotion first, justification second. The neocortex thinks it’s in charge. But the limbic system votes before you reason. Your prefrontal cortex is basically the person who joins a Teams call 10 minutes late, stays on mute, and then says, “Sorry, can you repeat that?” The whole room waits with the dead-eyed patience usually reserved for printers and airport kiosks. And this explains exactly what happened to Katalin Karikó over 30 years of underfunding. Her grant applications were speaking to the neocortex, the analytical brain, the part that evaluates evidence and weighs probabilities. Elizabeth Holmes’s pitch was speaking to the limbic system, the part that actually initiates decisions. Karikó was trying to convince review boards with data. Holmes was making investors *feel* something first. A childhood fear of needles. A turtleneck that whispered *Steve Jobs*. A voice pitched low enough to signal control. Each of those details created a somatic marker in every person in the room. When the (non-existent) data came afterward, the brain already understood what to do with that. I’ve made my own version of this mistake so many times I’ve lost count. Fifteen years in academia. Publications. Citations. Conference presentations. Tenure. A cool research lab. All the credentials that were supposed to signal competence. And for years, I operated under one assumption only. If I just kept producing better work, the right people would eventually notice. And fame and fortune might follow. The meritocracy trap. The belief that excellence is self-evident. It’s like expecting your Hogwarts acceptance letter to arrive by owl post when you haven’t checked the mailbox. The letter might exist. There is no Hedwig delivering it for you. Now I know that what I assumed was a visibility problem was a connection problem. I was broadcasting data. I wasn’t telling stories. The expertise was real. The communication strategy was broken. The irony still stings. I had spent years studying human-computer interaction, understanding how people process information, how games shape behaviour, how cognitive load determines whether someone engages or tunes out. I had the theoretical framework to understand exactly why my own communication was failing. I just never thought to apply it to myself. Busted. ## Don’t mistake your credentials for connection You’ve felt this. You’re in a meeting, and you know more about the topic than anyone else at the table. You have the data. You have the experience. You open your mouth, and you lead with your strongest evidence. Fifteen minutes later, someone with half your expertise tells a two-minute anecdote, and the room perks up like Miss Piggy. Suddenly everyone’s nodding. The conversation pivots around their point, not yours. You sit there wondering what just happened. Maybe you’re a little pissed. I know I would be. What happened is that they gave the room an emotion, and you gave the room receipts. The limbic system won. It wins every time. Emotion is the gateway through which evidence enters into our mind. At the end of the day, we’re just fancy next-gen apes in suits or skirts. Without that gateway wide open, the best data in the world stays on a slide that nobody acts on. In 1966, psychologist Elliot Aronson at the University of Minnesota published [a study in *Psychonomic Science*](https://doi.org/10.3758/BF03342263?ref=lennartnacke.com) that should be required reading for every expert wondering why their credibility doesn’t land. Participants listened to a recording of a person answering quiz questions. The person got 92% right. In one version, the person then knocked over a cup of coffee on themselves. Clumsy. Human. Imperfect. The clumsy expert was rated as significantly more likeable than the flawless one. Aronson called it the pratfall effect. A highly competent person who reveals a visible human moment becomes *more* attractive to others. The stumble signals something the polished performance can’t do. It shows us that this person is real. (The effect only works for high-competence individuals though. When an average performer spills the coffee, they just look incompetent. You need the credentials first. Then you’re allowed to mess up in public.) This is the academic equivalent of wearing a wrinkled shirt to your keynote. On purpose. It only works if you’re the keynote speaker. If you’re the technician, people just think you slept in your car. Sorry, Mark. In 2005, Target rolled out a completely redesigned prescription bottle called [**ClearRx**](https://lemelson.mit.edu/resources/deborah-adler?ref=lennartnacke.com). The system came from Deborah Adler, a graphic design student at the School of Visual Arts in New York. It started with her grandmother. Helen Adler had accidentally taken her grandfather Herman’s medication. The pharmacy had condensed both their names to “H. Adler” on identical amber bottles with tiny text. Same last name, same first initial, same container. Helen couldn’t tell them apart. The pharmaceutical industry knew medication errors were a massive problem. They had decades of data. Pharmacists, doctors, regulators, all of them understood the numbers. And all of them kept designing labels for *compliance.* They crammed the legally required text onto a cylinder that rolled off the counter and into the junk drawer. They had the equivalent of a 40-slide deck of adverse-event statistics and FDA requirements. Forty slides. In landscape orientation. With a font size that qualifies as a HIPAA violation against your eyeballs. None of it fixed Helen’s kitchen table. For her thesis project, called SafeRx, Adler started with what she’d seen in her grandparents’ home. Two identical bottles that could kill someone. That’s a real design flaw. She redesigned the bottle from scratch. Flat-sided so it wouldn’t roll. Colour-coded rings so each family member’s prescriptions were instantly distinguishable (red for Helen, blue for Herman). The drug name printed on top of the cap, visible in a drawer. An information card tucked behind the label instead of stapled to a bag nobody reads. Target’s creative team discovered Adler’s work and adopted it across every in-store pharmacy in the country. The ClearRx bottle entered the permanent collection at the Museum of Modern Art. Adler hadn’t discovered a single new fact about medication errors. Every pharmacist in the country already had the data. She told the story of one grandmother’s kitchen table, and an entire industry’s approach to prescription packaging changed around it. Steve Jobs understood the same principle. When Apple launched the iPod, the engineering team had spec sheets full of storage capacity, transfer speeds, and codec support. Until then, the conversation among audiophiles and Hi-Fi fanatics had always been about sound quality, not the volume of music you could carry with you. Jobs distilled the pitch to five words: “1,000 songs in your pocket.” Boom. He didn’t present the drive size (5GB). He presented what 5GB *felt like* in your life. The pharmaceutical industry had been selling drive sizes to Helen Adler. Her granddaughter sold them 1,000 songs in their pocket. The prevailing wisdom claims that authority comes from credentials and evidence. I get it. We spent years earning those credentials, and it feels right that they should speak for themselves. Who doesn’t love to be called “Doctor.” Now, come on. Admit it. You dig it. What the research shows is different though. Authority comes from the willingness to be human first and an expert second. Credentials get you in the room. Stories make people listen once you’re there. The gap between those two sentences is where most experts lose their audience, their funding, and their influence. Karikó’s career is the starkest proof. She had credentials that would eventually win a Nobel Prize. For 30 years, those credentials couldn’t get her a funded lab. ## The CCR story framework: Context, conflict, resolution You may be thinking, “I’m not a natural storyteller. I’m an engineer. A scientist. A consultant. I deal in data and evidence, not narratives.” Fair enough. Most experts feel the same way. But here’s what I’ve learned after a decade of teaching communication to researchers and technical professionals: Storytelling is structure, not talent. And the structure is so absurdly simple. Every story that moves people, from Homer’s *Odyssey* to the latest TED talk that made you sit up straighter, follows three beats. Context. Conflict. Resolution. That’s it. If you’ve encountered [Randy Olson’s ABT framework](https://press.uchicago.edu/ucp/books/book/chicago/H/bo21174162.html?ref=lennartnacke.com) (And, But, Therefore) or Kurt Vonnegut’s “Man in Hole” shape of stories, you’ll recognize this skeleton. Context is the *And*. Here’s the world as it stands. Conflict is the *But.* Here’s what disrupted it. Resolution is the *Therefore.* Here’s what changed. I’ve stripped it down to three simple words because the experts I work with don’t need a screenwriting vocabulary refresher. They need a structure they can deploy in a hallway. **Context** tells the audience where they are. **Conflict** tells them why they should care. **Resolution** tells them what changed. Holmes understood this structure and deployed it with precision, even though nothing behind the narrative was real. Let’s inspect this again. Context: A young woman in a lab coat, driven by personal experience. Conflict: A childhood fear of needles, a healthcare system that demanded too much blood for basic tests. Resolution: A device that would make painful, expensive blood work obsolete. Every element was fabricated. The structure was flawless. And that structure is exactly why $724 million flowed toward a machine that didn’t work. Now imagine Karikó had been given the same structural coaching. Context: A biochemist who emigrated from Hungary with £900 sewn into her daughter’s teddy bear, who believed a molecule called mRNA could reprogram the body to heal itself. Conflict: Every institution she approached told her the science was a dead end, her university demoted her, her grants were rejected for years. Resolution: She kept working anyway, solved the problem that made mRNA therapies possible, and her discovery now protects billions of people worldwide. That story is more compelling than anything Holmes ever fabricated. Karikó just never told it this way. ![A schematic drawing of the CCR framework.](https://lennartnacke.com/content/images/2026/03/The-CCR-story-framework.webp) The CCR Story Framework Imagine you have a filing cabinet with three drawers. The top drawer is labelled “Where.” The middle drawer is labelled “What went wrong.” The bottom drawer is labelled “What changed.” Every time you need to communicate your expertise, in a pitch, a presentation, a grant proposal, a LinkedIn post, a hallway conversation, you open those three drawers in order. You don’t need to be eloquent. You don’t need dramatic flair. You need these three drawers. Not seventeen. Not a filing cabinet the size of your dissertation appendix. Just three. > *The formula is this: “When I was \[CONTEXT\], I encountered \[CONFLICT\], which led to \[RESOLUTION\].”* **Research presentation.** “When I started studying early biomarkers in 2007, I assumed the diagnostic challenge was technical, that we needed better imaging tools. Three years in, I discovered the real barrier was behavioural because patients weren’t seeking testing until symptoms were already severe. That insight transformed our entire research programme toward pre-symptomatic screening protocols.” **Client pitch.** “When we first audited your onboarding flow, we expected to find a design problem. What we found instead was a trust problem because new users didn’t believe the product could do what the marketing promised. That’s why our recommendations focus on proof points, not interface changes.” **Team update.** “When we launched the beta last quarter, we assumed our biggest risk was server load. The actual bottleneck was customer support. We’d underestimated ticket volume by 300 percent. We’ve since restructured the support pipeline, and average response time is down from 48 hours to six.” **Grant proposal.** “When I began this line of inquiry, the prevailing model predicted X. Our preliminary data showed Y. This proposal seeks funding to investigate what that discrepancy means for the field.” **Job interview.** “When I joined the team, retention was at 68 percent and declining. I identified that the root cause wasn’t compensation. It was a disconnect between new hires’ expectations and their first-90-days experience. After redesigning the onboarding process, retention climbed to 89 percent within two quarters.” ## Get The Write Insight Frameworks like this one. Every week. In your inbox. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. Notice what each of these does. The context grounds the listener. The conflict creates tension, a rift between expectation and reality. The resolution delivers the payoff. The listener’s brain doesn’t have to work to follow the logic, because the structure is doing the work for them. This is one of the core reasons why stories feel effortless to absorb even when the underlying ideas are complex. The other thing to notice here is that the conflict is always a surprise. The researcher expected a technical problem and found a behavioural one. The UX team expected a design flaw and discovered a trust deficit. The beta launch team predicted server strain and got buried by support tickets. The conflict works precisely because it violates the listener’s assumption. That violation creates a small spike of neurochemical attention, a burst of norepinephrine that the brain interprets as that it needs to pay attention because something unexpected is happening. Surprise spikes attention. Engineer it. ### The one thing they’ll remember Here’s a test. Think about the last conference talk you attended, and I mean the one you actually remember. Not the one with the best slides. The one that stuck with you. I’d wager you can recall one idea from it. Maybe two. A single image. A single line. A single moment where the speaker said something that made you think, “I need to write that down.” The sketchnote artist Katrin Wietek captures this principle with a deceptively simple phrase: Audiences remember one thing. Your job is to decide what that one thing is. Most experts resist this brilliant idea. After years of accumulating knowledge, the instinct is to share as much of it as possible. You’ve done the work. You’ve earned the depth. Leaving things out feels like a waste, or worse, an oversimplification. More slides. More static. In 2006, Hans Rosling walked onto the [TED stage](https://www.ted.com/talks/hans%5Frosling%5Fthe%5Fbest%5Fstats%5Fyou%5Fve%5Fever%5Fseen?ref=lennartnacke.com) with decades of UN health data and one animated chart. This was a talk I would remember for years. Rosling was a professor of global health at Sweden’s Karolinska Institutet. He had spent years teaching students about global development and discovered something unsettling. His students at one of Europe’s top medical schools knew less about the developing world than chance alone would predict. They had access to the same UN statistics he did. The data didn’t stick. The numbers didn’t move them. For his TED talk, he chose an animated chart built with his Gapminder software, showing 200 countries tracked across four decades, life expectancy on one axis, income on the other. As he narrated, the bubbles moved. Countries converged. The world visibly improved in real time, right in front of the audience. It was impressive. He could have shown tables. Reports. Statistical summaries. He chose one thing. Flowing data. The talk has been watched over four million times. Within a year, Google acquired the Gapminder visualization software and made it freely available worldwide. The data hadn’t changed. The same UN statistics had existed for years, and for years they had sat in databases while the world operated on assumptions Rosling could prove were wrong. What changed was the decision to show one idea, one image, one thing in motion, and trust that a single animated chart could do what decades of reports had not. You can do similar magic with any data you have using Claude Code today with just a few simple prompts. The discipline is the same whether you’re a global health professor or a data scientist presenting to a board. Cutting the other eleven findings feels like betraying the work. But the work only lands if someone receives it. The sacrifice isn’t the data. The sacrifice is the ego that wants credit for all of it at once. Suppress it. Make one point. Get heard. It means doing the hard cognitive labour of deciding which single insight matters most, and then building everything else around it. I didn’t know it at the time, but this principle would reshape how I approached every piece of communication I produced, from keynote talks to grant applications to the emails I sent to collaborators. The constraint of one idea is not a limitation. It’s an act of generosity toward your audience. You’re not withholding. You’re curating. You’re basically saying: “I’ve done the work of sifting through complexity so you don’t have to, my friend.” Think about the experts you admire most. The ones whose ideas have actually changed how you work or think. I’d bet they’re known for one thing each. Not because their knowledge is narrow, but because they had the discipline to lead with a single, great idea and let everything else orbit around it. Brené Brown: Vulnerability. Daniel Kahneman: Cognitive bias. Clayton Christensen: Disruption. Katalin Karikó (finally, after decades in obscurity): mRNA. Each of them knew a hundred things. They chose to be known for one. Meanwhile, your LinkedIn headline lists nine specialties, three certifications, and a partridge in a pear tree. Pick one. Let the rest orbit. Your expertise doesn’t speak for itself. It never did. Katalin Karikó had the right answer for 30 years. Nobody heard her until someone wrapped it in a story. That’s the same game you’re playing. Go package your expertise into a story people can feel. I’m rooting for you. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Bonus The [Write Insight subscribers with an AI Research Stack premium account](https://lennartnacke.com/#/portal/signup/?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=header-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter) this week also get 2 print-ready PDF worksheets (a one-page Expert Story Builder Card and a multi-page Expert Visibility Worksheet), 3 AI prompts (audit any presentation or paper for story vs. data balance, convert your expertise into a Context/Conflict/Resolution narrative, and extract the one thing from a complex document), 5 curated resources on expert storytelling and science communication, and a full 5-phase Expert Story Conversion Protocol checklist. _This post is for paying subscribers only._ ### Your Proposal Didn't Fail. Your Writing Did. URL: https://lennartnacke.com/your-proposal-didnt-fail-your-writing-did/ Last updated: 2026-02-28T05:10:02.000Z You spent 59 days on your research proposal. The reviewer spent 30 minutes. That’s half a lunch break. The rejection email arrives in four sentences. No detailed feedback. No explanation of what went wrong. Just: “We regret to inform you that your proposal was not selected for funding at this time.” And the worst part isn’t the rejection. It’s that you don’t know WHY. My early proposals were thorough. They were also forgettable. Here’s what nobody tells you about research proposals. The problem is almost never your research idea. A [2018 study by Elizabeth Pier and colleagues](https://www.pnas.org/doi/pdf/10.1073/pnas.1714379115?ref=lennartnacke.com) at the University of Wisconsin-Madison had 43 reviewers independently score the same 25 NIH grant applications. The agreement between reviewers was essentially zero. The outcome depended more on which reviewer happened to read your proposal than on the research you proposed. The ideas didn’t fail. Never. The proposals did. And most proposal-writing advice makes this worse, not better. They teach you seven sections to fill out like a form. Title, background, literature review, methods, timeline, resources, bibliography. Check, check, check. But a winning proposal is not a completed form. It’s an argument. Proposals that read like completed forms get treated like completed forms. Filed away. Every section exists to answer one question the reviewer is silently asking themselves. If you don’t know what that question is, you’re building IKEA furniture in the dark. Scary AF. This is the mistake that’s everywhere. It’s the mistake I made for years. My early proposals read like nice and dry textbook chapters. Thorough, properly cited, and utterly forgettable. They had no juice. It wasn’t until I started treating proposals as persuasion documents that anything changed for me. And there is a perfect story that shows exactly why this matters. In 1964, a chemist named Stephanie Kwolek was working at DuPont’s research lab in Wilmington, Delaware. Her job was to find a lightweight polymer strong enough to reinforce car tires. By 1965, she had created something unusual. A polymer solution that broke every rule. Standard polymer solutions were thick as molasses and clear. Hers was thin as water, cloudy like buttermilk, and shimmered when she stirred it. Any other researcher would have poured it down the drain. Kwolek didn’t. She brought it to Charles Smullen, the technician who operated the filament spinner. Smullen took one look and refused to run it. The solution would clog the holes. It would destroy his equipment. This was not what a polymer solution was supposed to look like. Kwolek came back the next day. And the day after that. She filtered the solution to prove it contained no particles. She argued. She insisted. Days passed. She had grit. Finally, in her own words: “I wore him down.” They spun it. It spun beautifully. The fiber was five times stronger than steel by weight. The results were so extraordinary Kwolek didn’t believe them herself. She asked colleagues to re-run the tests. The numbers held. She had invented *Kevlar*. She had the literal cure for bullets in a beaker. And she almost couldn’t get anyone to test it. Not because the science was bad. Because the presentation didn’t match what the gatekeeper expected. That’s the same game you’re playing every time you submit a proposal. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. Your reviewer has 30 to 50 other proposals to read. They’re tired, overcommitted, and 63 percent of their own research time is already consumed by administrative tasks. They will give your proposal maybe 30 minutes of genuine attention. That’s it. In those 30 minutes, everything you’ve worked on for months gets reduced to a single two-pack question: > Does this person understand what they’re trying to do, and do I believe they can pull it off? What follows are the seven moves that changed everything for me. Not seven form sections to fill out. Seven strategic decisions that turn a pile of good ideas into a proposal that makes reviewers want to throw money at you (and what recently won me half a million dollars in grant money). ## Get real about the problem you’re solving Your proposal lives or dies in the first page. Not the methodology. Not the timeline. The problem statement. Here’s why most problem statements fail. They describe a topic instead of a tension. You want friction. You want traction. You want what Steven Pressfield calls resistance (but not just in yourself but in the world). “This research will investigate the relationship between X and Y” is a topic. It tells the reviewer what general area you’ll be working in. It does not tell them why they should care, why it matters right now, or what will break real soon if nobody steps up and does this work. A problem statement needs to create discomfort. The reviewer should finish it and think: “Yeah, that **IS** a problem. Someone should fix that.” Imagine you’re at a dinner party explaining your research to a smart friend who works in a completely different field. Not your committee. Not your supervisor. Someone who will ask “So what?” and actually mean it. If you can make that person that challenges you lean forward, you’ve got a true problem statement. Stewart Butterfield, the co-founder of Slack, wrote an internal memo before the product launched titled “We Don’t Sell Saddles Here.” His argument was that if you were selling saddles in a world where nobody had discovered horseback riding, you wouldn’t talk about leather quality and stitching. You’d sell the dream of speed and freedom and range. The saddle is the artifact. The transformation is the product. If you’re in sales, I’m speaking your lingo here, but if you’re a deep subject expert, this concept is likely totally alien to you. Your problem statement works the same way. You’re not selling your method. You’re selling the transformation that becomes possible once this problem is solved. How are you making the world a better place? Gandalf didn’t recruit the Fellowship by describing the metallurgical properties of the One Ring, although I’m sure Tolkien would have loved to write a whole prequel just about that. He told them what happens to Middle-Earth if nobody walks into Mordor. That’s how you get a pair of shy Hobbits to climb up to Mount Doom. That’s the Kool Aid you need to be selling. > *The formula is: \[SPECIFIC GAP\] prevents \[SPECIFIC GROUP\] from \[SPECIFIC OUTCOME\], which matters because \[SPECIFIC CONSEQUENCE\].* For example: - Neuroscience: “Current brain-computer interfaces decode motor intention with 73 percent accuracy, which means patients with locked-in syndrome still cannot reliably communicate basic needs to caregivers.” - Education: “Teachers in low-income districts spend an average of 11 hours per week on administrative compliance reporting, displacing the equivalent of 55 instructional days per school year.” - Climate: “Existing carbon capture methods cost $400–600 per ton, roughly 4x what’s needed for commercial viability, which means the technology that could offset 15 percent of global emissions remains economically impossible.” Notice the numbers. Every single one. A claim without a number is an opinion. A claim *with* a number is cold hard evidence. Reviewers do trust evidence. Strong problem statements don’t describe the world. They diagnose what’s broken in it. They show everyone what’s hurting them. > Every week I send one system to [premium subscribers](https://lennartnacke.com/#/portal/signup/?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=header-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter): a framework, a prompt workflow, a writing structure. Something you can use before Friday to make your expertise visible.13,000+ experts are in. → [Join them.](https://lennartnacke.com/#/portal/signup/?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=header-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter) ## Context always makes them care On January 9, 2007, Steve Jobs walked onto a stage in San Francisco and announced three revolutionary products. A widescreen iPod with touch controls. A revolutionary mobile phone. A breakthrough Internet communicator. Being the eloquent and tricky speaker that he was, he cycled through them. “An iPod, a phone, an internet communicator… an iPod, a phone… are you getting it?” He kept accelerating. Then he revealed: “These are not three separate devices. This is one device.” Boom. Same product. Completely different emotional impact. He could have walked out and said “We made a phone with a touchscreen that plays music and browses the web.” Cool features. But by framing it as three separate breakthroughs first, he made the audience evaluate each function against its best competitor before revealing one device that dominated all three. The product didn’t change. The frame did. This is exactly what your background section must do. This is also why the Star Wars prequels disappointed fans when they opened with a trade dispute about taxation. The heck? Same galaxy. Same Force. Completely different emotional response from us. George Lucas proved that even a universe with cool light sabres can bore you the heck out of you if the frame is wrong. (Let’s not talk about Rian Johnson here.) Most researchers write background sections like Wikipedia entries. Here’s what we know. Here’s how we know it. Here’s where we are. That’s context without framing. It’s boring. Sorry. Your background section needs to walk the reviewer through the story of this problem. How it emerged, why it persists, what happens if nobody solves it. Give them something to gnaw on. Tversky and Kahneman proved this matters with hard numbers. In their [1981 famous framing experiment](https://sites.stat.columbia.edu/gelman/surveys.course/TverskyKahneman1981.pdf?ref=lennartnacke.com), they described an identical medical outcome two ways. Saying “200 people will be saved” versus “400 people will die.” You get it. It’s the same result. Seventy-two percent chose the first framing. Only 22 percent chose the second. Same facts. Different framing. Opposite decisions. A few moves that work here: - Start with the practical tough implication, not the theoretical foundation. “Antibiotic resistance kills 1.27 million people per year” hits harder than “antimicrobial resistance is an emerging area of concern in infectious disease research.” - Name your assumptions. Stating them clearly shows honesty. It also saves the reviewer from guessing if you see your own biases. - Define your terms. Grant reviewers are not always specialists in your exact subfield. If they have to Google something in your second paragraph, you’ve already lost momentum. (Pick up the reader at a bus stop they recognize before taking them on field trip somewhere new.) The background is not where you prove you’ve read everything. It’s where you prove you understand why this research matters to someone who doesn’t study what you study. That’s your goal. ## That hole in the world only you can fill Your literature review is not a book report. I made this mistake for years. I treated it like a comprehensive survey of everything ever written on my topic, organized chronologically, with polite nods to everyone who came before me. I was so afraid to forget someone. Thorough. Well-cited. Completely useless for convincing anyone to fund my work. Here’s what a literature review actually needs to do. It must build a logical case that there is a specific hole in existing knowledge, and that your project is the precise shape to plug it. Think of it like an escape room. You walk the reviewer through each clue until the one missing step becomes obvious. Then you hold up your project and say that this is the piece. Show what’s known. But spend more time on what’s NOT known. Where did previous work stop? What questions remain open? What assumptions remain still untested? Don’t just nod along with previous work. Challenge methods. Question conclusions. Point out when findings are based on outdated data or narrow samples. The reviewer wants to see your critical thinking. If you’re introducing a new framework, explain exactly what it improves. “This framework accounts for X, which the standard model ignores” is infinitely more persuasive than “this framework offers a new perspective.” And organize thematically (not chronologically). A chronological lit review reads like a timeline. A thematic one reads like a good argument. Arguments win funding. Timelines don’t. ## You need a method that survives contact On September 23, 1999, NASA lost a $327 million spacecraft. The Mars Climate Orbiter had traveled nine months to reach Mars. As it approached, ground control realized something was wrong. The spacecraft came in at 57 kilometers altitude instead of the planned 226\. It burned up in the atmosphere. Talk about burning cash, man. The cause was Lockheed Martin’s software, which produced thrust data in pound-force seconds. NASA’s navigation software expected newton-seconds. One team used imperial. The other used metric. The spec document called for metric. Nobody caught the discrepancy during nine months of flight. Hey, if you ever needed an argument for why the world should switch to the metric system, this is mine. The spacecraft was destroyed not by a bad idea but by a failure of specification. Star Trek totally understood this issue. Every episode where the Enterprise nearly explodes, someone miscalibrated a sensor array or forgot to account for a tachyon variance. Starfleet’s entire dramatic tension runs on specification failures. (At least back in the day when it was good.) Your methodology section is that sensor array. It sits between your research plan and the reviewer’s confidence in your ability to execute. Most methodology sections fail for the same reason the Orbiter failed. They look plausible at a glance. The numbers seem reasonable. But under scrutiny, the specifics don’t hold together. Don’t make that easy mistake. Here’s what needs to be flawless: - **Justify your approach.** Qualitative, quantitative, or mixed? Don’t just state it. Explain why it’s the right choice for THIS exact question. Defend it like you’re explaining your pizza topping choices to someone who believes pineapple is a crime against food. (For the record: pineapple is still valid, friends.) - **Detail your methods with precision.** Surveys? What questions, what scale, what sample size, what distribution method? Interviews? How many, how long, what structure, what coding approach? Lab work? What equipment, what protocols, what controls? - **Name your tools.** Statistical software, analysis platforms, measurement instruments. Specificity signals competence. - **Address ethics.** Informed consent, confidentiality, data storage, IRB approval. This isn’t just a lame checkbox. It’s where reviewers look for red flags. Give them nothing to worry about. - **Anticipate what could go wrong.** Low response rates. Equipment failures. Participant attrition. A contingency plan doesn’t make you look uncertain. It makes you look experienced. Experienced researchers know Murphy’s Law is not a joke. It’s a project management framework for chaotic science realities. ## Your timeline isn’t a wishlist Here’s a number that should change how you think about timelines. A study published in [BMJ Open](https://bmjopen.bmj.com/content/3/5/e002800?ref=lennartnacke.com) (Herbert et al., 2013) found that preparing a single new research proposal takes an average of 38 working days. In one round of Australian medical research funding alone, researchers collectively spent 550 working years preparing 3,727 proposals. Cost: AU$66 million in salary. That’s a lot of dough, Frodo. The most striking finding was that time spent writing was not correlated with whether the grant was funded. More time does not equal more success. Stick that sticky note to your grind machine at work. But a realistic timeline indicates something reviewers actually care about deeply. It is that you’ve actually thought about how this project unfolds in real life. Break the project into distinct phases. Preparation, data collection, analysis, writing, revision. Set milestones for each. Not aspirational milestones. Real ones. (If you’ve ever gone through EU Horizon proposal work package hell, you feel me.) The kind where you’ve accounted for the fact that ethics board approvals take three months or more. The kind where you know participant recruitment always runs slower than planned. Pad your schedule. Not because you’re lazy. Because you’re honest. Equipment breaks. Co-investigators get pulled onto other projects. Holidays exist. People take time off. You know the drill. (You’d be amazed how many proposals schedule critical data collection over December. Nobody is collecting data over December. I will die on this hill. Scheduling fieldwork over Christmas is the research equivalent of the Stark family ignoring every warning about winter. We all know what’s coming. It’s winter, Jon Snow. Plan accordingly.) A tight, realistic timeline tells the reviewer one thing. This person has done this before. They know what a research project actually looks like. ## The reviewer is not your enemy In 1993, FBI star negotiator Chris Voss was on the phone with an armed bank robber at a Chase Manhattan branch in Brooklyn. Two masked men had cracked a security guard across the skull with a .357 revolver and were holding three hostages. Voss didn’t argue. Didn’t moralize. Didn’t try to convince them they were wrong. He kept his cool. He used what he later called *tactical empathy*. He mirrored their words back to them. Labeled their emotions. Spoke in a calm, downward-inflecting tone. He demonstrated that he understood their situation, their fears, and their need for a way out. This is so powerful. He never once told them what to do. All hostages survived. Here’s the connection in case you’ve been wondering. Your grant reviewer is, in a weird way, a hostage, too. Trapped in a room with a stack of demanding proposals. Under time pressure. Often reading outside their narrow specialty. They don’t want to reject you. Gosh, man of us don’t even get paid for this. They want to find proposals worth funding so they can feel good about their time. Your reviewer is basically Tyrion Lannister at his own trial. Exhausted, underpaid for the job, surrounded by people who don’t fully understand what’s in front of them, and just trying to get through the day without making a terrible decision. Write your proposal so Tyrion would fund it. You may be thinking, “But reviewers ARE the gatekeepers. They have the power.” And sure. Fair enough. But power and attention are different things. A reviewer with power and 30 minutes to spend on your proposal is someone you need to serve well. Grant writing expert Robert Porter, whose proposals have won more than $8 million in funding over 30 years, put it bluntly: [“To succeed at grant writing, most researchers need to learn a new set of writing skills.”](https://files.eric.ed.gov/fulltext/EJ902223.pdf?ref=lennartnacke.com) Academic papers inform. Proposals persuade. The muscle is different. Here’s how to serve the reviewer: - Use the language of the funding call. If the call says “innovation,” your proposal says “innovation.” If it says “community impact,” your proposal says “community impact.” Don’t make the reviewer translate terms. - Put the most important information first in every section. Reviewers skim. The first sentence of each paragraph carries ten times the weight of the last one. - Format for exhausted eyes. Shorter paragraphs than a paper. Clear headers. White space. Bullet points for complex information. If your proposal looks like a wall of text, the reviewer’s brain checks out before their eyes do. Anna Clemens, a grant writing specialist, identifies the most common mistake well. Researchers overestimate how much reviewers already know. Different synonyms for the same concept. Subfield shorthand never defined. Key points buried in dense paragraphs. Jargon is a killer. Write for the smartest person who knows nothing about your specific topic. That’s your reviewer. ## Your bibliography is like your reputation This section seems like a formality. It’s not. Your bibliography tells the reviewer three things in under 60 seconds. How deeply you know the field, how current your knowledge is, and whether you’ve done the intellectual work of engaging with the right conversations. Cite works that genuinely inform your research. Not everything you’ve ever read or deem vaguely interesting. This is not the place. Check formatting against the required style guide. APA, Chicago, Harvard, ACM. Get it right. Sloppy citations signal sloppy thinking, even when they don’t. It’s not fair, but it’s the reality. Especially when AI can fix this in seconds. Make sure your references are current. If your most recent citation is from 2019, the reviewer will wonder if you’ve been living under a rock to do your research. Reference your own previous work if it’s relevant. This isn’t vanity. It’s evidence that you have a track record. As John Holmes used to say, if it fits, put it in. Your bibliography is the last thing a reviewer sees. Make it the reason they trust you. ## The one comma that cost $5 million In February 2018, a Maine dairy company called Oakhurst settled a lawsuit for $5 million dollars. Not about their product. Not about their business practices. About a missing comma of all things. A Maine overtime law listed exempt activities: “The canning, processing, preserving, freezing, drying, marketing, storing, packing for shipment or distribution of” perishable foods. No comma after “shipment.” The court couldn’t determine whether “distribution” was a separate activity or part of “packing for.” Judge David Barron opened his 29-page ruling with: “For want of a comma, we have this case.” Five million dollars. One comma. Take that, em-dash haters. If a missing comma can cost $5 million in a legal proceeding where teams of lawyers scrutinize every word, imagine what unclear writing does in a grant review where a panelist gives you 30 minutes (sure sometimes more, but the decision is usually made quickly). Precision is not just a stylistic preference. It’s the mechanism by which your ideas reach another human brain in the way you have intended. Here’s your checklist. Run it before you submit your proposal: - Does your problem statement create genuine tension? - Does your background section frame the research as urgent? - Does your literature review build toward an inevitable gap? - Does your methodology leave zero “but how?” questions? - Is your timeline realistic? - Have you written for the reviewer’s experience? - Does every claim include a specific number, date, or named example? ## The hard split is already happening Ninety-two percent of researchers believe they spend too much time preparing proposals. Only 10 percent believe the current funding system positively affects research quality. And the system isn’t changing. But a divide is forming among the people who operate within it. On one side, we’ve got researchers who treat proposals as paperwork. They fill out the sections, cite the literature, describe the methods. The proposals are complete. They are also interchangeable with thousands of others. For lack of a better word, it’s slop. On the other side, we’ve got researchers who treat proposals as persuasion. They diagnose a problem. They frame the context. They build an argument that makes the reviewer want to fund them before the methodology section even begins. The first group competes on credentials. The second group competes on clarity. Clarity wins. Not every time though. If the Pier study proved anything, it’s that randomness is baked into our system, too. But across ten proposals, across a career, the researcher who writes to persuade will outperform the researcher who writes to comply. Even more so, if they use AI to get their angle right. The NSF funds 27% of proposals. The ERC funds 12%. NIH early-stage investigator success rates dropped from 29.8% to 18.5% in just two years. In Canada, the reality is just as unforgiving. The CIHR Project Grant success rate sits at a bleak 15.3%. The NSERC Discovery Grant success rate dropped from 67% down to 58%. SSHRC Insight Grants hover around a 40% average, but if you want the larger Stream B funding, your odds drop to 38%. Those numbers are not getting better anytime soon. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. Which means the margin between funded and unfunded is getting thinner. And in that thin margin, the quality of your argument is the only variable you fully control. Your ideas might be as good as Stephanie Kwolek’s. Her breakthrough looked like a failure to the gatekeeper standing between her and the test. She had to spend days persuading him just to run it. You have 30 minutes and 15ish pages to do the same thing. Clear problem. Compelling context. Inevitable gap. Bulletproof method. Honest timeline. Reader-first writing. Credible evidence base. Seven easy moves. Start writing today. ## Bonus The [Write Insight](https://lennartnacke.com/your-proposal-didnt-fail-your-writing-did/#/portal/signup/?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=header-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter) subscribers with [an AI Research Stack premium account](https://lennartnacke.com/your-proposal-didnt-fail-your-writing-did/#/portal/signup/?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=header-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter) this week also get 2 print-ready PDF worksheets (a one-page Proposal Checklist and a 7-Move Proposal Builder), 3 AI prompts (audit your problem statement against the tension formula, generate a thematic literature review structure, and extract a reviewer-proof methodology specification), 5 curated resources on grant proposal writing, and a full 7-move proposal protocol checklist. [→ Join them.](https://lennartnacke.com/your-proposal-didnt-fail-your-writing-did/#/portal/signup/?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=header-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter) _This post is for paying subscribers only._ ### How to read like Warren Buffett URL: https://lennartnacke.com/how-to-read-like-warren-buffett/ Last updated: 2026-02-21T14:09:56.000Z The more carefully you read, the less you need to read. Academia doesn’t need more people who read a lot. Stacks of highlighted PDFs, Zotero libraries with 400+ entries, shelf after shelf of books with cracked spines and zero retention. Impressive library. Empty mind. Academia needs people who extract cues from what they read. Cues create frameworks. Frameworks compound into expertise. I’ve supervised over 20 PhD students. All groups show this pattern first. The students who read 200 papers and remember nothing versus the ones who read 40 papers and can reconstruct every argument from memory. But the difference has zero to do with intelligence. It has everything to do with *how* they read. Warren Buffett figured this out decades ago. ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## The man who reads five hours a day Buffett spends roughly 80% of his working day reading. Five to six hours. In his Omaha office. No entourage, no back-to-back meetings, no Slack notifications. Just reading. Deep. When his investment manager Todd Combs started working with him, he heard Buffett tell a class to read 500 pages a day. “That’s how knowledge works,” Buffett told him. “It builds up, like compound interest.” Combs tried. He managed to track pages and eventually reached very high daily counts. Maybe 600 to 700 pages a week at first. A fraction of the target either way. But he kept at it. Here’s what most people miss about Buffett’s reading habit. He doesn’t speed-read. He doesn’t skim for talking points. He doesn’t prompt AI for just the summary. He reads annual reports, probably line by line, cross-referencing claims against financial statements, testing each assertion against what he already knows. To put it simply: He reads less material more carefully than almost anyone alive. Charlie Munger, his partner of 60 years, put it plainly: “In my whole life, I have known no wise people who didn’t read all the time. None. Zero.” *All the time.* Continuous, deliberate, active reading. The kind that changes how you think, sentence by sentence. ## You stopped learning to read in sixth grade James Mursell documented this in a 1939 *Atlantic Monthly* piece called “[The Defeat of the Schools](https://www.theatlantic.com/past/docs/issues/95dec/chilearn/murde.htm?ref=lennartnacke.com).” Reading skill improves steadily through elementary school, then flatlines. The average high school graduate can follow simple fiction. Put that same person in front of a tightly argued exposition, a carefully structured research paper, or a passage that demands critical evaluation, and they shilly-shally. > *“Up to the fifth or sixth grade, reading, on the whole, is effectively taught and well learned. To that level we find a steady and general improvement, but beyond it the curves flatten out to a dead level.” — James L. Mursell* Mortimer Adler and Charles Van Doren formalized this observation into a hierarchy in *How to Read a Book* (1972). Four levels, each building on the last. > How to read a book [pic.twitter.com/AKFTAKkvpO](https://t.co/AKFTAKkvpO?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [July 6, 2024](https://twitter.com/acagamic/status/1809603226939085066?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) **Elementary reading.** Basic literacy. Decoding words. Following sentences. You learned this by age ten. **Inspectional reading.** Systematic preview. You scan the title, table of contents, preface, index. You flip through pages. You spend 15 minutes deciding whether this book deserves 15 hours. Most academics skip this level entirely, then complain about wasting time on papers that weren’t relevant. **Analytical reading.** Full comprehension of a single work. You classify the book’s type, summarise its core argument in one sentence, map its logical structure (which rarely matches chapter structure), and identify the problem the author set out to solve. Then you evaluate: is it true? So what? **Syntopical reading.** Reading multiple books on one topic to build understanding that transcends any single author. You create your own terminology, define the central questions, map where authors agree and disagree, and construct independent conclusions. ![A whimsical illustration outlining four levels of reading skills, emphasizing progression from basic literacy to advanced understanding.](https://lennartnacke.com/content/images/2026/02/image.png) How to create literacy. Most people operate at level one for their entire careers. They read words. They accumulate information. They never extract understanding. This is the sixth-grade plateau, and most PhDs never leave it. To achieve mastery, you must. ## Skim before you commit Francis Bacon wrote in the 17th century: *“Some books are to be tasted, others to be swallowed, and some few to be chewed and digested.”* Now, I know that Bacon quote sounds like a party that Jabba the Hut would enjoy, but it’s a true sentiment when it comes to how you should approach a book. You have to begin with inspectional reading. Inspectional reading answers one question: **Does this deserve my full attention?** Fifteen minutes. Scan the title. Read the abstract. Check the table of contents or section headers. Flip to the references. Sample a few pages from the middle. Assess writing quality, argument density, and relevance to your actual question. This single habit eliminates 60% of reading that wastes your time. I tell every new PhD student the same thing. Before you read a paper for two hours, spend 10 minutes deciding whether it earns those two hours. Most don’t. ## Do a messy first read This one violates every instinct careful readers have. You encounter a difficult passage. You stop. You reread. You look up unfamiliar terms. You spend 20 minutes on a single paragraph. I know I used to do that a lot. Adler and Van Doren urge us to stop doing that. On your first pass, read the entire work without stopping. The same way you would eat that Whopper when you’re a starving son of a gun. Cover to cover. Don’t pause for confusion. Don’t annotate. Don’t look anything up. Just read. This feels wrong. It feels lazy. But it works. Trying to deeply understand a book before you have the full picture is like getting caught up in individual brush strokes before stepping back to see the whole painting. You need the whole composition first. The first pass reveals structure, identifies where the real arguments live, and shows how the pieces connect. Your second reading becomes radically more efficient because you now know where to slow down and where to move fast. I usually audiobook my way through this messy first part. Then, I get down and dirty with the text. [*If you want a one-page triage system that cuts 60% of irrelevant reading, subscribe to premium.*](https://lennartnacke.com/#/portal/signup) ## Reverse-engineer the argument Analytical reading asks four questions in sequence. 1. *What kind of book is this?* Practical or theoretical? If practical, what action does it prescribe? If theoretical, what kind of theory? Classification determines your evaluation standards. 2. *What is the core argument in one sentence?* If you can’t produce this, you haven’t understood the work. Full stop. Most readers can summarise individual chapters but fail at whole-book synthesis. That gap reveals the difference between tracking details and grasping structure. 3. *What is the logical architecture?* Map the major parts and how they relate. This structure almost never matches chapter divisions. Authors organise for readability. You need to reconstruct the argument’s skeleton. 4. *What problem does the author solve?* Every book worth reading addresses specific questions. Know the questions before evaluating the answers. After these four steps, answer two more. Is it true? Does the evidence hold, do the conclusions follow, are the assumptions stated? And then the big one that every academic just loves so much: So what? Or as Snoop Dogg would say: “Why give a fuuuuu…?” If the argument is true, what does it change? [This is the system I use to build grant-winning literature reviews. I packaged it into a one-page execution template for premium subscribers.](https://lennartnacke.com/#/portal/signup) ## Read beyond the books Syntopical reading is how actual expertise forms. You read five to 15 sources on the same question. You create your own vocabulary to compare ideas across authors. Different writers use different terms for the same concept, or identical terms for different concepts. You impose neutral language so you can actually compare. You identify where authors agree. Where they disagree. More importantly: *Why* they disagree. Different definitions? Different evidence? Different values? Different logic? Then you form your own conclusions. At this level, you stop being a reader. You become a thinker who uses other people’s work as raw material. Any AI can synthesize this raw material. Your unique spin on it with your life experiences — your lens if you will — is what makes your take stand out. This is how I built the literature reviews for every major grant I’ve written. I identified the different opinions across dozens of papers, mapped the disagreements, and used those gaps to position my own contribution. That approach helped me secure over $3 million in competitive funding across the Canadian granting landscape NSERC, SSHRC, CIHR, and CFI (I know if you’re in the US, your grants are the size of Texas, but your students also cost way more, so put things into perspective for once, Elon). The reading method was the engine underneath. ## Push through the noise to find the signal In Games User Research, we face a basic problem. Players generate massive amounts of data during gameplay. Weapons, clicks, paths, states, waypoints, physiological stuff even if you do things in a lab. A continuous stream. Most of it is noise. The researcher’s job is extracting signal from that noise. This requires reading data at multiple timescales. Millisecond-level data captures reflexes. Second-level patterns reveal decisions. Minute-level trends show engagement. Session-level analysis exposes learning curves. Analyze only one level and you get incomplete, often misleading conclusions. Reading functions identically. Books generate a continuous stream of words, sentences, paragraphs, chapters. And our daily AI consumption pretty much does the same thing. Most readers sample at one resolution only. They read words in order, accumulating information linearly. They never extract signal at other timescales. They don’t establish a baseline understanding before getting into details. They don’t identify which sections contain the real arguments versus supporting filler. They don’t track idea development across chapters. They don’t aggregate book-level understanding to compare across sources. Maximum data processed, minimum signal extracted. Exactly like a researcher who records hours of behavioural measurements but never runs the analysis. Hierarchical reading lets you extract signal at every resolution, exactly the way good research extracts signal from noisy data. ## The counterexample (and why it doesn’t break the model) You might think: “I read casually for pleasure. Not everything needs four levels of analysis.” Fair. Adler and Van Doren would agree. Inspectional reading exists precisely to sort books into categories. Some deserve analytical reading. Most don’t. Reading a novel on the beach doesn’t need a structural outline. Reading a paper that might redirect your research programme does. So, the choice is still yours. We are not taking that away here. The real glitch is reading *everything* the same way. Word by word, front to back, no preview, no structural mapping, no cross-source synthesis. One speed, one depth, regardless of what the material demands or what you need from it. Variable-speed reading matched to purpose. That’s the skill Buffett has and most people don’t. ## Get on the couch and try it in the next 24 hours Pick up a book or a substantial paper you’ve been meaning to read. Before you open to page one, spend exactly 15 minutes on inspectional reading. Set a timer. Title, table of contents, preface, index, random page samples. Then answer three questions *in writing*: 1. What kind of work is this? Practical or theoretical? 2. What problem does the author appear to solve? 3. Does this deserve my full analytical attention, or is a superficial pass enough? Write the answers down. Not in your head. On paper or screen. If you can’t answer confidently after 15 minutes of inspection, you’ve just discovered how much time you’ve been wasting on books you hadn’t properly evaluated. [*If you want to operationalize this reading stack immediately, the full execution system is in the premium tier.*](https://lennartnacke.com/#/portal/signup) ## The more carefully you read, the less you need to read This is the lesson Buffett’s career taught in public, over decades. His edge was never access to information. Every investor reads the same annual reports. He reads fewer things more carefully and retains the structure of every argument he encounters. Reading 50 books with these methods produces more understanding than reading 200 passively. Each book connects to frameworks you’ve already built. Gaps show themselves. Relate to big problems. Questions form. You stop collecting bricks and start building houses, even cities. Two researchers read 500 papers over 10 years. One extracts signal at every level, builds conceptual frameworks, synthesizes across domains. The other collects impressions and fragments. Same volume. Radically different impact. Active readers let others talk first. They inspect before they commit. (Turns out they are often good active listeners, too.) They reconstruct arguments before they judge. They build their own vocabulary across sources. They write down what they understood, because writing is the only honest test of how well you get it. Writing. Not generating text with the latest AI. Active readers build the architecture. Everyone else fills warehouses. Reading carefully is a quiet act. It doesn’t announce itself on social media. It doesn’t produce visible output until the moment it does, and then it compounds in ways that look, from the outside, like genius. It was never genius. It was always the reading. Most PhDs plateau at sixth-grade reading. Premium members train beyond that plateau with structured execution. **Further reading:** *Adler, M. J., & Van Doren, C. (1972). How to read a book. Simon and Schuster.* ## Get The Write Insight Turn deep expertise into scalable leverage. Join 14k+ experts Email sent! Check your inbox to complete your signup. No spam. No fluff. Unsubscribe anytime. ## Bonus Materials _This post is for paying subscribers only._ ### Reviewer 2 Can't Touch a Paper Structured Like This URL: https://lennartnacke.com/reviewer-2-cant-touch-a-paper-structured-like-this/ Last updated: 2026-03-26T00:05:08.000Z You have the PhD. The publications. The grant money. You’ve read hundreds of papers in your field. And yet. Sitting down to write still feels like dragging yourself through wet concrete. You open the document. You stare at the blank page. Your brain, the same brain that designed a study and ran a lab meeting this morning, locks up. Two hours later, you’ve produced a paragraph you’ll probably delete tomorrow. You know this feeling. The Sunday afternoon dread when you promised yourself you’d finish that discussion section sooner. The guilt when a co-author emails asking for your draft and you haven’t even started. The quiet panic at 11 PM the night before a deadline, realizing you’ve rewritten the same introduction four times and none of them feel like they work. You start wondering if you’ve lost the ability to write altogether. Maybe you never had it. Meanwhile, some colleague who seems to have a secret portal to a parallel dimension where time moves slower just submitted three papers this semester. Same deadlines. Same teaching load. Same committee obligations. Not going to lie, I had a moment like that in the early 2020s when Juho Hamari outpublished me as the most published researcher in the field of gamification. I felt like I was already operating at the peak of my capabilities, but I watched his research team take over the field with dozens of journal publications that outcited my work. Well done. In hindsight, I shouldn’t have compared myself to them (I was in a different situation with a very different teaching load), but it is often unavoidable when you feel like their CVs keep growing while yours sits stuck at *in progress*. You assumed they had more discipline. More talent. More protected writing time (this is the academic equivalent of a unicorn sighting; you’ve heard legends, but you’ve never actually seen one in the wild). They don’t. They have a system. That word matters. The reality is this. You’re not bad at writing. You’re bad at *how* you write. Let that sink in for a moment. It’s a tough pill to swallow. Writing productivity runs on systems. Most academics have never built one. They treat writing like a single task, one giant cognitive monolith you power through with enough discipline and so much coffee your hands are jittery. Writing is actually six different cognitive processes crammed into one activity: 1. Deciding what to say (**content**). Choosing whether to include a specific study in your literature review, or deciding which of your three experiments to present first. 2. Figuring out how to say it (**language**). Struggling between “participants demonstrated improved performance” vs. “performance improved” vs. “we observed performance gains.” 3. Remembering who you’re saying it to (**audience**). Deciding whether to define “gamification” for a general social science journal or assume readers know the term in a specialized HCI venue. 4. Tracking what you’ve already said (**coherence**). Realizing mid-paragraph that you already mentioned this limitation two pages ago, or checking if you’ve already cited Nacke et al. (2023) earlier. 5. Planning what comes next (**structure**). Figuring out whether your next paragraph should address the second research question or first discuss the implications of your current finding. 6. Judging whether any of it is good (**evaluation**). Stopping mid-sentence to wonder “Is this too obvious?” or “Does this sound too informal?” or “Should I delete this entire section?” The problem is doing all six simultaneously while drafting. You’re running Chrome with 59 tabs open and wondering why your laptop sounds like a jet engine. Most people think writing is hard because they don’t have enough willpower. But the real problem is that writing asks your brain to do too many things at the same time. I’ve supervised 30+ PhDs. Reviewed hundreds of papers. Written grants that got funded and grants that got rejected. The pattern is always the same. Researchers who struggle with writing aren’t lazy. They’re running their brain’s software on the wrong operating system. Here’s the shell script that will fix it for you: A complete system for academic writing that works *with* your brain’s architecture. Six protocols. Each one targets a specific cognitive bottleneck. Each one removes a decision you don’t need to make. I just spent a full week writing this massive guide you are reading on how to write better research papers. I'm not going to lie, the irony of agonizing over a piece about writing faster was not lost on me. So, this will be a longer read. All I ask is that you try one protocol with your next paper. Then try another. Let’s get this show on the road. > [AI Research Stack members this week get: a paper-writing Notion dashboard with the complete Inverted Assembly Line workflow, all AI revision prompts, and a pre-submission checklist. Join them.](https://lennartnacke.com/#/portal/signup) ## 1\. Start with what you know, end with what you’re promising Here’s how most junior researchers write a paper. They open a blank document and start with the title, then the introduction, then trudge linearly toward the conclusion. This is like trying to write the movie trailer before you’ve filmed the movie. This is cognitively backwards. The introduction requires the *highest* cognitive load of any section. You’re framing the problem, positioning your contribution, making promises to the reader, all before you’ve fully articulated what you found. You’re asking your brain to do the hardest work first, with the least material to work with. This is like Tolkien trying to write the Scouring of the Shire before he’s even gotten the hobbits out of the Green Dragon. Your brain doesn’t have the material yet. Resequence your writing to match cognitive load: ![Whiteboard diagram comparing two paper structures: left shows linear "Standard Way" (Intro→Methods→Results→Discussion), right shows "Inverted Assembly Line" with six steps from Figures/Tables to Abstract.](https://lennartnacke.com/content/images/2026/02/Whiteboard-AssemblyLine.webp) A sketch of the inverted assembly line. 1. **Figures & Tables.** Visual thinking. Low language load. You know your data, so just arrange it. 2. **Methods.** Procedural recall. You did this, so describe it like a recipe. 3. **Results.** Descriptive reporting. Narrate your figures. No interpretation yet. 4. **Discussion.** Interpretive thinking. Connect your results to existing findings in the field. 5. **Introduction.** Rhetorical framing. Now you know what you’re actually promising. Focus on the contribution. 6. **Abstract & Title.** Compression and marketing. Requires knowing the whole. Each step uses material generated by previous steps. You’re writing so that you will gain momentum. You’re riding a bike downhill instead of pushing it uphill with a flat tire. Think of it like building a LEGO Death Star. You don’t start with the intimidating final form on the box cover. You start with bag 1, the small structural pieces you can actually see and touch. Imagine you’re writing a paper on how gamification affects student motivation, because that’s a topic that was once super hot (bear with me, fellow non-techies). 1. You start with **Figures & Tables**. You create a bar chart showing motivation scores across conditions. Now you can *see* your main effect. 2. You move to **Methods**. You describe your 123 participants, your gamification intervention, your validated motivation scale. Easy. You did this work. You’re just documenting it here. 3. You write **Results**. You narrate your figures: “Participants in the gamified condition reported significantly higher intrinsic motivation (M = 4.2, SD = 0.8) than in the control condition (M = 3.1, SD = 0.9), t(245) = 3.42, p < .001.” The figure tells you what to say. 4. You draft the **Discussion**. Now you’re interpreting it all. “These results align with Self-Determination Theory, suggesting that well-designed gamification satisfies competence needs.” Your results section hands you the claims to explain, you just have to find the citations next. 5. Finally, you write the **Introduction**. Now you know exactly what promise you’re making: “This study demonstrates that gamification can increase intrinsic motivation when designed around competence feedback.” You’re not guessing what the paper will argue. You already know. 6. The **Abstract** writes itself. You compress what you’ve already written into 150 words. At CHI, it’s a simple formula that I’ll explain below. Notice what happened here. Each section gave you the raw material for the next. You never faced a blank page asking, “What should I say?” The previous section already answered that question. And to make it easier for premium subscribers to [**The AI Research Stack**](https://lennartnacke.com/#/portal/signup), I have included a Worksheet in this week's Notion drop. Start with what you know (methods, data). End with what requires synthesis (framing, positioning). Never write the introduction first. Save that delicious bit when you have your data all mapped out into a story. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a premium subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 2\. Write badly first → edit later Your brain has two modes that are neurologically incompatible. Generative mode and evaluative mode. This distinction comes from [Flower and Hayes’ Cognitive Process Theory of Writing](https://doi.org/10.58680/ccc198115885?ref=lennartnacke.com), the most cited model of how writers actually think. Their research, based on think-aloud protocols with experienced writers, revealed that writing involves three recursive processes: planning (generating ideas), translating (turning ideas into text), and reviewing (evaluating and revising). The critical insight was that these processes compete for the same cognitive resources. 1. **Generative mode** (what Flower and Hayes call “translating” combined with idea generation) is expansive, associative, fast. Your brain retrieves information from long-term memory, makes loose connections, follows tangents. Working memory stays open. You’re exploring possibility space without filtering. 2. **Evaluative mode** (what they call “reviewing”) is contractive, critical, slow. Your brain compares output against internal standards, checks for coherence, judges quality. Working memory narrows. You’re applying constraints and making decisions. The problem is that both modes require working memory, and working memory is severely limited. Trying to generate and evaluate simultaneously creates what cognitive scientists call *task-switching costs*. Each switch burns mental energy and breaks flow. Years ago, I worked on a paper where I spent three hours on an introduction paragraph, and I think I wrote about nine different versions of it. I deleted most of them, patched some parts of them back together, and then the final version was eerily close to the first version. But I felt like I had burned an entire morning judging each sentence before I gave them a bit of room to breathe. Most people try to write in both modes simultaneously. They generate a sentence, then immediately evaluate it, then edit it, then generate the next one. This is like trying to drive with one foot on the gas and one on the brake. Create a **Zero Draft** using this strict protocol: 1. Open a distraction-free writing tool (I like [iA Writer](https://ia.net/writer?ref=lennartnacke.com) and [Bear](https://bear.app/?ref=lennartnacke.com), or turn off your monitor). 2. Set a timer for 25–45 minutes. 3. Write as fast as you possibly can without stopping. 4. Do not fix typos. Do not check references. Do not re-read. 5. Use placeholders liberally: \[REF\], \[STATS\], \[TRANSITION\], \[FIX THIS\]. You’re shovelling sand into a box so you can build castles later. You cannot shape sand while quarrying it. Your inner critic will scream. Let it scream. It gets its turn later. (Think of it like putting Reviewer 2 in timeout while you build the sandcastle.) ![Banner titled "The Zero Draft Protocol" with five illustrated steps: distraction-free, set timer, write fast, no fixing, and use placeholders for editing.](https://lennartnacke.com/content/images/2026/02/Zero-Draft-Protocol.webp) The Zero Draft Protocol The Zero Draft gives you raw material. Raw material is infinitely easier to edit than a blank page. Even the Chicago Bulls needed to show up to practice before Phil Jackson could turn them into champions. ## 3\. Let templates make the structural decisions for you Every decision you make while writing burns mental fuel. What should this paragraph do? How should I structure this section? What comes next? Most decisions about structure can be offloaded to proven templates that do the cognitive work for you. The core problem is decision fatigue. Every structural choice consumes limited cognitive resources. When you’re constantly making these micro-decisions while writing, you’re burning mental energy that could be spent on the actual intellectual work. Templates solve this by turning structural choices into a fill-in-the-blanks process, so you can analyze data, develop your argument, and articulate your insights. Instead of reinventing how to organize an abstract or discussion section each time, you follow a proven pattern to free up working memory for the substantive thinking that actually matters. These frameworks turn the art of writing into an assembly-line process. You focus on the data. The structure does the thinking for you. Seven templates follow. One for each major component of a paper. (Stick with me. This is where the real time savings happen.) ### 3.1 The CHI abstract formula (Context → Problem → Solution → Findings → Contribution) At CHI, abstracts follow a simple formula. Five slots. Fill them in order. 1. **Context (1–2 sentences).** Establish the lay of the land. Orient the reader in the general research area. 2. **Problem (1 sentence).** Establish the niche. State what is missing, unknown, or broken in the current state of the art. 3. **Solution (1–2 sentences).** Occupy the niche. Explain your method and how you addressed the problem by building, observing, or studying something. 4. **Key Findings (2–3 sentences).** Provide the evidence and specific outcomes. This is usually the meat of your abstract (unless you’re writing a method paper). Quantify where possible. 5. **Contribution/Impact (1 sentence).** The “So what?” Tie your findings to the broader HCI community. Don’t reinvent the wheel. Just fill the slots. The abstract writes itself. **Example (the meat in this one is the solution/method)**: > **(Context)* Biofeedback provides a unique opportunity to intensify tabletop gameplay. It permits new play styles through digital integration while keeping the tactile appeal of physical components. *(Problem)* However, integrating biofeedback systems, like heart rate (HR), into game design needs to be better understood in the literature and still needs to be explored in practice. *(Solution)* To bridge this gap, we employed a Research through Design (RtD) approach. This included (1) gathering insights from enthusiast board game designers (*n *\= 10), (2) conducting two participatory design workshops (*n *\= 20), (3) prototyping game mechanics with experts (*n *\= 5), and (4) developing the game prototype artifact* One Pulse: Treasure Hunter’s*. *(Key Findings)* We identify practical design implementation for incorporating biofeedback, particularly related to heart rate, into tabletop games. *(Contribution/Impact)* Thus, we contribute to the field by presenting design trade-offs for incorporating HR into board games, offering valuable insights for HCI researchers and game designers.* Source: Tu, J., Kukshinov, E., Hadi Mogavi, R., Wang, D. M., & Nacke, L. E. (2025). Designing biofeedback board games: The impact of heart rate on player experience. In *Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems* (Article 466). ACM. [https://doi.org/10.1145/3706598.3713543](https://doi.org/10.1145/3706598.3713543?ref=lennartnacke.com) ### 3.2 The CARS introduction formula (Territory → Problem → Contribution) [CARS](https://archive.org/details/genreanalysiseng0000swal) stands for “Creating a Research Space.” [John Swales developed it through genre analysis](https://archive.org/details/genreanalysiseng0000swal) of research article introductions across disciplines. He studied what successful papers actually do, then named the pattern so writers could see it and reuse it. The core insight is descriptive. He found out how published introductions already work. The model frames an introduction as three rhetorical moves that funnel from broad context to your specific niche. ![Three-step research model: establish a territory (why it matters), define a niche (problem to address), and occupy the niche (proposed solution and method).](https://lennartnacke.com/content/images/2026/02/CARS-model.webp) The CARS (Creating a Research Space) model. CARS gives you a checklist that forces logic: Importance, gap to problem, your response. That structure makes it easier for reviewers to see your contribution fast, because they can locate the problem and the payoff without hunting. It stops vague introductions that summarize a topic without earning the reader’s attention first. **Move 1 (Establish Territory).** Ask: Why should anyone care? - *Template:* “Recent developments in \[Field\] have heightened the need for \[Topic\].” **Move 2 (Establish Niche).** Problematize the current state. - *Template:* “Previous studies have failed to address \[Problem\]…” or “Little is known about \[Variable\]…” **Move 3 (Occupy Niche).** State your specific solution. - *Template:* “The purpose of this study is to address this problem by \[Method/Action\].” Territory → Niche → Occupation. That’s the algorithm. Don’t freestyle your introduction like you’re in a jazz ensemble. Use these three moves to organize what goes in each paragraph. Draft one or two sentences per move first, then expand with evidence and citations where they strengthen the niche claim. If your niche feels weak (“no one has studied X”), tighten the scope first. By that I mean narrow the population, method, setting, measure, or time window until the problem becomes specific and defensible. **Example** (from [Tu et al., 2025](https://doi.org/10.1145/3706598.3713543?ref=lennartnacke.com)): > **(Move 1 — Territory)* Board games have begun incorporating digital elements, often called “hybrid digital games,” combining non-digital and digital components to introduce new game experiences. These hybrid board games often use companion applications on tablets or phones to manage tasks like storytelling, automated actions, or progress tracking \[*[*53*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0053)*\]. Alternatively, so-called* affective games *use players’ physiological signals, or biofeedback, to directly influence the experience \[*[*75*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0075)*\]. \[…\] Digitally incorporated biofeedback, such as heart rate (HR), has been studied in Human-Computer Interaction as a tool to understand and improve the player experience in games \[*[*23*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0023)*,* [*45*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0045)*,* [*67*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0067)*,* [*126*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0126)*\].* > **(Move 2 — Niche)* However, the integration of HR into board games, particularly in multiplayer contexts, remains largely unexplored. While HR mechanics have been studied in digital single-player settings, there is a striking lack of established methods or frameworks for embedding biometric data into analog multiplayer experiences. From a design perspective, this challenge encompasses not only the task of “getting the design right” but “making the right design” \[*[*18*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0018)*\]. \[…\]* > **(Move 3 — Occupy)* To understand HR integration in board games, we employ a Research through Design process \[*[*133*](https://dl.acm.org/doi/10.1145/3706598.3713543?ref=lennartnacke.com#Bib0133)*\] because it allows for iterative user testing, the creation of tangible prototypes, and the generation of reusable design assets \[…\] We implemented three key research phases: (1) gather preliminary insights from enthusiast board game designers, (2) participatory design workshops, and (3) prototyping game mechanics with board game experts using a game prototype artifact called* One Pulse: Treasure Hunter’s*. \[…\]* [**AI Research Stack premium members**](https://lennartnacke.com/#/portal/signup) get a printable one-page CARS template this week that walks you through all three moves with fill-in-the-blank prompts. It’s the reference sheet I keep next to my keyboard when drafting introductions. ### 3.2.1 Alternative problem-first introduction structure ![Solid circle labeled "Research Problems" with arrow pointing to a dashed circle labeled "Research Gaps," implying problem areas becoming identified gaps.](https://lennartnacke.com/content/images/2026/02/RP-RG-1.webp) Research gaps are not research problems. CARS works. But some papers need a different rhythm. They need to extend the idea behind CARS. Here’s an alternative that builds on CARS, but works especially well when your research problem is urgent or your field context is unfamiliar to readers. Remember that a research problem is not the same as a research gap. - A **research problem** answers the “So What?” question. It’s a real-world pain, a condition that causes instability, cost, or harm. If you don’t solve this, people get sick or stay sick. Systems break. Money gets lost. Real problems transcend academic papers. They exist in the world, whether or not anyone writes about them. - A **research gap** is different. It’s a void in the literature that blocks us from solving the problem right now. You find gaps in papers, usually buried in limitations or future research sections. Researchers write them down. Gaps are concrete and easier to access than the larger problem they relate to. Thus, here’s where most people go wrong. They read the literature, summarize what’s been studied, list what hasn’t been studied, and call that a research gap. That’s not a research problem. You are merely listing unstudied variables. Nobody cares about unstudied variables unless you connect them to a real pain. The connection between problem and gap is where you excel as a researcher. In practice, you often work backwards. You find the gap first (because gaps are easier to spot in papers), then you work toward the problem it connects to. You ask: “Why does this gap matter? What real-world pain does it leave unsolved?” In the paper, you present it the other way around. You start with the problem (the pain out there), then you show the gap (why this problem remains unsolved), then you present your solution (the bridge). This is the problem-first structure. Each part does rhetorical work: - **The problem** makes them care. - **The gap** proves you’re the expert who has read the literature. - **The solution** shows you can actually fix this. **Step 1: Start with the topic or problem.** Lead with the issue itself. No throat-clearing about the field. Drop the reader directly into what’s broken, missing, or unresolved. **Step 2: Argue for the importance of the field.** Now zoom out. Why does this domain matter? What’s at stake if we don’t solve this problem? This earns the reader’s attention before you ask them to follow your research journey. **Step 3: State your research questions, purpose, or hypothesis.** Be explicit. “This study investigates whether…” or “We hypothesize that…” or “The objectives of this research are to…” Readers need a clear destination. **Step 4: Summarize the research gap.** Connect the gap directly to the problem you opened with. The gap justifies the research. If readers don’t see why existing work fails to solve the problem, they won’t see why your study matters. **Step 5: Intersperse relevant background.** Weave in social, political, historical, or scientific context that situates your work. This contextual grounding prepares readers for the deeper literature dive that follows. This structure works well when your audience needs persuading before they’ll invest in your literature review. CARS often implicitly assumes readers already care about the territory. The problem-first structure *makes* them care by leading with the stakes. ### 3.2.2 Theoretical framework and literature review Once your introduction lands, you have two paths: (1) Present a theoretical framework, or (2) move directly into a literature review. Sometimes you do both and combine these sections. **Theoretical framework (optional section or subsection):** A theoretical framework section does three things in sequence: 1. **Discuss theories relevant to your article.** Survey the theoretical landscape. What lenses could someone use to understand this phenomenon? 2. **Introduce the specific theories you used.** Narrow from survey to selection. Why these theories and not others? 3. **Articulate your theoretical contributions.** What does your study add to or challenge about these theories? This section can stand alone or fold into your introduction. Use it when your contribution is partly theoretical, when your method depends on a theoretical lens (like grounded theory or phenomenology), or when your audience might not share your theoretical assumptions. **Literature Review/Related Work (often a separate section):** The introduction motivates. The literature review situates. Different jobs, which is why I like to write them as different sections (which works in my field, human-computer interaction, but in fields like psychology, this is often not done this way). A literature review provides an overview of the research areas that contribute knowledge to your study. Structure it to cover: 1. **Historical and contextual analysis.** How did this research area develop? What were the turning points? 2. **Key concepts and definitions.** What terms do readers need to understand your study? Define them here, not buried in your methods. 3. **Critical debates and frictions.** Where do researchers disagree? These frictions often point to the gaps your study addresses. 4. **Comparison of key studies.** Don’t just list papers. Compare them. What did Study A find that Study B contradicted? What methods did each use? Systematic comparison shows you’ve done the work. 5. **Gaps in existing research.** Make the literature gaps explicit. “No study has examined X in the context of Y.” “Existing work assumes Z, but this assumption has not been tested.” 6. **How your study addresses these gaps.** End the literature review by circling back to your contribution. The reader should finish this section understanding exactly how your research fits into, and advances, the existing conversation. Only then do you move to methods. At this point, readers understand what you did (introduction), why it matters theoretically (framework), and how it relates to everything else (literature review). Now they’re ready to learn *how* you did it. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a premium subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 3.3 Why listing what you did isn’t enough A methods section that can’t be attacked isn’t doing its job. A bare list of methods is like telling someone you ‘made dinner’ without mentioning you microwaved a *Hot Pocket*. Technically true. Yummy to some. Completely useless to your wife, who likes salads. “We recruited 30 participants. We used a Likert scale. We ran a t-test.” That’s a list. And lists fail the most basic test of scientific writing: Could a skeptical reader disprove your claim? This is Karl Popper’s falsifiability criterion at work. In [*The Logic of Scientific Discovery*](https://books.google.com/books/about/The%5FLogic%5Fof%5FScientific%5FDiscovery.html?id=Yq6xeupNStMC&ref=lennartnacke.com), Popper argued that science progresses by proposing claims that *could* be proven wrong, then subjecting them to rigorous tests. A claim you can’t test isn’t science. Your methods section exists to show readers exactly how they could replicate your study and (if your findings are wrong) debunk your point. A bare list fails this test because it hides the reasoning. Why *that* sample size? Why *that* measure? Why *that* analysis? Without answers, a skeptical reader (most often that’s Reviewer 2) can’t evaluate whether your choices were defensible. The list looks like science but doesn’t function like science. ### 3.3.1 What a methods section actually covers Before we get to structure, let’s be clear about content. A complete methods section addresses four areas: 1. **Materials.** Equipment, participants, samples, stimuli, software, apparatus. Everything you used. Readers need to know what they would need to replicate your study. 2. **Procedures.** Study preparation, protocols, data collection. This is the recipe: what you did, in what order, under what conditions. If you ran interviews, how long were they? If you used surveys, when and how did participants complete them? If you observed behaviour, what was the setting? Mixed methods studies describe each approach. 3. **Analysis.** Statistical tests (for quantitative work) or analytical frameworks (for qualitative work). Which tests did you run and why? Which coding approach did you use? Thematic analysis, grounded theory, content analysis? Name the approach and justify the choice. 4. **Methodological issues.** Problems encountered during execution. Equipment failures, participant attrition, unexpected confounds, deviations from protocol. Don’t bury these. Acknowledging issues builds credibility. Hiding them invites rejection. Each of these four areas needs the same rhetorical treatment. You don’t just list what you did. You explain *why* you did it that way. And the way you explain it is identical across all four areas. ### 3.3.2 The three-move loop for each methodological choice The fix is a three-move loop: **Contextualize → Describe → Justify**. Run this loop for every major choice in your methods section. That means you execute the loop when you describe your **materials** (why these participants? why this equipment?). You execute it again for your **procedures** (why this protocol? why this order?). You execute it again for your **analysis** (why this statistical test? why this coding framework?). And you execute it again for any **methodological issues** (why did you deviate from the original plan? why does that deviation not invalidate the results?). Four areas. One loop per area. Here’s what the loop looks like: ![A colorful flowchart titled "The Methodological Choice Loop" outlining three steps: contextualize background, describe actions and materials, justify and validate decisions.](https://lennartnacke.com/content/images/2026/02/Method-Choice-Loop-w.webp) The Methdological Choice Loop **Example** (from [Tu et al., 2025](https://doi.org/10.1145/3706598.3713543?ref=lennartnacke.com), who described this out of sequence): > **(Contextualize)* The first phase of this study (RP1) focuses on gathering initial data on attitudes towards digital integration from board game designers (*n *\= 10). These participants are primarily analog board game designers, known for their frequent enjoyment of traditional, non-digital board games. \[…\]* > **(Describe)* We conducted interviews both offline and online to accommodate remote participants. Interview questions explored how integrating physiological indicators, interactive displays, and accurate measurement technologies can enhance immersion, social interaction, and strategic complexity in social deception board games. The focus of the interviews was on attitudes toward designing mechanics that use HR data, shared or individual displays, and spatial dynamics to heighten tension, deception, and player engagement. A semi-structured interview (average 28:54 minutes) allowed us to collect new exploratory data relevant to our design philosophy.* > **(Justify)* We deliberately chose this demographic for the first phase to gain insights into the perception of digital integration from the perspective of players who primarily engage with non-digital games. \[…\]* [AI Research Stack premium members](https://lennartnacke.com/#/portal/signup) get the Contextualize → Describe → Justify cheat sheet as a printable PDF. One page. Four methods areas. The loop printed clearly so you can run it without hunting through this article. ### 3.4 Walk readers through your data one finding at a time Results sections fail when they become data dumps. (Sorry for any hardcore psychologist in the room, I know some of you are still purists.) Table after table, statistic after statistic, with no orienting thread. Readers lose track of which hypothesis each analysis addresses. Reviewers skim, miss the connection between your numbers and your claims, and remain unclear how this supports the argument. The problem is cognitive. By the time readers reach your results, they’ve processed your introduction, literature review, and methods. Working memory is nearly full. Now you’re asking them to absorb dense statistical output *and* remember which research question each test addresses *and* translate numbers into meaning. That’s too many demands at once. Make it easy for them instead. ### 3.4.1 What a results section actually covers Before we get to structure, let’s be clear about content. A results section is pure description. You report what you found. No interpretation yet. That comes in the discussion. Within that constraint, results sections typically cover four content areas: 1. **Significant findings in plain words.** Open with the headline. State your main results in accessible language before diving into statistics. Readers need orientation before detail. 2. **Adjustments and exclusions.** Data rarely arrives clean. Report any exclusions (outliers removed, participants dropped, missing data handled) and adjustments (transformations, recoding, combining variables). This belongs early because it affects how readers interpret everything that follows. 3. **Qualitative results (if applicable).** Participant observations, interview excerpts, thematic patterns. These provide texture and evidence for your interpretive claims. Select only the participant quotes that do rhetorical work. 4. **Quantitative results with tables and figures.** Descriptive statistics (means, standard deviations, frequencies) and inferential statistics (tests, effect sizes, confidence intervals). Tables and figures carry the heavy lifting here. **One critical rule:** Never duplicate information across tables and figures. A table shows exact numbers. A figure shows patterns, trends, or relationships. If you have both, they should complement each other. A bar chart that simply visualizes the same numbers already in a table wastes space and insults the reviewers (also can get you desk-rejected). Results sections are figure-heavy by design. Your figures and tables are the evidence. The prose narrates them, points to what matters, and connects them to your research questions. Think of the prose as a tour guide walking readers through an exhibit of figures. ### 3.4.2 The Remind → Describe → Explain loop for each finding Now that you know what goes in a results section, here’s how to structure each finding so readers can follow without getting lost. The *Remind* → *Describe* → *Explain* pattern does the cognitive work for the reader. You reactivate the relevant hypothesis before presenting data. You report the statistics cleanly. Then you translate the numbers into plain English (without interpretation mind you) before moving on. Each finding becomes a self-contained unit that readers can follow without flipping back to your research questions. This structure also protects you during review. When Reviewer 2 asks “How does this analysis address RQ3?”, the answer is already in the text, because you explicitly linked the result to the question before presenting it. (Now, mind you, Reviewer 2 has opinions. Strong ones. They will find your Results section at 1 AM on a Sunday and write 1,200 angry words about your sample size. Be ready.) **Step 1 (Remind).** Remind the reader of the research question (or hypothesis). - *Template:* “To determine if \[Variable A\] affects \[Variable B\]…” **Step 2 (Describe).** State the statistical result. - *Template:* “We found a significant correlation (p < .05) as shown in Table 1.” **Step 3 (Explain).** Translate the math into English. (No deep theory yet, no inferences.) - *Template:* “As predicted, participants who \[Action A\] were more likely to \[Outcome B\].” ![Three-step infographic showing: Step 1 remind research question; Step 2 state statistical result with p-value; Step 3 explain findings in plain English.](https://lennartnacke.com/content/images/2026/02/Remind-Describe-ExplainLoop.webp) Remind → Describe → Explain Loop in the Results section. Remind → Describe → Explain. Each finding gets this treatment. The reader never gets lost. Think of it like giving directions. First, remind the reader where they’re going (“We’re testing if X affects Y”). Then, show them the numbers (“We found this result”). Finally, explain what those numbers mean in plain language (“This means people who did A were more likely to do B”). When you follow this pattern for every result, your reader can easily follow along without getting confused or having to flip back to earlier pages. **Example** (from [Tondello et al., 2016](https://doi.org/10.1145/2967934.2968082?ref=lennartnacke.com)): > **(Remind)* Our analysis included the following measures, as reported in the Methodology section: scale reliability, distribution of the user types scores, scale correlation with personality traits, and scale correlation with game design elements.* > **(Describe)* “Table 6 presents the bivariate correlations coefficients and significance levels between each Hexad user type and each of the Big Five model personality traits, measured by Kendall’s τ. \[…\] The Free Spirit type is positively correlated with openness, supporting hypothesis H2, and also positively correlated with extraversion and negatively with neuroticism.”* > **(Explain)* “H2: supported. The Free Spirit user type was positively correlated with openness to experience.”* Source: Tondello, G. F., Wehbe, R. R., Diamond, L., Busch, M., Marczewski, A., & Nacke, L. E. (2016). The Gamification User Types Hexad Scale. In *Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play* (pp. 229–243). ACM. [https://doi.org/10.1145/2967934.2968082](https://doi.org/10.1145/2967934.2968082?ref=lennartnacke.com) [AI Research Stack premium members](https://lennartnacke.com/#/portal/signup) get all three structural templates this week: CARS for introductions, the methods loop, and this Remind → Describe → Explain pattern for results. Each one is a single-page PDF reference sheet you can print and keep at your desk. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a premium subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 3.5 How to widen from findings to field-level impact This is where most writers struggle. The introduction funnels from broad to narrow. The **discussion** does the reverse: Open narrow (your findings) and widen out to the broad field. Your results don’t mean anything in isolation. A statistically significant effect size tells readers *what happened* but not *why it matters*. The discussion is where you answer the “So What?” question by connecting your specific findings to the broader conversation in your field. Without this connection, reviewers see a technically competent study that doesn’t advance knowledge. They’ll see a limited contribution and reject your paper. Most graduate training emphasizes *doing* research (that’s why you know how to collect the data). The interpretive move, asking “what does this mean for the field?”, requires a different skill set (that most professors just don’t teach you). You need to hold your findings in one hand and the entire literature in the other, then explain where the two connect. Bazinga. That demands deep familiarity with your field’s open questions, not just the papers you cited in your lit review. Many grad students haven’t read widely enough to see those connections yet. They know their study. They don’t yet know the *field*. That’s why you should run reading circles or give grad students reading assignments. AI summaries don’t cut it for this knowledge. Here’s an easy way to unstick yourself. Imagine your related work authors are guests at a dinner party, and your study just arrived. What would they say to each other? Nacke (2019) is holding a wine glass while he is nibbling on some fancy cheese, nodding vigorously with his mouth full (that old slob). Hamari (2019, 2020, 2021) is having a whiskey about it. Tu (2025) is shaking their head. Mogavi (2021) is chill and trying to get everyone to calm down and see the nuance. Your results just walked into this conversation looking like Vanilla Ice, the rapper not the ice cream flavour. Write that fun conversation. Each exchange becomes a paragraph in your discussion. This way of thinking reimagines your study as part of a bigger conversation with other researchers. The dinner party metaphor make you think of earlier research papers as people you’re talking to, who have their own ideas. ### 3.5.1 What a discussion section actually covers Before we get to structure, let’s be clear about content. A discussion section does the interpretive work that your results section deliberately avoided. Results describe *what you found*. Discussion explains *what it means*. Within that frame, discussion sections cover five content areas in sequence: 1. **Summary of key points.** This is a cognitive reset. By the time readers reach your discussion, they’ve processed your entire paper. Working memory is strained. You open by briefly restating your main findings, not to rehash statistics, but to get everyone back on the same page. Think of it as saying: “Here’s what we found. Now let’s talk about what it means.” 2. **Interpretation of results.** This is the heavy lifting you couldn’t do in the results section. What do your findings actually mean? Why did participants behave the way they did? What mechanisms explain the patterns you observed? This is where you move from description to explanation. Don’t rush it. This is the meaty, juicy core of your discussion. 3. **Comparison with existing literature.** Now you situate your findings in the field. How do your results relate to what others have found? Do they support, contradict, or extend prior work? This is where the dinner party metaphor comes alive. Each comparison is a conversational exchange between your study and someone else’s. 4. **Contribution to the research question.** This is your exclamation mark. After interpreting and comparing, you state clearly what your study contributes. You can foreshadow this at the beginning of the discussion, but you place the full statement here, at the end of the interpretive work, so it lands with impact. This is the “so what” that ties everything together. 5. **Limitations (and successes).** What went right and what went wrong? Be your own red team. Acknowledge the boundaries of your claims. This honest accounting builds credibility and helps readers calibrate how much weight to give your findings. These five areas form the interpretive core of your discussion. What comes next, the conclusion, may be a separate section or a final subsection within the discussion. That varies by field and venue. ### 3.5.1 Conclusion: separate section or final subsection The conclusion serves a different function than the discussion. Discussion interprets. **Conclusion** synthesizes impact. Some readers scan the conclusion first, looking for the bottom line. Your conclusion must deliver that bottom line clearly. A complete conclusion covers four elements: 1. **Main contributions to the field.** Remind the reader what this research adds to the broader conversation. This echoes the contribution statement from your discussion, but frames it for someone who might be reading the conclusion in isolation. 2. **Significance of achievements.** Don’t just state what you contributed. Explain *why it matters*. What changes because this research exists? What can people do now that they couldn’t do before? 3. **Recommendations.** Lean out of the window a little. What should practitioners, designers, policymakers, or other researchers do with this knowledge? Don’t just deposit findings. Tell readers what to do with them. Keep recommendations grounded and actionable. 4. **Future areas for investigation.** What questions remain? What should the next study examine? **A note on field variation:** In HCI and many SIGCHI venues, future work typically appears *before* the conclusion, often as its own subsection (e.g., “Limitations and Future Work”). The conclusion then wraps up with contributions, significance, and a final recommendation. In other fields, future work folds into the conclusion itself. Know your venue’s conventions. **Ending options:** A strong conclusion can end two ways. It can close with a grounded recommendation (“Designers should prioritize X over Y”). Or it can close with a forward-looking statement about what future research must address (“Understanding mechanism Z remains the critical next step”). Either works. Choose based on what your study best supports. ### 3.5.2 The structural template for discussion sections This template maps directly onto the five content areas above. Use it as a checklist. **Part 1: Summary and Positioning (1–2 paragraphs)** Open with a brief summary of your key findings (cognitive reset), then immediately position your work against prior literature. This part does three jobs at once: it reminds readers what you found, shows how your findings relate to existing work, and previews your contribution. - *Template:* “We set out to \[Goal\]. Our findings indicate \[Main Finding\]. This \[extends/supports/contradicts\] prior work on \[Framework/Taxonomy\], which \[What Prior Work Did\].” - Do not rehash statistics. Summarize meaning. The summary is implicit in shorter papers but should be explicit in longer ones where readers may have lost the thread. **Part 2: Interpretation and Implications (the core)** This is where the heavy lifting happens. You interpret your findings, compare them to existing literature, and explain what they mean for specific audiences. - In **shorter papers** (late-breaking work, short papers): 2–4 paragraphs doing interpretation + comparison + contribution. No subsections needed. - In **longer papers** (full papers, journal articles, taxonomy papers): this part fans out into thematic subsections. Each subsection is labelled “Implications for \[Audience/Domain\]” and walks through what your results mean for that stakeholder group. - *Structure per subsection:* Interpret the finding → Compare with prior literature (support or contradict) → Explain what this means for that audience. **Part 3: Contribution (1 paragraph, or woven into Part 2)** After the interpretive work, state your contribution explicitly. This is your exclamation mark. You can foreshadow it in Part 1, but the full statement lands here, at the end of interpretation, so it carries weight. - *Template:* “Taken together, these findings contribute \[Specific Contribution\] to the field of \[Domain\].” - In some papers, the contribution is woven into the final implications subsection rather than stated separately. Either works. **Part 4: Limitations (1 subsection)** Be your own red team. Acknowledge what went right and what went wrong. - *Template:* “These findings should be interpreted with caution due to \[Limitation\]. This was mitigated by \[Defence\], but future work should \[Address\].” **Part 5: Conclusion (separate section or final paragraphs)** Synthesize impact for readers who may scan here first. Cover contributions, significance, recommendations, and (in some fields) future work. - *Template:* “This research contributes \[What\] to \[Field\]. These findings suggest that \[Broad Implication\]. \[Practitioners/Designers/Researchers\] should \[Recommendation\].” **The underlying logic:** Summary → Interpretation → Comparison → Contribution → Limitations → Conclusion. That’s the reverse hourglass. You open narrow (your findings), widen through interpretation and comparison, land the contribution, acknowledge boundaries, and close with impact. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a premium subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. **Example** (from [Hadan et al., 2025](https://doi.org/10.1080/10447318.2025.2549073?ref=lennartnacke.com)): **(Part 1 — Summary and Positioning: Comparison + Contribution Preview)** > *“Our research extends prior literature by categorizing AI incidents across the four stages of the AI lifecycle (Ng et al., 2022), which offers a more detailed and actionable perspective on their occurrence. We also empirically synthesized the relationships between incident types, contextual factors, and their occurrence frequency in real-world contexts. This approach supports more targeted prevention strategies compared to studies that either did not address AI incidents within the lifecycle (e.g., Zeng et al., 2024), only distinguished pre- and post-deployment incidents (e.g., Slattery et al., 2024; Yampolskiy, 2016), or ignored the contextual factors (e.g., Shelby et al., 2023; Zeng et al., 2024).”* **Notice:** The summary of findings is implicit (“categorizing AI incidents,” “empirically synthesized relationships”). The bulk of this paragraph does comparison and contribution positioning. This is typical of taxonomy papers where the contribution *is* the classification. **(Part 2 — Interpretation and Implications: Four Thematic Subsections)** The paper fans out into four “Implications for…” subsections. Each interprets findings, compares to prior work, and explains what it means for a specific audience: > **5.3 Implications for raising public awareness:* “Prior studies have emphasized the importance of educating users about manipulative tactics (Hadan et al., 2024)… Our findings indicate that AI can be used to exacerbate the creation of hyperpersonalization and emotional manipulation… Thus, improving people’s AI literacy for them to effectively recognize and resist the AI-driven manipulation and harmful outcomes become essential.”* *(Interpretation: AI exacerbates manipulation. Comparison: Prior work on user education. Audience takeaway: AI literacy is essential.)* > **5.4 Implications for future AI incident reporting:* “Our findings emphasize the critical need for standardized, comprehensive AI incident reporting frameworks to ensure more consistent and transparent documentation of AI incidents and contextual factors that contributed to the incidents.”* *(Interpretation: Current reporting is inconsistent. Audience takeaway: Standardized frameworks needed.)* > **5.5 Implications for future AI incident detection and prevention:* “Our analysis revealed that AI developers and adopting organizations and government entities were identified as the primary responsible entities in more than half of incidents we analyzed… These incidents highlight the need for improved methods to detect and prevent AI incidents and for establishing baseline standards for AI development and deployment.”* *(Interpretation: Developers and organizations are primary responsible entities. Audience takeaway: Better detection methods and baseline standards.)* > **5.6 Implications for future AI incident governance and regulation:* “Our findings suggest the insufficiency of existing organizational and governmental AI governance systems, as over half of the incidents (*n *\= 117, 58%) we analyzed were due to organizational issues among AI developers and adopting organizations and government entities…”* *(Interpretation: Current governance is insufficient. Audience takeaway: Proactive governance measures needed.)* **(Part 3 — Contribution)** > *In this paper, the contribution is woven into the implications subsections and restated in the conclusion rather than appearing as a standalone paragraph.* **(Part 4 — Limitations)** > *“While our research offers valuable insights into AI privacy and ethical incidents, we acknowledge several limitations that may guide future work. First, our analysis focuses exclusively on incidents reported on the AIAAIC repository from 2023 and 2024\. As AI technologies evolve, the incidents and the associated contextual factors may change.”* **(Part 5 — Conclusion)** > *“Our research builds on and extends existing work by offering a more comprehensive and detailed AI incident taxonomy. This taxonomy captures cases previously hypothesized in the literature and novel incident types and contributing factors that existing frameworks fail to address… As AI continues to shape global policies, economies, and societal norms, the urgency to establish proactive, enforceable governance measures cannot be overstated.”* **(Section 3 — Limitations)** > *“While our research offers valuable insights into AI privacy and ethical incidents, we acknowledge several limitations that may guide future work. First, our analysis focuses exclusively on incidents reported on the AIAAIC repository from 2023 and 2024\. As AI technologies evolve, the incidents and the associated contextual factors may change. \[…\]”* *(Contribution restated. Significance explained. Ends with a recommendation framed as urgency.)* Source: Hadan, H., Hadi Mogavi, R., Zhang-Kennedy, L., & Nacke, L. E. (2025). Who is responsible when AI fails? Mapping causes, entities, and consequences of AI privacy and ethical incidents. *International Journal of Human–Computer Interaction*. [https://doi.org/10.1080/10447318.2025.2549073](https://doi.org/10.1080/10447318.2025.2549073?ref=lennartnacke.com) > [Premium AI Research Stack members](https://lennartnacke.com/#/portal/signup) get these five templates pre-built in a Notion workspace. [One click to duplicate.](https://lennartnacke.com/#/portal/signup) ## 4\. Use AI to critique your writing, not to write for you When ChatGPT came out, I initially resisted it as a tool for academic work, but I quickly changed my mind as more LLMs and AI tools came out and I saw them make huge improvements in extremely short periods of time. As I understood prompt engineering better, I was hooked. I got a feel for what it hallucinates and learned how to operate it better by providing proper context. That’s when I really got a hang of how the right prompting structures influence the LLM output that I get. Let me be clear here. We do not use AI to write our papers start to finish. It hallucinates citations (outside of [NotebookLM](https://notebooklm.google.com/?ref=lennartnacke.com) or more recently [OpenScholar](https://www.nature.com/articles/s41586-025-10072-4?ref=lennartnacke.com), try out the [demo](https://openscilm.allen.ai/?ref=lennartnacke.com)), often produces generic prose, and still lacks your intellectual fingerprint. But we include it in every writing process and we always refine our prompts and workflows (or create new skills in Claude Code/Co-Work). AI is extremely useful for offloading specific cognitive tasks that drain you. Frame it this way: AI is your intellectual sparring partner, but it’s not your ghostwriter. Muhammad Ali had a sparring partner. He still threw the punches in the ring. One of my favourite prompts assigns the AI a persona of a senior academic editor and runs it through three phases on your pasted draft. First, it summarizes your paper’s core argument in three sentences (claim, evidence, contribution) so you can confirm the AI actually understood what you wrote. Second, it identifies up to five structural problems, ranked by severity, with each one named, located by section and paragraph, explained in terms of how it breaks the argument, and paired with a one-sentence fix. Third, it recommends any section-level restructuring (moves, merges, or cuts). The constraints keep the AI locked on architecture only: no grammar, no word choice, no rewrites. The output format is standardized so you get a scannable diagnostic report, not a wall of prose. The whole design forces the AI to work like a structural X-ray of your paper, catching misaligned research questions, missing logical steps, redundant sections, and scope drift before you waste hours polishing sentences that might get cut anyway. Here is the full prompt ([**AI Research Stack Members**](https://lennartnacke.com/#/portal/signup) **can also download the full** [**skill**](https://support.claude.com/en/articles/12512180-using-skills-in-claude?ref=lennartnacke.com) **file at the very bottom of this article and get installation instructions in Notion**): Copy You are a senior academic editor who has reviewed 500+ manuscripts for top-tier journals (CHI, CSCW, Nature Human Behaviour). Your specialty is diagnosing structural problems before authors waste time on sentence-level polish. CONTEXT I am revising an academic paper draft. Before I edit sentences, I need to know if the argument architecture is sound. Structural problems include: misaligned research questions and findings, missing logical steps, redundant sections, scope creep, and violations of the broad → narrow → broad flow expected in empirical papers. INPUT I will paste my full draft below the prompt. TASK (3 phases) Phase 1: Summarize the paper's argument in 3 sentences. What claim does it make? What evidence supports it? What contribution does it offer? Phase 2: Identify structural problems. For each problem: - Name the issue (e.g., "Introduction promises X but Discussion never addresses X") - Locate it (section and paragraph number) - Explain why it breaks the argument - Suggest a fix in one sentence Phase 3: Provide a restructuring recommendation. If sections need to be moved, merged, or cut, specify the new order. CONSTRAINTS - Do not comment on grammar, spelling, or word choice. Structure only. - Do not rewrite sentences. Diagnose and prescribe. - Limit feedback to the 5 most critical structural issues. Rank by severity. - Be direct. No hedging language ("you might consider..."). State the problem and the fix. EXAMPLE OUTPUT FORMAT **Argument summary:** [3 sentences] **Structural issues (ranked by severity):** 1. [Issue name] | Location: Section X, ¶Y | Problem: [explanation] | Fix: [one sentence] 2. ... **Restructuring recommendation:** [If needed, specify new section order or cuts] OUTPUT ROADMAP Deliver the argument summary first so I can confirm you understood the paper. Then list structural issues. End with restructuring recommendations. Do not proceed to other revision levels. --- [PASTE DRAFT HERE] **Three examples of how I have used AI in the past:** 1. **Reverse Outlining.** Paste your draft and ask: “Extract the logical outline of this argument. Where is the reasoning weak?” This offloads the zooming out cognition work that’s hard to do when you’re deep in the text. ([AI Research Stack Members](https://lennartnacke.com/#/portal/signup) get the full prompt below.) 2. **Red Team Review.** Paste your methods section and ask: “Act as a skeptical Reviewer 2\. Identify three methodological concerns.” Better to find holes now than in your rejection letter. ([AI Research Stack Members](https://lennartnacke.com/#/portal/signup) get the full prompt below.) 3. **Clarity Editing.** For clunky paragraphs: “Act as an academic editor. Improve clarity, coherence, and conciseness without changing meaning. Let every word earn its place.” This is polishing. You’re offloading the micro-level optimization. ([AI Research Stack Members](https://lennartnacke.com/#/portal/signup) get the full prompt below.) Of course, there are a whole bunch more approaches like my personalization prompts that get rid of many AI writing quirks that current models have. I’ll share some for [premium subscribers of my newsletter](https://lennartnacke.com/#/portal/signup) this week. Frame it this way. AI is always your intellectual sparring partner It evaluates your thinking under stress so your working memory doesn’t have to hold everything at once. ## 5\. Fix the big problems before you polish the small ones When you revise, you face a decision at every sentence: what should I fix? Most people fix whatever catches their eye. A typo here, a weak word there. This is inefficient. You might polish a paragraph that gets cut when you realize the section doesn’t work. Fixing typos before fixing structure is like polishing the silverware while your house is on fire. Admirable attention to detail, Sheldon. Completely wrong priority. ![Four-step writing revision flow: 1 Structure (fix logic), 2 Clarity (fix paragraphs), 3 Style (fix word choice), 4 Proofing (fix grammar) -> Done.](https://lennartnacke.com/content/images/2026/02/Revision-Order.webp) Revision Structure *Always revise in this order:* 1. **Structure.** Is the argument logical? Does the paper flow broad → narrow → broad? If structure is broken, sentence-level fixes are wasted work. 2. **Clarity.** Does each paragraph have one job? Do topic sentences actually summarize? Is there fat to cut? 3. **Style.** Kill weasel words (seems, perhaps, might). Kill zombie nouns (conduct an analysis → analyze). Tighten prose. 4. **Proofing.** Grammar, spelling, formatting. Last, because everything above might change. When you’re fixing your research paper, start with the biggest problems first and work your way down to the smallest ones. Think of it like fixing a house. If the whole building is tilted, you need to fix the foundation before you paint the walls. Structure is like the foundation of your paper because it affects every single part. If your main argument doesn’t make sense or your sections are in the wrong order, nothing else matters yet. Once the structure is solid, move on to clarity so that each paragraph is easy to understand. Then work on style, which means choosing better words and making sentences flow smoothly. Finally, fix typos and grammar mistakes at the very end. These small errors only affect one or two words, so save them for last. If you fix typos first and then realize you need to delete that whole paragraph, you’ve wasted your time. Always work from big to small, and you’ll save yourself hours of unnecessary editing. ## 6\. The complete system Five protocols that reduce cognitive load at every stage: 1. **Inverted Assembly Line:** Write sections in cognitive-load order (Figures → Methods → Results → Discussion → Introduction → Abstract), not document order. 2. **Zero Draft:** Separate generation from evaluation completely. Write fast and messy first. Edit later. 3. **Structural Templates:** Use proven formulas for each section so you focus on content, not organization. - Abstract: Context → Problem → Solution → Findings → Contribution - Introduction: Territory → Niche → Occupy (CARS) - Methods: Contextualize → Describe → Justify - Results: Remind → Describe → Explain - Discussion: Answer → Implications → Limitations → Conclusion 1. **AI Sparring:** Offload critique, outlining, and clarity editing to AI. Keep the actual writing human. 2. **Hierarchical Revision:** Fix problems in order of impact. Structure first, then clarity, then style, then proofing. Each protocol removes a specific cognitive bottleneck. Together, they turn writing from a willpower battle into a systematic process. Your brain is a supercomputer running sophisticated cognition. Stop asking it to juggle and start giving it sequential, well-scoped tasks. That’s how you write better papers in less time. The protocols above are the complete system. [Premium AI Research Stack members](https://lennartnacke.com/#/portal/signup) get the *Notion page that runs it*, plus this week’s *7 AI prompts for every revision stage*. Add to that *three printable structural PDF templates* (CARS Introduction, Methods Loop, Results Pattern) as cheat sheets to print out and keep at your desk. ## Bonus Materials AI Research Stack members this week also get the argument audit Claude skill (and installation instructions in Notion), the 7 AI audit and revision prompts, 5 PDF worksheets in total, and a pre-submission checklist. [Join them.](https://lennartnacke.com/#/portal/signup) _This post is for paying subscribers only._ ### How to Stop Being a Bottleneck Professor in Academic Research URL: https://lennartnacke.com/how-to-stop-being-a-bottleneck-professor-in-academic-research/ Last updated: 2026-01-27T16:27:53.000Z #### Key Points - High personal productivity turns tenured PIs into bottlenecks once their lab grows. - Rewriting drafts yourself costs 10-15 hours a week and hinders student growth. - Delegation backed by structured feedback improves time use and student learning. - Each hour you spend rewriting is an hour your student does not learn to write. - A simple, repeatable process can recover that time within about 60 days. I used to rewrite my students’ Discussion sections all the time. After all, I had spent many years getting good at executing the skills that deliver them well. I could fix any draft in 30 minutes. So I did. Again and again. Here’s what most PIs get wrong: They think the problem is their team’s skill level. It isn’t. Most PIs lose 10–15 hours weekly fixing work their team could do. A simple change in that approach recovers that time within 60 days. The change takes 30 minutes per draft. And all it takes it a new mindset and a professional shift. The problem is that your competence has become a chokepoint. [A study of 135 managers in financial and insurance firms](https://techniumscience.com/index.php/socialsciences/article/view/2033?ref=lennartnacke.com) found that effective delegation significantly increased managers’ time available for priority activities, which means delegation improves time use. In academia, the math is worse. Every hour you spend rewriting is an hour your student didn’t learn to write. A [2024 meta-analysis of 15 empirical studies on academic writing for publication](https://jle.hse.ru/article/view/22198?ref=lennartnacke.com) found that attending academic writing courses and ongoing supervisor support were the most effective strategies for improving publication outcomes, while direct editing services ranked significantly lower. This means in practice that when you teach the basic ideas, your students get better. When you just fix their work, they keep needing your help. The skills that got you tenure are now the skills preventing what comes next. If you run a research team, if you’ve earned tenure by being the fastest, sharpest person in the room, and if you feel a gnawing guilt about the stack of drafts waiting for your red pen, this is for you. Here is a simple example of how this manifested in my life. I’m working late another Friday evening. I promised my partner I’d be home by now but there are three student drafts open on my screen, which each just needed a quick pass that has taken me hours and they are not done. I go in for another rewrite and, boom, it’s 8:30 PM and I missed dinner. I’ve now spent the entire evening doing work that my students should have learned to do two years ago. And deep in my gut I know that next week they’ll send me three more drafts that need the same fixes. This happened way more often that I’m comfortable to admit. It just doesn’t scale. So, I felt defeated, stuck deeply in the mid-career capability trap. Saying yes to every project and doing sh%t myself earned me tenure. So, why are they now blocking me from moving ahead? Because I had time and no team. Now, I’ve become the bottleneck with myself burning out at the centre of my operation. Self-Determination Theory, developed by psychologists Edward Deci and Richard Ryan, identifies three drivers of intrinsic motivation: autonomy, competence, and relatedness. When you rewrite your student’s draft, you strip away all three of them. They lose their autonomy (because you made the decisions to just fix things). They lose competence (they didn’t build the skill to rewrite). They lose relatedness (you became a gatekeeper of their work when you should have just mentored them instead). The fix for this isn’t to work harder. The fix is a simple 30-minute protocol that moves your role from editor to coach. (BTW, if you want more protocols for research leaders, [you will get one every week on my newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com).) Here’s how it works. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a paid subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Why this matters? You’re the lone cashier at a busy grocery store. Everything pools behind you. Drafts queue up. Students wait. Collaborators check in. The faster you work, the more people wait for you. Your speed creates an endless queue of work. Every week you stay the bottleneck, you lose ground to PIs who delegate. Their labs publish while yours just cannot get off the ground. Their students grow while yours still heavily depend on you. The gap compounds quietly, year after year, until you look up and wonder why your peers seem to have more bandwidth than you do and why you’re stuck in do-it-yourself mode forever. The math behind this is quite simple. You save 2 hours today by rewriting a draft yourself. But you create 200 hours of future dependency because your student never learned to do it. Now multiply that across your lab. Three students. Four drafts each per year. Five years of this pattern. That’s 60 rewrites you’ll do personally. At two hours each, you’ve spent 120 hours doing work that could have been distributed among your team, if you’d have taught the principles once. If you don’t fix this now, you’ll still be rewriting introductions at 55\. That’s painful stuff. The same structural problems. The same gap-finding issues. The same contribution clarity failures. Decade after decade. Boring. But I get it. Your identity was built on being the one who fixes things. You’re the person who can take a messy draft and make it sing in 20 minutes. Awesome. That skill earned you respect, grants, and tenure. You’re a rockstar, buddy. Fixing feels productive. You see the problem, you solve it, you move on. Let the dopamine rain down on you. But teaching feels slow. You explain the principle, you wait for them to try, you watch them fail, you explain again. This takes patience. Your reward is immediate when you fix. The payoff of teaching is delayed by months or years. This is the psychological trap. Competence becomes control becomes chokepoint. The better you are at fixing, the more you fix. The more you fix, the more dependent your team becomes. The more dependent they are, the more you have to fix. So, keep in mind that every time you rewrite a draft, you send a signal to your mentees: Your work isn’t good enough to learn from. You think you’re helping. But in all reality, you’re training helplessness. Students stop trying their hardest because they know you’ll fix it anyway. Why spend four hours polishing when you’ll rewrite half of it? Better to submit something rough and let you do the heavy lifting. It’s like what’s happening in primary schools right now. Kids asking why they need to learn to read and write when AI can do it for them. It’s downright concerning. Don’t be like AI. Be a mentor to your students. I once had a student submit a half-finished Discussion section with a note that kind of went like “you’ll probably rewrite this anyway, so I just put in the basic flow”. I was a little upset. But that student wasn’t lazy.. He had just learned, correctly, that effort didn’t matter because I would override it anyways. I had trained that behaviour by being too helpful. Here’s the question that should keep you up at night: What would happen if you couldn’t touch a single draft for 90 days? If your lab can’t publish without you for six months, you’ve failed as a leader. You’ve built a machine that requires you to keep everything moving. That machine will break apart the moment you step away from it, whether for sabbatical, illness, a major grant push, or retirement. It’s just not a working system. Most PIs discover this too late. They take their sabbatical and come back to a pile of disastrous projects. Or they get sick and watch their lab’s momentum evaporate into thin air. Or they retire and realize they never built anything that could outlast them. #### Why your strongest skill is now the problem You got tenure by doing the work yourself, fast and well. That habit does not scale once you run a real lab.​ - You can fix a Discussion section in 30 minutes, so you do. Again and again.​ - Students see the final text but never build the skill to produce it from scratch.​ - Your calendar fills with rescue edits instead of high-leverage work. In other sectors, managers who delegate well gain more time for priority work. The same logic holds in academia, but the cost of not delegating is worse because you are blocking both your own time and your students’ development. ### The three pillars of an effective lab Think of your lab as McDonald’s. I’m not talking about the food. Bear with me. I’m talking about the franchise business. I got this idea from *The E-Myth Revisited*, Michael Gerber’s book, where he explains why McDonald’s became “the most successful small business in the world.” Ray Kroc built McDonald’s on creating a system so clear that anyone could deliver consistent results. The hamburger wasn’t his product. McDonald’s itself was. Kroc’s genius was in creating the Franchise Prototype, a tested, documented system where the system runs the business and the people run the system. At Hamburger University, franchisees learn how to run the system that makes hamburgers. And the results of this are impressive. Where 80 percent of independently owned businesses fail in five years, 75 percent of Business Format Franchises succeed. The system compensates for skill gaps so that ordinary people can produce extraordinary results because they’re following a proven framework. Your lab needs the same approach. You can’t scale your editing speed. But you can scale your frameworks. ![](https://lennartnacke.com/content/images/2026/01/McD-Franchise.webp) What McDonald’s can teach us about building a lab. #### Pillar 1: Principles over polish The typical PI thinks they will just fix the draft because it is faster. The franchise-thinking PI asks what principle would prevent this problem in the next draft. Every recurring edit you make is a missing entry in your lab’s operations manual. Teach the three things that solve 80% of draft problems: problem-solution structure, contribution clarity, and problem identification. A student who knows these principles can self-correct. They read their own draft and recognize where the knowledge gap is missing and tied to an important friction in the literature, a great problem. A student who only sees your edits can’t do that. They just see red ink and feel bad. I once had a PhD student who submitted what I can only describe as unusable drafts for the first year. Structureless. Meandering. Missing the point of half the sections. I kept rewriting. Nothing changed. Then I tried something different. I spent three sessions, maybe two hours total, teaching just the principles in 1:1 sessions. No line edits. Just the frameworks. I even created a full-on course for this. Here’s how an introduction works. Here’s how you establish contribution clarity. Here’s how you identify and articulate a problem in the literature. Six months later, that same student’s drafts only needed light edits. Not because she suddenly became a better writer. Because she finally understood the system. #### Pillar 2: Review cycles over rewrites To change your approach from fixing it yourself to guiding them on what to fix and why, structure your feedback in three distinct cycles: 1. **Review of structure only.** Is the problem clearly stated? Does the contribution land? Is the argument organized? Don’t touch sentences. Don’t fix grammar. If the structure is broken, polishing sentences is wasted effort at this point in time. 2. **Review of the argument.** Now that structure is solid, examine the reasoning. Are claims supported? Does evidence connect to conclusions? Are there logical gaps in the writing? 3. **Polishing.** Only now do you address prose, word choice, and flow. And frankly, by this point, the student should be doing most of this themselves. But if you’re anything like me, this part if fun and you don’t want to miss out on doing it yourself. Each cycle is much slower than rewriting. You have to sharpen the axe before you can chop down the forest. But each cycle builds your student’s capacity and chop down the forest you will. The student learns to self-diagnose at each level. Once I understood that the students needed diagnosis, I now do one of these three things when I get a new draft (instead of diving in for edits): 1. Don’t open the draft to edit, but read a section and send back 3–5 diagnostic questions: “What problem are you solving in paragraph 2?” “How does your contribution differ from Smith et al.?” “What’s the logical connection between sections 3 and 4?” Make students articulate their thinking behind their writing to show you whether they understand the principles. 2. Open the document, but only add comments that identify the type of problem, never the solution. So, instead of rewriting a unclear sentence, comment something like: “Your contribution is really not clear here. Can you tell me what specific claim for your contribution this paragraph makes?” The student has to diagnose and solve it themselves now. 3. Don’t provide written feedback on drafts at all. Make your grad students bring their draft to a 1:1 meeting with you and walk you through their reasoning out loud. This is slower per student but forces active learning and prevents you from slipping into fix-it mode when you’re alone with their manuscript. We’ll revisit these ideas and how to trigger better habits below. The main idea for all these methods is to make it harder for yourself to jump in and fix things right away. Give yourself that space to let some teaching happen. #### Pillar 3: Tolerance for imperfection Their version will be 80% as good as yours. Accept that. And know that if you do your job well, at some point they might surpass you. This is the hardest pillar because it attacks your essence of producing perfectly polished work. Watching slightly less-polished version go out with your name on it feels totally painful. I get it. But it isn’t. The lab’s output ceiling is your willingness to accept good-enough from others. If you demand perfection, you cap your lab’s capacity at whatever you can personally touch. If you accept good-enough, you multiply your output. Here’s what helped me change my thinking. It’s better to have work that is good enough and where your students can do it on their own, than to have perfect work where they always need your help and can’t move forward without you. A student who publishes a good paper without you is worth more than a student who publishes a great paper because of you. The first one scales. The second one simply doesn’t. #### Your lab’s franchise prototype Mentoring costs 30 minutes now. Rewriting costs 30 minutes forever. After five teaching sessions, the student handles it themselves. After five rewrites, the student still needs you. Five teaching sessions at 30 minutes each equals 2.5 hours invested. The return is infinite. Every future draft that student produces without your intervention. Five rewrites at 30 minutes each also equals 2.5 hours. The return is zero. The sixth draft will need you just as much as the first. This is why Ray Kroc spent years perfecting the Franchise Prototype before scaling. The upfront investment in systems pays compound returns while the ongoing investment in fixing things yourself pays no dividends. Your lab’s franchise prototype is your documented writing system. The principles you teach. The review cycles you enforce. The standards you accept. You build it once. Deploy it to every student who joins your lab. Watch it work whether you’re there or not. That’s how McDonald’s serves 69 million hungry people a day in 120 countries. That’s how your lab publishes 10+ quality papers a year without you at the centre of every draft. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a paid subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### How to change your editing habits for good Knowing these principles isn’t enough though. You need triggers. If-then plans, known as [implementation intentions](https://cancercontrol.cancer.gov/sites/default/files/2020-06/goal%5Fintent%5Fattain.pdf?ref=lennartnacke.com), are simple rules that connect a situation with an action. Psychologist Peter Gollwitzer has studied these plans for many years. They work like this: If Y happens, then I will do Z to reach goal X. You link a situational cue (often time/place) to a specific action. These plans are your best tool to stop yourself from automatically editing student work. Here are four triggers and the replacement mental models to adapt that break the bottleneck editing pattern. #### If you get the draft **Current:** Draft arrives → Open document → Start editing → Finish in 30 minutes → Feel productive → Repeat forever. **Replacement:** *“If a draft arrives, I add it to my Feedback Queue and schedule a 30-minute mentoring session within the next 48 hours.”* Never open the document with the intention to edit. The moment you open it in edit mode, you’ve already lost. Your fingers will find problems. Your brain will fix them. Thirty minutes later, you’ve done exactly what you swore you’d stop doing. The pause breaks the reflex. Adding it to a queue creates much needed friction. Scheduling a mentoring session reframes your role from editor to coach before you ever even see the content. #### If you see all the problems **Current:** See all problems → Fix most of them because you can → Student learns nothing → Next draft has same problems. **Replacement:** *“If I see more than 5 issues, I stop reading and write down the top 3 structural problems.”* Focus on structure first. Don’t even touch line edits. Let students do those first. When everything looks broken, your instinct is to fix everything. Resist it. Ask yourself: *What’s the one thing that, if fixed, makes everything else easier for the student?* Usually it’s a structural issue. The argument doesn’t flow. Or the contribution isn’t clear. The problem statement is buried way too late into the draft. Fix that. The student learns to diagnose structure. You stop being the grammar police. #### If you feel the urge to do it yourself **Current:** Thinking it’s faster if you rewrite this section → Rewrite it → Feel efficient → Student learns nothing → Repeat. **Replacement:** *“If I feel the urge to rewrite, I close the document and write a 3-bullet explanation of what’s wrong and why.”* The urge to rewrite is real. It feels like efficiency. It’s actually the trap snapping shut. When you close the document and write bullets instead, something shifts. The explanation becomes your teaching material. You’ve just created a reusable artifact. Next time a student makes the same mistake, you send them the bullets instead of rewriting again. Here’s the test: if you can’t explain what’s wrong in 3 bullets, you don’t understand it well enough to teach it. And if you can’t teach it, you’ll be fixing it forever. #### If there is deadline pressure **Current:** Deadline approaching → Panic → Take over → Submit something polished → Student learns that deadlines mean you’ll rescue them. **Replacement:** *“If a deadline is within 72 hours and the draft isn’t ready, I schedule a 1-hour working session with the student instead of taking over.”* This is the hardest trigger. Deadlines feel non-negotiable. The stakes feel too high to risk. It’s all just a game in your head. But here’s what you’re actually risking: Another year of rescuing everyone on your own time. Another student who learns that pressure means you’ll swoop in and save them. Another draft that lands on your desk at 11:30 PM because they know you’ll fix it. Sit with them. Coach them in real time. Let them type while you talk. Resist the takeover reflex even when your hands itch to grab the keyboard. They learn under pressure. You learn to trust them. And next deadline, they’ll be 20% more capable. Then 40%. Then, at some point, they won’t need you at all. That’s the goal. Working yourself out of a job, one habit trigger at a time. #### A simple delegation and feedback system for PIs This is a straightforward way to move from fixing it to mentoring how to fix it. ****Step 1: Set clear writing expectations** 1. Share 1–2 annotated examples of strong papers from your group. 2. Spell out, in plain language, what you expect for each section (e.g., Discussion must answer: what we found, why it matters, how it fits, what’s next). 3. Give a short checklist for common failure points (missing claim, weak link to results, no limitations, etc.). ****Step 2: Change how you receive drafts** Require students to submit drafts with: - A brief note: “What I think is working” and “Where I know it is weak.” - Their own revision pass done before you see it (no first-draft dumping). This forces them to think like authors, not typists waiting for correction. ****Step 3: Use a consistent feedback template** Instead of line-editing everything, give feedback in three tiers: - ****Tier 1 – Structure:** - - “Your main claim is missing or buried.” - “Results and Discussion do not match; revise so each claim ties to a result.” - ****Tier 2 – Argument:** - - “You report effects but never say why they matter to this field.” - “You need one paragraph comparing your result to at least two prior studies.” - ****Tier 3 – Language and polish:** - - “Fix tense consistency.” - “Reduce long sentences and nominalizations.” Only drop into line edits for 1–2 example paragraphs. Make the student generalize the pattern to the rest. ****Step 4: Require student-led revision cycles** 1. Student revises based on your structured notes. 2. Student writes a brief **change log* explaining how they addressed each point. 3. You check whether the changes match your feedback. You do not rewrite. Repeat this cycle. Within a few rounds, the edits shift from structural to minor polish. Your time per draft drops. ### Creating a lab leader identity The hardest part of this transition is how you have to reshape your professional identity. For years, your value was execution. You outworked and outperformed everyone around you. Promotions came from output. Recognition came from fixing. Grants came from being the person who could deliver. Your entire academic identity crystallized around one core belief: *I am the person who can do it better than anyone.* Many professors will take this pride to the grave. And that belief served you well. It got you through grad school. It got you published. It got you tenure. But that identity around being indispensable is now the obstacle to mentorship. Leadership requires you to become dispensable. To create a system that can work without you. Senior leadership is about making the room better without you in it. It’s the opposite of the personality cult that many professors worship. Read that again. The goal isn’t to be the fastest writer, the sharpest thinker, or the most polished editor. The goal is to build a lab that produces quality work whether you’re there or not. The new identity is this: *I build people who build things.* Instead of saying I build things or I fix things that others build, my job is to help people grow and develop their skills. When I do this well, the good work happens naturally. *Would you rather publish 10 papers you didn’t write all by yourself than 5 papers you wrote yourself?* Sit with that for a moment. It feels wrong at first. It feels like you’re giving up quality. It feels like you’re lowering your standards. Consider James Patterson. More than 100 million people have read at least one of his books. He holds the *New York Times* bestseller record. And he doesn’t write most of his books alone. Patterson’s method is leadership in action. He creates the concept and writes a detailed outline (sometimes 60–90 pages). A co-author drafts from that outline. Then Patterson revises and does subsequent drafts to match his pace and voice. For *Sundays at Tiffany’s*, he wrote seven drafts after his co-writer delivered the first one. Here’s what Patterson understood that most junior PIs don’t. His job isn’t to write every single word himself. His job is to build systems that produce James Patterson novels. The 60-page outline *is* the system. The voice standard *is* the system. The revision process *is* the system. Co-writers execute the system. Patterson maintains quality control by doing subsequent drafts, not by writing the first one. The result? Patterson publishes more books in a year than most authors publish in a decade. Not because he works harder. Because he separated vision from execution and built a team that could multiply his output. Your lab can work the same way because your writing principles *are* the outline and your review cycles *are* the revision process. Your students execute. You maintain quality control through coaching and standard operating procedures. Patterson could write every word himself. He’s good enough. But he’d publish five books instead of fifty. He chose multiplication over personal execution. That’s your identity shift. A lab that publishes 10 good papers without you is more valuable to the university than a lab that publishes 5 great papers because of you. The first one scales. The first one survives you (if you end up hiring another professor). The first one actually trains the next generation of researchers instead of just using them as writing assistants for your own greatness. This identity shift will feel terrible at first. Let me be honest about that. Letting go of execution control feels like losing competence. You will feel like a fraud. You’ve spent all that time building your reputation on quality. Watching work go out that you could have made better feels like a personal failure. Like you’re slipping. Like you don’t care anymore. Watching a slower, less polished version go out with your name on it triggers something deep. Pride. Fear. The nagging voice that says *people will think you’ve lost your edge.* Keep in mind here that I’m not advocating for you to submit sub-par work. Never do that. Just let go of your perfectionism and trust your students. That discomfort is the price of multiplication. Every time you feel the urge to take over, that’s your old identity fighting for survival. Every time you cringe at an 80% draft, that’s your ego protecting itself. Every time you think *it would be faster to just do it myself*, that’s the bottleneck pattern reasserting control. **The discomfort means you’re growing past your old ceiling.** If this transition felt easy, you wouldn’t be doing it right. The discomfort is evidence that you’re letting go, you’re changing, you’re actually becoming the leader your lab needs instead of the hero your ego wants to be. The goal isn’t to eliminate the discomfort. The goal is to recognize it as a signal that you’re on the right path. Feel the cringe. Let the draft go anyway. Watch your student grow. Repeat until the new identity feels as natural as the old one. ![](https://lennartnacke.com/content/images/2026/01/Identify-Shift.webp) Changing your lab leader identity. ### Getting back your time in 60 days Here’s what changes if you commit to this for 60 days. Week 1 feels wrong. You’ll add a draft to your Feedback Queue and you will itch to open it. You’ll schedule a mentoring session and spend the whole time resisting the urge to grab the keyboard. You’ll watch a student struggle with something you could fix in two minutes. It will feel inefficient. It will feel slow. It will feel like you’re failing them. Week 3, something is likely going to change. A student sends you a draft and it’s… better. Not because you fixed the last one. Because you discussed the principles with them instead. You notice you explained the same structural issue to two different students, so you write it down. Now it’s a document. Now it’s part of your new system. Week 6, the compound returns start showing. A student catches their own contribution issues before you point it out. Another one brings a draft to your meeting and walks you through the structure before you even ask. The drafts aren’t perfect. But they’re diagnostic. You can see what they understand and what they don’t. You’re mentoring them like a good coach. By day 60, you’ll notice something strange. You have more time. Because your team is carrying more of the load. The drafts still come. But now they come with fewer structural problems. The teaching sessions are shorter because the students are asking better questions. The queue moves faster because you’re quickly reviewing them. Ten to 15 hours a week, back in your hands simply by changing what you do with the 30 minutes you spend on each draft. ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a paid subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Start with this one action The next time a draft lands in your inbox, don’t open it. Add it to a queue. Schedule a 30-minute teaching session within 48 hours. Show up to that session with one question: *What principle, if taught now, would prevent this problem in their next draft?* That’s it. One draft. One trigger. One teaching moment. If you do that once, you’ll see how it feels. If you do it for 60 days, you’ll see how it compounds. The best professors are the ones who build the best writers, not the ones who write the best papers themselves. Your best skill got you here. Now it’s time to build a better one. Lennart P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). #### Frequently Asked Questions ****Q: How do I stop being a bottleneck PI in my lab?** A: Stop rewriting everything yourself and switch to a clear delegation and feedback system. Set expectations, use structured feedback, and require students to revise their own text so you coach instead of fix.​ ****Q: How many hours can I save by delegating more effectively as a PI?** A: Many PIs can recover around 10-15 hours per week that they currently spend fixing student work. Over a semester, that equals several weeks of deep work time for grants and strategy.​ ****Q: How do I train students to write papers without rewriting everything myself?** A: Share concrete examples, define what good looks like, and use a stable feedback template. Make students revise based on your comments and explain their changes, instead of replacing their text with yours.​ ****Q: What is the best way to give feedback on a bad Discussion section?** A: Focus first on structure and argument, not line-level edits. Point out missing claims, weak links to results, and absent limitations, then model one strong paragraph and have the student rewrite the rest to match.​ ****Q: How long does it take for a delegation system to pay off in a lab?** A: The first few weeks feel slower because you are coaching instead of fixing. With consistent use, many PIs see a clear drop in editing load and better student drafts within about 60 days.​ ****Q: Is it fair to expect students to handle most of the writing?** A: Yes, because writing is a core research skill and part of their training. Your role is to provide standards, structure, and feedback so they can grow into independent authors, not to be their ghostwriter.​ ****Q: When is it okay for a PI to fully rewrite a student’s draft?** A: Reserve full rewrites for true emergencies like immovable deadlines or serious student crises. Treat these as exceptions, then debrief afterward so they still learn from the changes. # The AI Research Stack Bonus Paid subscribers get the complete lab delegation system: a printable PDF protocol to hand every team member, a 60-day bottleneck elimination system checklist that walks you from bottleneck to publishing machine, and 3 AI prompts that generate diagnostic questions to send, one that analyzes your editing patterns, and a script for running your first mentoring session. ↓ _This post is for paying subscribers only._ ### How To Write Every Day When Your Calendar Is Already Full URL: https://lennartnacke.com/how-to-write-every-day-when-your-calendar-is-already-full/ Last updated: 2026-01-16T22:27:22.000Z Most academics treat writing like an emergency. You've been taught that serious writing requires serious time. You block off a Saturday. You cancel plans. You tell yourself this weekend will be different. You sit down with coffee at 8am, determined to finally make progress on that paper. By 11:30 AM, you've rewritten the same paragraph three times. By 2 PM, you're exhausted and checking email. By Sunday night, you've produced maybe 400 usable words and you're dreading the next attempt. This is binge writing. And it's destroying your output and productivity. > *The academics who publish consistently don't find more time. They build systems that work in imperfect conditions.* I've seen binge writing wreck careers in lab after lab. I myself once spent half of a semi-sabbatical preparing to write and produced 12 pages. A mentee blocked every weekend for six months and submitted zero papers. Meanwhile, the most prolific researcher I know writes 30 minutes every morning, five days a week. She publishes at least six papers each year. The data backs this up. [A survey of 342 environmental biology trainees](https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4664?ref=lennartnacke.com) found that those who planned regular writing time during the week had more first‑author publications than those who wrote mainly in large blocks before deadlines. Students who only wrote right before deadlines published fewer first-author papers than students who wrote regularly on a schedule. For graduate students, writing on a regular schedule was the best way to get more publications. > “I only write when inspiration strikes. Fortunately, it strikes at nine every morning.” — popularly attributed to William Faulkner Here's the exact system I teach my mentees: --- Last week, I ran a poll wondering about renaming this newsletter. On LinkedIn (81 votes), 33% chose **The AI Research Stack**, on X (25 votes), 36% chose **The AI Research Stack**, and then curious enough, from the 52 of you who participated by clicking on the poll in your email, 32.7% choose **Research Freedom** trailed by 23.1% choosing **The AI Research Stack**. I actually enjoyed the discussion on LinkedIn the most, where Cole recommended **The Research Stack** as the new name (and I kind of like it). Alina chimed in that AI may be too narrow for what the newsletter does and supported the idea of Research Freedom. Well, so here is the thing, because the newsletter has two versions: paid and free, I am renaming it to **The Research Stack (free)** and **The AI Research Stack (paid)** to emphasize the weekly AI prompts subscribers are getting in the paid version. --- ## Get The Research Stack Become a smarter researcher in 5 minutes per week. Or become a paid subscriber to [****The AI Research Stack**](https://lennartnacke.com/#/portal/signup) for weekly AI prompts. Join 13k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Step 1: Reject the Big Chunk Myth Before you can design an effective writing schedule, you must kill the toxic belief that you only need long, uninterrupted stretches to write. I find that way too often, thinking that you need this big stretch of time to begin writing is the actual writing block. We tell ourselves it’s our workload or that it’s our schedule or that it’s maybe our lack of discipline, but it’s often just that we feel pressured to find this big chunk of writing. When, in reality, we don’t need to engage in binge writing, we just need to find little blocks of time to write consistently. The mindset that is tricky here and that you need to overcome is that you think you need to have holidays, weekends, maybe even a sabbatical, to just find that writing time, so that you finally, ultimately, sit down and begin writing. The problem is, if you put that much pressure on yourself, you often end up in an emotionally charged, desperate state, and then you write for eight hours straight, maybe. Sure, you feel good in the moment, but eventually, you’ll burn out completely. And then—as a result of that—you will avoid writing for weeks. This is not how to form a good writing habit. This cycle has a real motivational cost attached to it. Every time you return to the draft after a long gap, you’ve forgotten the argument. You spend the first hour just remembering where you were. You rewrite the same paragraph three times. It can get messy pretty quickly. One of the things I recommend to my mentees is to use snack writing instead to become more productive writers. The idea behind it is that you do short, regular writing sessions of 15 to 90 minutes that keep your project fresh in mind. You just use whatever works with your schedule and however short of a session you have. The idea behind it is that when you write in short bursts, your brain keeps working between those sessions and then hopefully you show up with clarity and you know exactly what to write next. Let me emphasize here that you shouldn’t need to clear the decks or free up your schedule to find that time. Because the reality is that you’ll never finish all your emails, your grading, or your admin work. If you wait for a clear schedule, then you’re just procrastinating. You want to actually schedule something in or begin a writing session as soon as an opening appears in your schedule. ## Step 2: Design Your Own Writing Setting Now that you’ve rejected the myth of the big chonk (that’s what I should have called it), it’s time to design your actual writing schedule. The best way to approach this is to treat your writing time like a formal class that you are teaching. It’s a non-negotiable item in your schedule and it’s recurring, so you’re defending it when others are trying to schedule something into that time. This is a technique known as time-blocking. The first step for this is to identify your most productive hours of the day. Ideally, those are your sacred hours that you protect from interruption. This should be the time when your brain is most alert. For most people, this is in the morning, but I’ve also had success scheduling late-night writing sessions when everything is quiet in the house and I can really focus. Ideally, you need to find a time when you have maximum access to your cognitive resources and you can focus just on the process of writing. The downside of an academic workday is that you have a lot of meetings and you have to make a lot of decisions and these actions drain you and exhaust you. So, trying to schedule writing time right after those is usually not productive. You might get a snack writing session in, but you likely won’t get good writing done. Block those hours for writing. Not for email. Not for meetings. For writing. It does help to pick a defensible time slot. So, if you can, then choose hours that are unlikely to be invaded by student or colleague requests. You have to understand that once you’ve scheduled these blocks, if someone asks for a meeting during this writing time, you have to tell them that you already have a commitment. Because you do! This commitment is with your research writing. Stay consistent. Treat writing times as regular appointments with yourself. Finally, it does help to establish a habitual site for your writing. If you’re using a consistent location, it will cue your brain that now it’s time to work. Now, it’s time to get writing. You can choose something inspirational like a a library corner, or a specific chair at home, or something as simple as your office. You just want to be consistent in the environments that you choose so that you can create this mental trigger. When you sit down in that spot, your brain knows it’s time to begin writing. Your brain knows what’s expected. Location + Time = Writing reflex. ## Step 3: Operationalize with Concrete Goals A schedule is a great way to begin a good writing habit, but it is useless without tying it to specific outputs. So I don’t recommend to ever sit down to just work on your paper. This is such a mushy goal that lets you procrastinate while you’re feeling productive. It is very important to be specific with your goal before you begin the writing process. One good way to do that is to set a S.M.A.R.T. goal. This means it should be Specific, Measurable, Achievable, Relevant, and Time-bound. Here’s an example of a S.M.A.R.T. goal that you could use: In the next 45 minutes, write the methods subsection for participants (eligibility criteria, recruitment score, sample size, demographics, table stub, ethics approval line) for your current paper in about 450 words plus a table skeleton based on your existing study notes and protocol. Or even simpler goals work, too: Write 200 words on the limitations section. Finish the second paragraph of the Discussion. Reconcile the citations in Chapter 3\. Format Table 2 with the new data. You just want to add a little bit of specificity to your goals and formulate them so that they give you traction. Now, once you’ve scheduled a writing time slot, you probably also want to include pre-writing tasks in that scheduled time (and, specifically, in your goals) as well. This is anything that will move the academic article forward. Things like data analysis, reading the relevant literature, just sketching or outlining the arguments or formatting tables. These tasks get you in the mindset of writing parts of your article. Always advance the research project as a whole. When you show up to your writing session, you should know exactly what you’re doing in the first 30 seconds. No wandering. No warm-up. Just getting the thing done that you set out to do. To create an effective habit we often need accountability systems. Social pressure is one secret hack to create and maintain a regular schedule of any activity, because humans are such social animals. ## Step 4: Build Accountability Systems One way to do this is to join a writing group where you have to publicly state your targets for the week at the beginning of the week and then report your success at the next meeting. The simple act of declaring your goal in front of the group increases how you will follow through with it. You could also create an explicit email trail that holds you accountable. The ideas here is that you regularly send your mentor or your writing partner updates that describe your writing behaviours, feelings, and your progress in a short bullet point list. And then you review this trail monthly to identify patterns that have affected your productivity. The simplest way to do personal accountability is to track your time writing. For this you simply record your daily writing minutes or your word counts or both in a simple spreadsheet or a notebook. This is a way to trick your brain because now you know that you must record a zero in your log. Not having to write down that zero can be an extremely strong motivator to sit down, shut up, and write. ## Step 5: Become a Master of Restarting Now, as you know, nobody is perfect and often life gets in the way of writing. Your schedule will probably break and deadlines will probably intrude and there will be crises or you might get ill. It’s ok. But the issue is that many do not have a system for how they will restart their writing sessions after such a snag occurs. So I would recommend the following recovery protocol in case of life getting in the way of your perfect writing time blocks. 1. A simple way to restart your writing is to **pre-engineer a warm start**. You want to make the entry point so obvious that even a tired person could do it. This means that you never finish your writing sessions at a natural breaking point. You want to stop mid-sentence, mid-paragraph, anywhere where you have a clear jumping off point for tomorrow. This cliffhanger means that when you return, you’ll have immediate traction because you’re bound to finish the open loop that you’ve set the day before. So you never are staring at a blank page when you’re beginning your writing session. 2. **Don’t get caught up in the emotional drama** of dealing with a lapse in your own writing. It is okay if you miss a day. You don’t need to make up for it by setting a huge binge writing session later. It is okay to keep a clear mind and simply return to your schedule the next day and just ignore that the lapse happened in the first place. There is no need to have an emotional investment in this. One missed session isn’t a writing failure. It is okay to just go back to the way things were before the lapse happened. 3. As you’re becoming consistent in your writing, make sure to **always reward your success immediately**. You can easily bribe yourself with small treats after completing your writing goal. Things like having a nice coffee, going for a walk outside, or getting your favourite snack. This never hurts. But you should not reward yourself by taking a break from your writing schedule. You always want to reinforce good writing behaviour with the reward that you choose. Something that lets you stay consistent. ## The Two-Week Test You might still be skeptical about whether or not you can actually set up a consistent writing habit, but I would urge you to try the following experiment. **Week 1:** Write in daily 30-minute sessions, Monday through Friday. **Week 2:** Save all your writing for just one 6-hour weekend binge. I would measure three things: your mental clarity, your stress levels, and your word output. This data should convert you faster than any motivational speech I could give you. You’ll likely discover that the 30-minute sessions produce more usable prose with less exhaustion. You’ll notice that returning daily keeps the arguments in your mind fresh. You’ll realize that the weekend binge leaves you depleted, exhausted, and dreading the next session. Try it out and report back to me how it went. ## Start Small, Start Now Creating new habits is hard and I want to leave you with some parting words to reminisce about. First of all, I wouldn’t redesign your entire schedule tomorrow. You’re just getting into another productivity binge and it will likely not be successful. A better way to do this is to just have a short 15-minute session where you’re picking your most productive time. Think about this a little bit and then choose your location. One location that you really like and then just set one goal that you want to achieve. Once you have those three things written down, you should be good to go for your first writing session. The next step, once you have your first writing session, would be to just write one simple new paragraph or to outline one simple new argument or just to revise one existing section of your paper. That’s it. One of those three. Not all three together. And then just stop. The goal is just to return to this the next day. Leave things unfinished in the first session. It’s good. Remember those cliffhangers. Once you’ve made a habit of returning to your draft, the drafts will eventually take care of themselves, because you’re not waiting for the perfect writing day anymore. We all know that doesn’t exist by now. But you now have a system that lets you work without having the perfect time. It just gets you started and returning to the drafts that are waiting for you. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## The AI Research Stack Paid subscribers get the complete daily writing system: a printable worksheet to design your schedule, a session checklist to tape next to your monitor, and three AI prompts that generate your session goals, decline meeting scripts, and session-end notes so you never waste time re-reading. ↓ _This post is for paying subscribers only._ ### How to negotiate your salary after getting a grant URL: https://lennartnacke.com/how-to-negotiate-your-salary-after-getting-a-grant/ Last updated: 2026-01-06T05:06:28.000Z You just secured a major grant. Congratulations. Now what? Most PIs celebrate, update their CV, and get back to work. They never realize they’re sitting on a compensation lever that expires the moment the institution forgets you won. The social proof of external funding creates a brief window where your market value is undeniable, your leverage is highest, and administrators actually have budget mechanisms to say yes. Miss that window, and you’ll spend years watching colleagues who landed smaller awards somehow negotiate bigger packages because they understood the game. Today, I’m going to walk you through the four pathways to turn your grant into more money, the one-page case that gets forwarded to the dean, and the exact numbers to put on the table. This playbook has four parts, and each one works whether you’re pre-tenure or running a lab of 20. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Summer salary is the fastest path to 33% more income in the US If you’re on a 9- or 10-month contract, this is your most direct win. Most grants allow you to cover summer salary, which means 2–3 months of additional pay on top of your base, funded entirely by the grant. The institution pays nothing. You’ve already earned the money. You just need to claim it. The math is simple. Three months of summer salary on a 9-month contract equals roughly 33% more annual income. Two months gets you 22%. Even one month covered by the grant puts real money in your account that wouldn’t exist otherwise. Here’s why this works: summer salary isn’t technically a “raise” in administrative language. It’s grant-funded investigator compensation. That distinction matters because it doesn’t require merit review committees, departmental approval chains, or budget reallocation. It requires your PI effort allocation and a chair who signs off. Check your grant’s terms. NIH allows up to two months. NSF varies by program. Many foundations have flexibility if you build it into the budget upfront. If you didn’t budget for summer salary in your original proposal, flag this for your next submission. ### Course buyouts trade teaching time for research momentum The second pathway doesn’t put cash in your pocket immediately. It protects your time, which compounds into more grants, papers, and eventually, money. A course release typically costs 12.5% to 18% of your salary per course (or less depending on your seniority), paid from your grant to the department. You lose one teaching obligation. The department uses those funds to hire an adjunct or redistribute load. You gain a semester of protected research time. Why does this matter for compensation? Because time is the rate-limiting factor for everything that gets you promoted, retained, and paid more. One bought-out course per year, sustained over three years, often translates into an additional R01-scale submission, two more papers, and a stronger case for the retention raise you’ll negotiate later. The hidden benefit for you: Course buyouts signal to your chair that you’re serious about research trajectory. They create institutional memory that you’re a funded PI who invests in productivity. When you eventually ask for base salary adjustments, that pattern usually works in your favour. ### A retention raise requires you to frame your value in their terms Here’s where most PIs sabotage themselves. They walk into the chair’s office and make the case about their work, their effort, their career. The chair nods politely and explains that budget constraints prevent any adjustments this cycle. Administrators don’t think about your career. They think about departmental rankings, overhead revenue, and recruitment costs. Your job is to translate your grant success into those terms. Start with Indirect Cost Recovery (IDCs). Every grant brings overhead to the institution, typically 40–60% on top of direct costs. A $500,000 NIH award might generate $200,000+ in IDC revenue. That money keeps the lights on, funds administrative positions, and subsidizes departments that can’t secure external funding. When you remind the dean that you’re generating six figures in overhead annually, you’re speaking their language. Next, calculate replacement cost. What would it take to recruit a new PI in your field? Startup packages in health and social sciences routinely exceed $500,000 when you factor equipment, personnel, protected time, and recruitment fees. Retaining you at a 10% salary bump is dramatically cheaper than replacing you. Finally, frame the trainee pipeline. Your grant funds graduate students and postdocs. Those trainees contribute to departmental metrics, generate publications, and often become the next generation of faculty who cite your institution in their bios. Disrupting that pipeline has costs the administration understands. ### Your one-page case must be forwarded The chair is not your final audience. They’re your advocate. The document you create needs to survive forwarding to the dean and possibly the provost’s office. That means eliminating anything that sounds like personal grievance and maximizing institutional benefit language. Structure your case with four components. Lead with the executive summary: A single paragraph stating the specific adjustment requested, the funding success that justifies it, and the risk of inaction. Be concrete. “$15,000 base salary adjustment to align compensation with market value following successful NIH R01 award” beats “request for consideration of salary review.” The second section documents value created. List the grant name, total award amount, IDC revenue generated, and trainees supported. Include one sentence on reputational benefit: “This award positions the department competitively for additional federal funding and enhances recruitment of high-caliber graduate students.” Third, articulate opportunity cost. Be direct without threatening. “Faculty with active federal funding are recruitment targets. Addressing compensation equity now prevents costlier retention negotiations later.” You’re not issuing an ultimatum. You’re helping them avoid a problem. Fourth, outline future deliverables. What will you submit in the next 12–24 months? What publications are in pipeline? This signals that you’re a continuing asset. ### Canadian PIs won’t have the grant pay them directly, but it still pays off If you hold NSERC, CIHR, or SSHRC funding, you already know the bad news. Tri-Council grants cannot cover PI salary like in the US. You’re on a 12-month contract. There’s no summer bonus waiting in your budget. But the good news is that your grant still creates compensation leverage. You just have to pull different levers. - **Course releases remain on the table.** Some Tri-Council grants allow course buyouts as an eligible expense, and many institutions have internal mechanisms to reduce teaching load for funded PIs. Check your institution’s collective agreement and your faculty association policies. A single course release may cost 12–15% of your salary charged back to the grant or covered by departmental research funds. That time compounds into papers, trainees, and the next grant. - **Your retention case is identical.** Canadian universities care about the same things American ones do: rankings, overhead recovery, recruitment costs, and trainee pipelines. A major Tri-Council grant generates institutional prestige and positions your department for Canada Research Chairs, CFI infrastructure funding, and provincial matching programs. Calculate what your grant brings: direct funding, HQP training, publication output, and reputational value. Then make the case for a market adjustment or accelerated progression through your salary grid. - **Target the next collective agreement cycle.** Many Canadian faculty salaries are governed by union contracts with defined grids and merit increments. Your leverage window may align with annual performance reviews rather than ad hoc negotiations. Know your grid. Document your grant productivity. Position yourself for the maximum allowable increment or an anomaly adjustment if your institution permits them. - **Use professional development funds strategically.** Most Tri-Council grants allow conference travel, research assistants, and equipment. Every dollar spent on support that frees your time is a dollar that accelerates your output. A well-funded RA who handles data collection saves you weeks. That time becomes your next application. The constraint is real. The workarounds exist. Canadian PIs who understand this system extract value from grants that goes far beyond the dollar figure on the award letter. ### What to do in Europe? ERC grants (Starting, Consolidator, Advanced) often explicitly include PI salary as eligible personnel costs. You can charge your time to the grant proportional to your effort commitment, typically 30–50% minimum depending on the grant type. This is a direct compensation pathway that Canadian Tri-Council grants prohibit. But what actually happens differs by country. **Civil servant systems (Germany, France, Italy, Spain, much of Central Europe):** Your salary is set by government pay scales. The ERC grant reimburses the *institution* for your time, but the money rarely flows to you as additional income. Your “W3” or “A13” or equivalent grade determines what you earn. The grant covers your cost to the university. You don’t see a bonus. **Contract-based systems (UK, Netherlands, Nordics, increasingly others):** More flexibility exists. Some institutions allow top-ups, buyouts, or supplementary payments from grant funds. The UK in particular has mechanisms for additional research time, though post-Brexit funding complexity has changed. **Mixed systems:** Many countries have hybrid arrangements where permanent faculty are civil servants but fixed-term researchers negotiate salaries. Your mileage will vary based on your specific employment category. Here is what you can still do: 1. **Check your institutional rules first.** The ERC says PI salary is eligible. Your university’s HR department and collective agreements determine whether you actually see any of it. 2. **Course buyouts work similarly.** If your grant can cover teaching replacement costs, you gain protected research time. This pathway exists across most European systems regardless of salary structure. 3. **The retention case still applies.** European universities compete for talent. An ERC grant makes you a recruitment target. Use that leverage the same way, whether you’re negotiating a new position, promotion, or exceptional salary adjustment outside the normal grid. 4. **Mobility creates opportunity.** ERC grants are portable. If your current institution won’t reward your success, another one will. The Additional Funding mechanism even covers relocation costs for PIs moving to take up their grant. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Set your anchor, target, and floor before you walk in Never negotiate without **three numbers** in your head. Your **anchor** is ambitious but defensible. A 15% base increase (plus full summer salary coverage if you’re in the US) represents significant recognition of your market value shift. You probably won’t get it. That’s fine. The anchor moves the conversation toward your target. Your **target** is success. Full summer salary coverage in the US (which costs the institution nothing) plus a 5–7% base merit increase committed for the next cycle. This is the outcome that materially improves your compensation while remaining achievable within most institutional constraints. Your **floor** is non-negotiable. At minimum, you should access whatever summer salary your grant allows. This is money you’ve already secured. Walking away without it means leaving compensation on the table that was built into your award. The psychology here matters. When you anchor high, the eventual compromise feels like a win for both parties. When you anchor at your floor, you often get less than the minimum you needed. One final note: if you lack market salary data, build it from public sources. Many state universities publish faculty salaries. In Canada, sunshine lists with public salaries exist in some provinces. Find comparable PIs at peer institutions with similar grant portfolios. Check the CVs of researchers 3–5 years ahead of you and note their funding history. Estimate startup packages in your field by asking recently hired colleagues. Data transforms “I deserve more” into “the market indicates a necessary adjustment.” That grant you just won created leverage. Use it before the institution forgets. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Toolkit [**Paid members**](https://lennartnacke.com/#/portal/signup) this week get my complete **Salary Negotiation Toolkit** for PIs who just landed a grant. You're getting the exact LaTeX memo template I give my clients to make the case to deans, a pre-negotiation checklist covering everything from F&A calculations to framing language, and four AI prompts that calculate your institutional value, coach you through the conversation, and extract funding data from any announcement: _This post is for paying subscribers only._ ### How to Build your Grant Tracking System URL: https://lennartnacke.com/how-to-build-your-grant-tracking-system/ Last updated: 2025-12-27T12:46:57.000Z Most PIs find grant opportunities the same way: someone forwards a call with three weeks left, you scramble to assemble a team, and you submit something decent enough to feel productive. Then you wait. Then you lose. Then you do it again. The researchers who consistently win funding do something different. They run a system that surfaces opportunities six months before deadlines. They know which program officers are prioritizing their methods. They track success rates across agencies and adjust their strategy accordingly. The grants come to them because they built infrastructure that makes them findable. Today, I'm going to walk you through a five-step workflow for tracking grant funding opportunities that transforms you from reactive scrambler to Gunter, the strategic funder-hunter. 😉 Here’s what that infrastructure looks like. The system runs on five components: (1) a curated list of funders worth watching, (2) a stack of discovery tools that do the scanning for you, (3) automated alerts that push opportunities to your inbox, (4) a central tracker that turns noise into decisions, and (5) a maintenance routine that keeps everything current. None of it requires expensive software or dedicated staff. You can build the whole thing in an afternoon and run it in 30 minutes a week. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 1\. Map the field before you search for anything The fastest way to waste time on grant tracking is to track everything. NIH alone releases hundreds of funding opportunity announcements annually (although that’s changing hard under the current US administration). Add NSF, DOE, private foundations, and international sources, and you're face with too many possibilities that don't match your work. Start by answering three questions: 1. What agencies have funded research similar to yours in the past five years? 2. What budget scales match your institutional overhead requirements and team capacity? 3. Which mechanisms align with your career stage and preliminary data? For US researchers, the primary federal funders in health, education, and social sciences are [NIH](https://www.nih.gov/?ref=lennartnacke.com), [NSF](https://www.nsf.org/?ref=lennartnacke.com), the [Department of Education](https://www.ed.gov/?ref=lennartnacke.com), and [AHRQ](https://www.ahrq.gov/?ref=lennartnacke.com). Add private foundations like [Robert Wood Johnson](https://www.rwjf.org/?ref=lennartnacke.com), [Spencer](https://www.spencer.org/?ref=lennartnacke.com), or [Mellon](https://www.mellon.org/?ref=lennartnacke.com) based on your domain. For Canadian researchers, the Tri-Agency framework of [CIHR](https://cihr-irsc.gc.ca/e/193.html?ref=lennartnacke.com), [NSERC](https://nserc-crsng.canada.ca/?ref=lennartnacke.com), and [SSHRC](https://sshrc-crsh.canada.ca/?ref=lennartnacke.com) covers most federal opportunities, with provincial bodies like [Alberta Innovates](https://albertainnovates.ca/?ref=lennartnacke.com) filling regional gaps and [Mitacs](https://www.mitacs.ca/?ref=lennartnacke.com) for industry grants. European researchers should focus on [Horizon Europe](https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe%5Fen?ref=lennartnacke.com)'s thematic clusters, ERC mechanisms, and national agencies like [UKRI](https://www.ukri.org/opportunity/artificial-intelligence-humanities-sandpits-canada-uk-and-us/?ref=lennartnacke.com), [DFG](https://www.dfg.de/?ref=lennartnacke.com), or [ANR](https://anr.fr/en/?ref=lennartnacke.com). Build a short list of eight to twelve primary funders. These become your surveillance targets. ### 2\. Set up your database stack for systematic discovery Your short list tells you where to look. Now you need tools that look for you. Government portals form your foundation. Grants.gov aggregates federal US opportunities with filtering by agency, eligibility, and keyword. The [Funding & Tenders Portal](https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/programmes/horizon?ref=lennartnacke.com) serves the same function for Horizon Europe. [ResearchNet](https://www.researchnet-recherchenet.ca/rnetsso/ssologin?language=en&ref=lennartnacke.com) handles CIHR in Canada. Bookmark these and learn their search syntax: quotation marks for exact phrases, Boolean operators for complex queries, and category filters for mechanism types. Layer in aggregation platforms for broader coverage. [GrantForward](https://www.grantforward.com/index?ref=lennartnacke.com), [Pivot-RP](https://pivot.proquest.com/session/login?ref=lennartnacke.com), and [Research Professional](https://www.researchprofessional.com/sso/login?service=https://www.researchprofessional.com/j%5Fspring%5Fcas%5Fsecurity%5Fcheck&ref=lennartnacke.com) scan across funders and provide personalized matching based on your profile. [Instrumentl](https://www.instrumentl.com/?ref=lennartnacke.com) offers AI-driven recommendations for both federal and foundation sources. [Grant Connect from Imagine Canada](https://imaginecanada.ca/en/grantconnect?gad%5Fcampaignid=23048063444&gbraid=0AAAAADlPFHG2nll1enxww00JQMRMl6OD9&ref=lennartnacke.com) is essential for Canadian private funders. [ScientifyRESEARCH](https://www.scientifyresearch.org/?ref=lennartnacke.com) provides European-focused curation with partner-matching features. Most universities provide institutional access to at least one of these premium aggregators. Check with your research office before considering to purchase an individual subscriptions. I never did. You can absolutely be successful without paying your own money for these. ### 3\. Configure alerts that deliver opportunities without manual search Manual checking doesn't scale. You need information pushed to you at predictable intervals. Start with agency-native alerts. [Grants.gov](http://grants.gov/?ref=lennartnacke.com) allows email subscriptions for specific agencies, keywords, or opportunity categories. The Funding & Tenders Portal offers similar functionality for Horizon Europe pillars. [NIH's Guide listserv](https://grants.nih.gov/funding/nih-guide-for-grants-and-contracts/subscribe?ref=lennartnacke.com) pushes new funding announcements directly to your inbox. These official channels catch announcements within hours of release. Add Google Alerts for broader monitoring. Create queries like "NIH health disparities funding" or "SSHRC partnership grants" to capture news coverage, blog posts, and secondary announcements that official channels miss. Set delivery to weekly digests to avoid inbox overload. Your aggregation platforms should also push personalized alerts. Configure GrantForward or Pivot-RP to send weekly emails based on your saved searches and profile keywords. The redundancy is intentional: different systems catch different opportunities, and missing a single announcement can cost you a year of planning. ### 4\. Build a central tracker that turns alerts into actionable intelligence Alerts generate noise. Your tracker creates signal. Create a spreadsheet or database with columns that capture decision-relevant information: funder name, opportunity title, mechanism type, deadline, eligibility requirements, budget range, required components, and current status. Include direct links to program announcements and submission portals. Note letter of intent requirements and standard submission cycles, since NIH operates on February, June, and October cycles while many foundations use annual or biannual windows. For team-based tracking, project management tools like Trello or Notion allow you to assign responsibilities, set deadline reminders, and move opportunities through pipeline stages. Create columns or tags for Discovery, Under Review, Pursuing, Submitted, and Archived. Automate reminders at 90 days, 60 days, and 30 days before deadlines. The tracker also builds institutional memory. After submissions, log outcomes and reviewer feedback. Track success rates by agency and mechanism. Note which program officers responded to pre-submission inquiries. This intelligence compounds over time, making your future targeting more precise. ### 5\. Establish a weekly review routine that maintains momentum Systems fail without maintenance schedules. Block 30 minutes weekly for grant surveillance. Use this time to process accumulated alerts, update your tracker with new opportunities, and archive expired listings. Monday mornings work well because agency announcements often cluster around the start of the week. Monthly, conduct a deeper review. Analyze your pipeline: How many opportunities are in active pursuit? What's your submission cadence over the next quarter? Are you over-indexed on one agency or mechanism type? Compare your success rates against published statistics (ERC starting grants average 10-15% success, NIH R01s hover around 20% depending on institute). Quarterly, evaluate the system itself. Which alert sources generated the most viable leads? Which databases consistently surfaced opportunities you missed elsewhere? Drop underperforming tools and add new ones as your research direction evolves. If you've expanded into international development, add [IDRC](https://idrc-crdi.ca/en?ref=lennartnacke.com). If you're doing more collaborative work, integrate partner-matching platforms like [EUcalls.net](https://eucalls.net/?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Compounding is your real advantage Researchers who run tracking systems don't just find more opportunities. They find them earlier, which means more time for relationship-building with program officers, more strategic collaboration assembly, and more thorough proposal development. The scramble disappears. The pipeline fills. Build the system once. Run it every week. Watch your funding trajectory change. Make 2026 your best funding year yet. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Content Paid subscribers this week get the exact grant tracking system I used to win $2.3M in federal funding across 7+ successful applications. You’ve already missed 26 research systems I sent this quarter. Each one cuts a major research bottleneck (email triage, paper rescue, literature reviews, faculty applications) from days to hours. Subscribe today to get access. _This post is for paying subscribers only._ ### How to get your email inbox batch processed URL: https://lennartnacke.com/how-to-get-your-email-inbox-batch-processed/ Last updated: 2025-12-18T08:18:59.000Z Every email you answer as it arrives costs you about 23 minutes of refocused attention. That number comes from productivity research on context-switching. For professors juggling research, lectures, and administrative duties, the damage compounds fast. Academic inboxes receive dozens to hundreds of messages daily, most of them routine stuff: due date confirmations, extension requests, grade clarifications, syllabus questions that were answered on page three (which is why [​comics like this exist​](https://phdcomics.com/comics.php?f=1583&ref=lennartnacke.com)). The problem isn't that you're slow at replying. Most professors I know handle emails rather quickly (most them don’t even spell their full name but just sign with a letter; guess they’ve never heard of a textexpander). The problem is that you treat every email as an interrupt when you could be batch processing everything at certain times. That’s why I'm walking you through a complete protocol for inbox triage that transforms fragmented email work into structured sessions. You'll process student emails faster, protect your deep work hours, and stop the mental whiplash of constant context-switching. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **1\. Schedule email blocks** The first rule of inbox triage is simple: stop treating email like a chat app. Constant checking destroys focus for research and writing. Studies on academic workloads suggest unchecked email consumes up to 30% of a professor's time. The fix is defensible time, blocks protected from intrusion. Set two to three specific times per day for email, such as 9 AM, 1 PM, and 4 PM. Each block lasts 30 to 45 minutes depending on your volume. Outside these windows, close your email client entirely. Not minimized. Closed. Gone. Cold turkey. Treat these blocks as rigid class meetings that cannot be cancelled for just checking. During your most productive hours (what some call *tiger time*), email doesn't exist. You're writing, analyzing data, or doing the work that actually advances your career. The messages will still be there at 4 PM. ## **2\. Scan the entire inbox before you reply to anything** When you open your email block, resist the urge to respond immediately to whatever sits at the top. Instead, scan all new messages without replying. Read in reverse chronological order. You're looking for patterns: multiple emails from the same student (where the second message often resolves the first), threads where someone already answered, spam and notifications that require zero action. Archive or delete the noise immediately. Flag genuinely urgent items, which should be rare (safety concerns, administrative emergencies). Categorize everything else mentally: routine questions, grade disputes, extension requests, referrals to other services. This scan-first approach takes five to ten minutes but prevents wasted effort. You won't draft a careful response only to discover the student sent a follow-up saying “never mind, found it.” You won't answer a question that a colleague already handled. You're gathering intelligence before deploying resources. ## **3\. Apply the two-minute rule** With your inbox scanned and sorted, execution begins. Handle urgent items first. These are genuinely rare. A student mentioning self-harm gets referred to campus counseling services immediately. An administrator requesting time-sensitive information gets a reply now. For everything else, apply the two-minute rule: if a reply takes less than two minutes, send it immediately. Don't overthink. Don't draft and revise. Type the response and move on. Most routine student emails fit this category when you have templates ready (paid subscribers get those today) and can put them on hotkeys or text expanders (see section below). The question about when the paper is due? Two sentences pointing to the syllabus. The extension request? Three sentences referencing your policy. The grade inquiry? Four sentences acknowledging the concern and setting a timeline for review. Batch your heavy emails, ones requiring investigation or nuance, for the end of the session. Clear the quick responses first. Then handle the complex items in one focused burst rather than scattered throughout the day. ## **4\. Build a snippet library that does the heavy lifting** You should never retype the same answer twice. Use text expansion tools (examples are [Espanso](https://espanso.org/?ref=lennartnacke.com) (cross-platform), [Alfred snippets](https://www.alfredapp.com/help/features/snippets/?ref=lennartnacke.com) (Mac), [Raycast](https://www.raycast.com/core-features/snippets?ref=lennartnacke.com) (Mac), [Text Blaze](https://blaze.today/?ref=lennartnacke.com) (Chrome Extension), [AutoHotkey](https://www.autohotkey.com/?ref=lennartnacke.com) (Windows), [Beeftext](https://beeftext.org/?ref=lennartnacke.com) (Windows), or built-in template functions, saved drafts) to create a library of responses you can trigger with short abbreviations. When you type "/syllabus," a complete, empathetic response about checking the syllabus appears. When you type "/extension," your policy statement materializes. Core templates every professor needs: 1. A syllabus redirect ("the answer to your question about \[topic\] can be found on page \[X\] of the syllabus"), 2. A late policy statement (neutral, pre-written, no emotional justification required), 3. An office hours redirect (for questions too complex for email), 4. A grade review acknowledgment (confirming receipt and setting a five-day timeline), and 5. A counseling referral (warm, immediate, with the correct contact information). I know what you’re thinking. The human element is maybe missing from templates and prompts, but templates are not cold. They are a consistent way that every student gets accurate information delivered with appropriate empathy, even when you're responding at 4 PM on a Friday after a week of grant reviews. ## **5\. Prevent emails before they arrive** Triage handles incoming volume. Prevention reduces it. If you're answering the same question three times, your course materials are failing. Review your last 50 emails and group them by topic: due dates, formatting requirements, grade weights, submission procedures. Each cluster represents a gap in your syllabus or assignment instructions. Deploy *a three before me policy*: students must consult three resources (syllabus, course FAQ, peers) before emailing you. Add this to your syllabus, repeat it in class, reference it in your auto-reply. Create a course FAQ document or discussion forum where common questions get answered once and remain searchable. Or if you want to go with the times, create a shared NotebookLM brain for all content and assignment information of your class. Set assignment due dates mid-week to reduce weekend panic emails. Use your learning management system for submissions to avoid attachment floods. Post short video updates when you notice confusion spreading. Each prevention measure you install reduces next semester's inbox volume permanently. ## **6\. Close every session at the end of a 24-hour window** The goal of each email block is completion. Archive resolved emails immediately. Use the snooze function for items that require follow-up on specific days. If you can't finish processing, snooze the remaining items to your next scheduled block rather than leaving them sitting in your inbox as visual clutter and mental weight. But if you can’t reach inbox zero. I know I certainly can’t. Ever. Then, become comfortable with the mess. Process a window of emails (24 hours) and call it there. You just need to work through a mental unit. At the end of each session, take 30 seconds to reflect. Did any pattern emerge that suggests a prevention opportunity? Did a particular assignment generate unusual confusion? Note these observations for syllabus revision at semester's end. Processing emails is all about closure for that period of work. When you leave your email block, the current inbox is processed. Nothing is waiting to ambush your focus during research hours. The system holds everything until your next scheduled window. After implementing this, I hope you're no longer a professor who checks email constantly. Be a professor who processes email twice or three times daily in focused batches. You scan before responding, apply the two-minute rule, use templates for routine queries, and close each session when your unit of work is processed. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. The result isn't cognitive and time savings. Your deep work hours stay protected. Your research gets uninterrupted attention. Your students still receive responsive, helpful communication, just on a schedule that serves your productivity. Start tomorrow. Set your blocks. Close the tab. Reclaim your focus. And if you need help replying to student emails, here are the 15 best email reply templates, your daily email triage checklist, and the ultimate AI prompt that does the work for you: _This post is for paying subscribers only._ ### How to write a discussion that reviewers accept URL: https://lennartnacke.com/how-to-write-a-discussion-that-reviewers-accept/ Last updated: 2025-12-08T10:11:57.000Z Your Discussion section is where most papers lose steam. Not because your data is bad. Not because you messed up your methods. But because you never actually *argued* anything. You shared your findings, added some citations, and hoped the reviewers would see the connections. They won’t. They’ll ask: “So what?” and add some spicy Reviewer 2 feels, and you’ll be scrambling through a major revision wondering where you went wrong. The Discussion is supposed to be an intellectual exchange. It transforms isolated data points into disciplinary knowledge. It positions your work in an ongoing academic conversation. It demonstrates you understand not just *what* you found, but *why* it matters and *to whom*. Skip this work, and you’re handing reviewers an easy rejection motive. Today I want to walk you through a five-phase framework for building Discussion sections that convince reviewers. This framework starts with specific results and then looks at broad implications. It also offers natural spots to include literature. This helps tackle possible reviewer criticism before they write it. Onward with the skeleton then. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **1\. Open with a core synthesis that answers your research question** Your first paragraph cannot rehash your entire Results section. Reviewers have already read it. What they need now is the answer. Give it to them. Draft 1-3 declarative sentences that explicitly restate your most significant finding. Make them directly address your original research question or hypothesis. This opening paragraph should be the only piece of your Results that a reader needs to recall to follow the rest of your Discussion. For quantitative work, phrase the result as the answer itself. “The intervention reduced anxiety by 60% at 12-week follow-up” communicates more than “We conducted repeated-measures ANOVA and found significant differences.” For qualitative findings, state the core theme, the *essence*, not the interview process that generated it. Immediately after this summary, add interpretation: A direct, critical analysis explaining *what the result means*. This is distinct from the raw finding. It translates the data point into meaningful, discipline-specific knowledge. You discovered X. What does X *tell us*? This opening synthesis anchors everything that follows. If you bury your answer on page 8, reviewers will assume you don’t have one. ## **2\. Positioning your findings in the ongoing debate** Your work does not exist in isolation. You stand on the shoulders of giants. This phase frames your new finding as the inevitable next step in an academic dialogue that your reviewers are already following. Start with the conformity check: Which established studies or theoretical positions do your results support? Make it explicit. Use strong topic sentences to launch comparison paragraphs. “This finding is consistent with foundational work by Nacke et al. (2016)” signals immediately where you’re placing yourself in the conversation. Then execute the contradiction pivot: Where do your findings diverge, contradict, or complicate existing literature? This divergence is often your genuine novelty. You *must* explain why the contradiction exists. Explain whether that’s sample differences, methodological variance, or a new context the original theory didn’t expect. Use contradictory results strategically. An unexpected null finding can challenge prior assumptions and suggest boundary conditions or moderators that previous work missed. “The absence of an effect in our rural sample, despite strong effects in urban contexts, suggests geographic infrastructure may moderate intervention reach” transforms a disappointing result into a theoretical contribution. Don’t merely list studies that agree or disagree. Build an argument about where your work sits in the knowledge space that surrounds your field and work. ## **3\. Name the contribution** This is where most Discussions fail. Researchers describe findings and compare them to literature. Then they stop, as if significance were self-evident. It’s not. You have to state it. First, articulate the theoretical advance. How do your findings revise, extend, or challenge the established frameworks you cited in your literature review? If your work introduces a new model, taxonomy, or concept, detail its structure and predictive power here. “These results suggest Theory X’s boundary conditions extend beyond the populations originally tested. This shows the mechanism may be more robust than we previously assumed” is a contribution statement. “Our results are interesting and warrant further investigation” is not. Second, define practical implications with specificity. What should practitioners, policymakers, or other stakeholders actually *do?* Or do differently because of your findings? “This finding suggests designers should restructure implementation protocols to prioritize Group B” gives readers something actionable. “Practitioners should consider these findings” gives them nothing. The gap between “we found something” and “here’s why anyone should care” is where reviewer enthusiasm lives. Bridge it explicitly. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) ## **4\. Use critical self-correction to preempt reviewer attacks** Demonstrating academic mastery requires anticipating critique. You want to be brutally honest with yourself to prevent rejection and prove your intellectual rigour. Reviewers respect authors who identify problems before they do. Dedicate a section to limitations and caveats. Detail the study’s weaknesses, whether sample size constraints, generalizability issues, or methodological trade-offs. Frame limitations as inherent constraints, though. Report them as the result of limited resources, ethical boundaries, or practical realities. Do not frame them as fatal errors. Every study has limitations. The question is whether you understand yours. A useful exercise to do before finalizing this section: Use AI to pressure-test your methodology. Prompt it to act as a highly skeptical peer reviewer and identify the three most significant methodological weaknesses. Address those weaknesses proactively in your draft. If you find them first, you control the narrative. Then convert every identified limitation into a future research plan. Each unresolved question becomes a specific, actionable proposal for later work. These suggestions serve as more than acknowledgments of what you didn’t do. They form the backbone of your next grant application (or paper). Limitations become your pipeline. ## **5\. Cement your position as a thought leader in the conclusion** Never end your Discussion with limitations. That’s ending on apology. Instead, conclude with a paragraph that reasserts significance at a high level of abstraction. Restate your core contribution and its importance in a way that links back to the broad opening context of your Introduction. Your paper should feel like a complete intellectual arc. You opened with a big problem. You narrowed it to a specific question. You answered it with data. You situated that answer in the literature, acknowledged constraints, and now return to the big picture with new knowledge in hand. End with a singular, compelling sentence that looks ahead. This forward view positions you as someone driving the field into new territory. This is more impactful than reporting from the present. “As HCI moves toward adaptive designs, these findings offer a foundation for matching exercise intensity to motivation profiles” does more than summarize. It stakes a claim. Something that your future research can build on. Your Discussion is where you convince reviewers the field is better off because you did this work. Structure it to make that case impossible to ignore. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). **Paid subscribers: Keep reading for three bonus materials. ⬇️** _This post is for paying subscribers only._ ### How to Write a Master’s Thesis Ready for Journal Publication URL: https://lennartnacke.com/how-to-write-a-masters-thesis-ready-for-journal-publication/ Last updated: 2025-12-08T10:11:23.000Z Most Master's theses never get published. Master’s theses have a 33% publication rate. The other 67% fail because researchers treat conversion as an editing task rather than a strategic rewrite. A thesis runs 15,000-50,000+ words and demonstrates deep mastery. A journal article usually spans 3,000-8,000 words and delivers one focused contribution. Most students spend six months trying to cut down their thesis, get discouraged, and give up. But you can write your thesis to be 80% publication-ready from day one. Five strategic decisions during the writing process can save months of revision work and dramatically increase your chances of getting published in academic journals. #### Key Points - Identify one central claim from your thesis that offers standalone value. Journal articles need exactly one focused contribution - Select your target journal before writing and model your sections on their published articles (word counts, citation limits, structure) - Cut 80-84% of content using surgical deletion protocols for each section, not proportional trimming across the board - Restructure from knowledge demonstration (thesis) to knowledge advancement (article) by front-loading your contribution in the abstract and introduction - Add publication-specific requirements like structured abstracts, data availability statements, and ethics disclosures that thesis committees don't require ## Find your main claim first Most theses address multiple research questions. Journal articles need exactly one central message. Read your thesis and identify which finding, methodology innovation, or theoretical contribution offers the strongest standalone value. Review your results chapter and mark every novel finding. Ask yourself: Which result would surprise researchers in my field? Check recent issues of three target journals to see which contribution fits their publication pattern. Then write a one-sentence claim that captures your contribution. For example: “Method X reduces processing time by 40% compared to standard approaches” or “Administrative support matters less than peer relationships in teacher retention decisions.” Editors reject 40-77% of submissions before peer review due to scope mismatch and other factors including poor quality, lack of novelty, and methodological flaws. A precise claim lets you select journals where your work fits, not where you hope it might. If you can’t explain your contribution in 20 words, you don’t have a focused article yet. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Define your journal profile pre-writing Systematic journal selection determines whether your manuscript receives fair consideration or immediate rejection. Create a comparison matrix of 3-5 journals, then select your primary target. Pull 5 recent articles from each candidate journal. Document the average word count for each section—Introduction, Methods, Results, Discussion. Note citation limits (which vary widely by journal and field), figure-to-table ratios, and reference style requirements. Check author guidelines for submission format, statistical reporting standards, and ethical disclosure requirements. Then contact the editor with a 150-word abstract to confirm fit before investing weeks in conversion. Introduction sections usually represent approximately 10% of total word count, Methods approximately 15%, Results and Discussion together approximately 65-75%, with specific proportions varying significantly by journal and field. For example, in my field, HCI papers have separate Related Work sections (a standalone section that reviews prior research and establishes the research gap), so Introduction sections represent approximately 10% of total word count, Related Work approximately 15-20%, Methods approximately 15%, Results approximately 20-25%, and Discussion approximately 25-35%. Each journal has different expectations. You need page-by-page breakdowns showing that your target journal publishes 7,000-word articles with specific section distributions and 25-40 references, not generic advice about keeping it concise. ## Perform surgical content reduction Condensing 15,000-50,000 words to 3,000-8,000 requires cutting 80-84% of content. Random deletion creates incoherent manuscripts. Reduce each section using specific deletion protocols, not proportional trimming. For your Introduction (target 800-1,000 words): delete your encyclopaedic literature review, keep 3-5 key citations that establish the research gap, cut all background on adjacent fields, and replace the whole “Here’s what we know about X” with “X remains unsolved because Y.” In HCI, for your Related Work (target 500-900 words): No deep surveys of the entire research domain, organize remaining citations by core themes rather than chronologically, focus on 8-12 papers for each that directly address your specific problem or approach, eliminate papers that are tangentially related or only provide general context, structure content like a funnel from broad area to specific gap, cut detailed descriptions of each cited system’s implementation, replace methodology descriptions with outcome-focused statements like “System X improved accuracy by 25% but required expert configuration,” synthesize findings into themed paragraphs rather than listing paper-by-paper summaries, and end with a clear statement of the gap your work addresses. For your Methods (target 600-900 words): delete procedural justifications, cite standard laboratory procedures rather than describing them, cut equipment specifications unless they affected results, replace detailed protocols with citations to established methods, and eliminate validation data. For your Results (target 800-1,200 words): report only findings that directly support your single claim, delete pilot studies and preliminary analyses, convert descriptive statistics to one summary table, cut any interpretation, and use exact values like “Response time decreased 40% (p<0.001)” instead of “Response time improved significantly.” For your Discussion (target 800-1,000 words): delete restatement of results, cut comparisons to tangential studies, focus on what your finding means and why it matters, eliminate vague future research suggestions, and restrict discussion to about one-third of your total word count. Reviewers reject wordy manuscripts because excess content obscures contributions. After this step, your manuscript should be 3,000-8,000 words with 20-100 references (if there is a total submission page limit, references usually cap at 40, but in many fields, references don’t count against the page limit \[like in HCI\], so Reference sections have been getting larger. If you’re still over 8,000 words, you haven’t made real cuts yet, go back in there and cut more. ## Restructure for journal narrative Theses demonstrate *your* knowledge. Articles advance *everyone’s* knowledge. The difference is structural. Rebuild your manuscript’s argumentative spine so every paragraph drives toward your single claim. Write new transition sentences between sections. Thesis transitions are too gradual for articles. Front-load your contribution by stating your main finding in the abstract’s final sentence and in the introduction’s last paragraph. Contribution is everything in publication. Reverse your discussion structure: start with what you found, then explain what it means. Theses build up to conclusions; articles lead with them and finish with them. Create one integrative figure that shows your complete methodology or key results at a glance. But keep Edward Tufte’s data-ink ratio high (i.e., maximize the proportion of ink dedicated to displaying actual data by removing gridlines, decorative elements, 3D effects, redundant labels, and visual clutter that do not contribute to understanding your findings). Add section subheadings that signal your argument, like “Reduced Processing Time Through Modified Algorithm” instead of just “More Results.” Reviewers typically spend several hours on first-round reviews, though time allocation varies significantly by field, journal, and manuscript complexity. They decide to reject or advance based on whether they can identify your contribution in the abstract and introduction (and unfortunately that subconscious decision often happens in minutes). Putting your findings out of sight until page 8 guarantees rejection. A colleague unfamiliar with your work should be able to read your abstract, introduction, and discussion’s first paragraph and explain your contribution in one sentence. ## Address publication-specific requirements Thesis committees and journal reviewers enforce different standards. Write a structured abstract matching the journal format. Thesis abstracts are *descriptive*, journal abstracts are *informative* with Background, Methods, Results, and Conclusions sections. Create author contribution statements and conflict of interest declarations. Acknowledge in the author note that the work was based on your thesis rather than citing the thesis in the article text. Add data availability statements explaining where readers can access raw data. Include funding acknowledgments even if unfunded by stating “This research received no specific grant.” Prepare a cover letter explaining why this journal, why this matters, and which 3-4 reviewers have appropriate expertise. Papers that don’t follow author instructions in terms of format, word count, number of figures and tables, and reference style face immediate desk rejection in many journals’ review processes. Before submission, verify you’ve addressed all items in the journal’s author checklist. Missing one required element (like ethics approval statements) triggers desk rejection. Your thesis already contains publishable research. These six steps convert it because conversion isn’t creation. It’s extraction. Extraction through deletion, deletion through criteria, criteria through journal analysis. Most researchers abandon this process not from failed research but from failed strategy, not from insufficient data but from insufficient focus. The published succeed where others stall by treating conversion as what it is. A shackled rewrite requiring three commitments from you: Cut what doesn’t serve your single claim. Structure what remains for journal narrative. Submit what survives to editors who publish that work. Start this week. Choose your claim. The other four steps become mechanical once you know what you’re publishing and where it belongs. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Frequently Asked Questions #### Q: How long does it take to convert a Master's thesis into a journal article? A: The conversion process takes 4-8 weeks if you follow systematic deletion protocols and have identified your single claim. Most researchers spend six months because they try proportional trimming rather than strategic extraction. Starting with a clear target journal and modeling their published articles cuts conversion time by 50-70%. #### Q: Can I publish multiple articles from one Master's thesis? A: Yes, but only if your thesis addresses genuinely separate research questions that each offer standalone contributions. Each article needs its own focused claim and appropriate journal home. Publishing the same findings in multiple journals (salami slicing) violates ethical guidelines. Extract different contributions, don't repackage the same contribution multiple times. #### Q: What's the biggest mistake students make when converting their thesis? A: Treating conversion as an editing task rather than extraction. Students try to cut their 50,000-word thesis down to 8,000 words while keeping the same structure and scope. This creates incoherent manuscripts that try to address multiple research questions. Successful conversion requires identifying one claim and building a new manuscript around that claim alone. #### Q: Should I contact the journal editor before submitting my converted thesis? A: Yes. Send a 150-word abstract to the editor asking if your work fits their scope before investing weeks in conversion. Editors can confirm fit or redirect you to a better journal. This prevents desk rejection due to scope mismatch, which accounts for 40-77% of rejections before peer review. #### Q: How do I know if my thesis contribution is strong enough for publication? A: Ask yourself: Which of my findings would surprise researchers in my field? Check recent issues of three target journals to see if similar contributions appear. If you can't state your contribution in one sentence under 20 words, you haven't identified a focused claim yet. A colleague unfamiliar with your work should read your abstract and introduction and explain your contribution immediately. #### Q: What happens to my thesis literature review when converting to an article? A: Delete the encyclopedic literature review entirely. Keep 3-5 key citations that establish the research gap your work addresses. Journal introductions run 800-1,000 words, not 3,000-5,000\. Replace "Here's everything we know about X" with "X remains unsolved because Y." In fields with separate Related Work sections (like HCI), focus on 8-12 papers directly addressing your problem and synthesize findings thematically. #### Q: Do I need to collect new data to publish my thesis work? A: No. Your thesis already contains publishable research if you extract the right contribution. The issue isn't data insufficiency but focus insufficiency. Most students abandon conversion not from failed research but from failed strategy. Identify which single finding justifies publication and build your manuscript around that claim using the data you already have. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Content Today’s bonus content includes a downloadable 6-week conversion plan, an interactive timeline database in Notion, a detailed surgical reduction checklist covering all major thesis sections, and three AI prompts for claim extraction, section reduction, and discussion restructuring. People ask me often, how the AI prompts look like [before making the decision to pay for the newsletter](https://lennartnacke.com/how-to-write-a-masters-thesis-ready-for-journal-publication/#/portal/signup), so here is your Black Friday week freebie AI prompt to [get a taste](https://lennartnacke.com/how-to-write-a-masters-thesis-ready-for-journal-publication/#/portal/signup): ### **Writing a journal submission cover letter that positions your contribution (AI Prompt)** Copy I need to write a cover letter for my journal article submission that explains why my work fits the journal's scope and matters to their audience. Target journal: [JOURNAL NAME] Journal's stated scope: [PASTE 2-3 SENTENCES FROM JOURNAL WEBSITE DESCRIBING SCOPE/AUDIENCE] My article title: [YOUR ARTICLE TITLE] My single publishable claim: [YOUR ONE-SENTENCE CLAIM] Main finding: [SPECIFIC RESULT WITH NUMBERS] Why this matters to the field: [2-3 SENTENCES] Recent articles from this journal that relate to my work: - [Author names, year, article title, how it relates to your work] - [Author names, year, article title, how it relates to your work] My co-authors (if applicable): [NAMES AND AFFILIATIONS] Special considerations: - Is this part of a special issue? [YES/NO, if yes provide theme] - Did you contact the editor beforehand? [YES/NO, if yes note their response] - Are there any suggested reviewers or reviewers to exclude? [LIST IF APPLICABLE] Please write a cover letter that: 1. Opening (1 paragraph): States the article title, manuscript type (research article, brief report, etc.), and the core contribution in one sentence. 2. Significance (1 paragraph): Explains what gap in the literature this addresses and why it matters to the journal's audience. References 2-3 recent journal articles showing fit. 3. Key finding (1 paragraph): States the main result with specific numbers and explains its theoretical or practical significance. 4. Fit statement (1 paragraph): Explains why this journal specifically is the right home for this work based on its scope and recent publications. 5. Compliance (1 paragraph): Confirms the manuscript is original, not under consideration elsewhere, all authors approved submission, and any required statements (ethics approval, conflicts of interest, data availability). 6. Closing (1 sentence): Thanks the editor for consideration. Requirements: - Total length: 300-400 words maximum - Write in first person plural ("We") even if you are the sole author - No promotional language or exaggeration ("groundbreaking," "pioneering," "novel") - Cite specific recent articles from the target journal by first author and year - State the contribution directly without hedging - Professional but not overly formal tone - Follow standard business letter format with proper salutation to the editor-in-chief After providing the cover letter, explain which elements make it effective for this specific journal versus a generic submission letter. _This post is for paying subscribers only._ ### How do you structure the paper writing process? URL: https://lennartnacke.com/how-do-you-structure-the-paper-writing-process/ Last updated: 2025-11-16T05:45:53.000Z > [*​Paid Write Insight Members get a complete paper-writing system today including a 6-week schedule, diagnostic tools for identifying writing bottlenecks, a literature matrix template, a structured 90-minute block breakdown, research question filters, a quality checklist, and AI prompts for improving academic drafts, all nicely bundled in a Notion template you can duplicate.*](https://lennartnacke.com/#/portal/signup) You know what stops most researchers from finishing papers on time? It’s not a lack of data. It’s not insufficient knowledge. It’s the paralysis that comes from staring at a blank page with zero structure to follow. You sit down to write, waste three hours reorganizing references, and end up with two mediocre paragraphs. That is if you even get to writing at all. Then you wonder why your colleague submitted three papers while you’re still stuck on the introduction of one. The solution isn’t working harder or finding more time, but you need a systematic process that removes decision fatigue from every stage of paper writing. So today, I’m walking you through a 4-phase system that turns paper writing from an overwhelming project that you never get started with into a series of concrete, completable tasks. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. #### **Phase 1: Install the right mental framework before you write a single word** Most writing advice skips this entirely, but your mindset determines whether you finish papers or abandon them halfway through. Start by accepting that writing stuff is inherently difficult. Even tenured professors with 100+ publications find writing hard. The difference is that they don’t interpret difficulty as a signal they’re doing it wrong. When you hit resistance, that’s normal. Steven Pressfield calls it Resistance with a capital R in his book *The War of Art*. It’s the internal force that stops you from doing your work. It shows up as fear, self-doubt, procrastination, and perfectionism. Resistance is invisible but you feel it as an energy field radiating from work you need to do. Amateurs quit at this point and check email. Pros show up every day no matter what. So, if you want to master your craft like a Pro (and I would say many PhDs are Pros), you have to feel the fear instead of waiting to overcome it first. Do the work anyway. The best way to get there is to eliminate perfectionism from your first draft. You cannot edit a blank page, but you can edit a terrible page. Give yourself permission to write badly in the first pass. We call this a zero draft. Your job in that draft is to get ideas out of your head and into a document. Quality comes later during revision. This also means you should only write papers about topics you care about. The process is much too painful to go through it for some mediocre outcome that doesn’t tickle your fancy. Next, schedule specific writing blocks in your calendar. Don’t find time to write, you never will. You make time. Treat 90-minute writing sessions as non-negotiable appointments. If you’re balancing a PhD with a full-time job, even three 90-minute blocks per week will produce a complete draft in 4–6 weeks. Yes, that is possible and something [I help my coaching clients](https://learn.lennartnacke.com/coaching-call/?ref=lennartnacke.com) with all the time. Finally, understand that writing generates thinking. Many researchers delay writing until they’ve *figured everything out in their head*. This is backwards. Writing is how you discover what you actually think. The act of converting vague ideas into sentences forces clarity. Writing is thinking. #### **Phase 2: Narrow your focus until you have one specific, answerable question** Broad topics kill papers before they start. Specificity wins. Begin with a research question that is *researchable*, *arguable*, *feasible*, and *relevant*. “How does social media affect mental health?” is too broad. “How does Instagram use correlate with anxiety symptoms in undergraduate women?” is specific enough to actually research and write about in an 8,000-word paper. Read strategically to find the gap your paper will fill and how it ties to a larger problem in the world. You’re not reading to absorb everything ever written on your topic. There’s too much research out there. You’re reading to identify what hasn’t been answered yet. Look for phrases in discussion sections like *future research should examine* or *this study was limited by*. Those sentences hand you a research gap on a silver platter. You just have to find them. | **Citation** | **Core Question** | **Method** | **Key Finding** | **Limitation/Gap** | **Connects To** | | ------------------- | ------------------------------- | ------------------------------- | ------------------------------------------ | ------------------------------ | ---------------------------- | | Smith et al. (2023) | Does X cause Y in Z population? | RCT, n=240 | Moderate effect size (d=0.52) | Small sample, single site | Jones (2021), Lee (2022) | | Jones (2021) | How does A relate to B? | Cross-sectional survey, n=1,200 | Positive correlation but causality unclear | Self-report bias, no follow-up | Current study addresses this | Create a literature matrix as you read (see example above). Set up a simple table with columns for: Author/Year, Research Question, Methods, Key Findings, and Gaps/Limitations. This takes 30 seconds per paper to fill out but saves you hours later when you’re trying to remember which study found what. Use a reference manager like Zotero to auto-generate citations so you’re not manually formatting references until the early morning hours. Develop a working thesis statement after you’ve read 10-15 core papers. A thesis statement should be a single, clear, specific, and focused sentence that presents your main argument, but is arguable (e.g., something that requires evidence and can be debated). It follows this structure: Topic + Position + Reasoning or Significance. Here’s how to find one quickly: 1. Identify your main argument: What’s the single most important claim? 2. Add your supporting pillars: What 2-3 key points support this? 3. Combine: Merge into one clear, compelling sentence. 4. Test it: Can you defend it? Is it specific enough? Does it guide your paper? This thesis will change as you write. That’s expected. But you need a preliminary statement of direction before drafting. Without it, you’ll write in circles. Basically, first ask yourself: What am I arguing, and why should anyone care? **Phase 3: Draft in the correct order and structure sections with extreme clarity** Never write linearly from introduction to conclusion. That’s how you read a paper, but not how you write one. Start with your Methods section. It’s the easiest to write because it’s purely descriptive, meaning you’re just explaining what you did. Getting 1,000 words on paper immediately builds momentum and eliminates the intimidation of a blank document. So, I always recommend to start with the Methods. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Ideally, before you do the Methods, you will have done a bit of literature review (already at the research gap/research question stage), so you can also write the Related Work (or literature review) section first in bullet-point form. Either way, you’ll need a literature review matrix for your Discussion section. Write your Results section second. Again, this is straightforward reporting of your findings after you run the experiment. No interpretation yet, just presenting data. Draft your Discussion section third. Now you interpret results, connect findings to the literature, and explain implications. You synthesize prior work and situate your results in it. This section serves to clarify your argument, which makes writing the introduction much easier. Many PhD students struggle with the discussion because it requires them to make original knowledge claims and interpret their results speculatively without being too literal or too unbelievable. This demands a level of authority and creative thinking that you might have not had to demonstrate before. Accept this. Write it anyways. Write your Introduction last. You need to know where you ended up before you can write an effective roadmap. Your introduction should accomplish five things in order: introduce the broad problem, summarize current knowledge briefly, identify the gap related to the problem, state your research question, and position your paper as the solution to that gap, which means explicitly mentioning your contribution to the field. Use standardized headings. Don’t get creative with section titles. Stick with Introduction, Related Work, Methods, Results, Discussion. (Some fields merge Introduction and Related Work, but in HCI, we like to keep these sections separate.) Reviewers and readers expect these headings. Making them hunt for information frustrates them. Don’t fall into that trap. Keep paragraphs focused on one idea. Each paragraph needs a topic sentence that states the main point, 3-5 sentences of evidence supporting that point, and a transition sentence connecting to the next paragraph. If a paragraph covers multiple ideas, split it in your edits later. **Phase 4: Iterate your rough draft through structured revision and targeted feedback** Your first draft is a starting point. Don’t envision it or treat it as a finished product. It’s cool to be messy and awkward like Urcle. Get feedback at the right stage. Don’t ask colleagues to review grammar and sentence structure when you’re still figuring out your argument. For early drafts, request conceptual feedback: Is the gap clear? Does the argument flow logically? Does the data support the claims? Always tell your reviewers exactly what you need from them and never just ask them to “review my paper, please.” Revise in two distinct passes. Pass one addresses big-picture issues: Argument clarity, section organization, paragraph flow. Pass two handles sentence-level editing: Word choice, grammar, citation formatting. Trying to do both simultaneously wastes time and mental energy. One at a time. Use your institution’s writing centre if you can. Excellent people work there. If you’re a non-native English speaker or writing in a second language, a writing centre consultation can catch idiomatic errors and clarity issues you’d never notice yourself. Most universities offer this service free to students and faculty. Build in buffer time before submission deadlines. The gap between *finished draft* and *submission-ready manuscript* is usually 2–3 weeks of proofreading, formatting, and final polishing. Never rush a deadline. It will always be a mess. Trust me. A messy publication is worse than no publication. Also, too many researchers who rush this phase produce papers that get desk rejected anyways, often even for minor formatting violations. Read your paper out loud during final proofreading (or get a text-to-speech app to do it for you, like ElevenReader). Your eyes will skip some errors that your ears will catch. It’s a great way to catch awkward phrasing, run-on sentences, and logical gaps that seem fine when reading silently. So, don’t think the researchers who publish consistently have more time or talent than you. They just have a systematic process that removes decision paralysis from every stage. Install this 4-phase framework and you’ll cut months off your paper timeline. Best of luck. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Bonus Materials Paid [Write Insight Members](https://lennartnacke.com/#/portal/signup) get a complete paper-writing system today including a 6-week schedule, diagnostic tools for identifying writing bottlenecks, a literature matrix template, a structured 90-minute block breakdown, research question filters, a quality checklist, and AI prompts for improving academic drafts, all nicely bundled in a Notion template you can duplicate. _This post is for paying subscribers only._ ### How to get your first faculty position URL: https://lennartnacke.com/how-to-get-your-first-faculty-position/ Last updated: 2025-11-05T15:11:51.000Z Last week, one of my postdocs asked me for tips on applying for faculty positions. They had strong publications, excellent teaching evaluations, and solid grant-writing experience. But after their first interview, they came back confused. “I presented my research so clearly,” they said. “Why didn’t it go perfectly?” The problem wasn’t their research. It was outstanding. But they treated the interview more like a research presentation than a job interview. Here’s what’s changed in academic hiring: with fewer tenure-track positions and more qualified candidates than ever, search committees can afford to be selective about fit. They’re not just evaluating your CV (they already know you’re qualified). They’re asking three questions: Can this person secure funding? Will they be a good colleague? Can they contribute from day one? Most candidates miss this entirely. They think it’s all about their awesome research. They prepare research portfolio answers when they should be preparing relationship-building strategies. The cost of getting this wrong? If you’re a part-time PhD student in your 50s, each failed interview represents months or years you can’t afford to waste. If you’re a postdoc researcher already, it delays launching your research program and puts you behind peers who figured this out faster. Financially, it means continued postdoc salaries instead of faculty compensation. Emotionally, it’s the exhaustion of repeated rejection when you can’t identify what went wrong. The traditional advice (“just be yourself” or “let your research speak for itself”) fails because it ignores the social complexity of faculty hiring. Departments aren’t hiring research machines. And, really, we got AI for a lot of that now. They’re hiring colleagues who will shape their culture for decades to come. So today, I’m sharing 7 specific tactics that separate candidates who get offers from those who don’t, based on 15 years of faculty hiring experience. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Here’s how to make each of these work for you: ### Likability matters more than your credentials The committee already knows you’re qualified. Hey, your CV got you in the room, Einstein. Now they’re asking a different question: “Do we want to work with this person for the next 20 years?” Enter with genuine excitement and positive energy. Make them like you. This sounds obvious, but most candidates walk in acting like they’re defending a dissertation instead of starting a new relationship. And, yes, it’s more like dating than defending. Read the temperature in the room. If the committee is formal and reserved, match their tone. If they’re casual and conversational, relax into that dynamic. Some committees want to see if you can handle pressure. Others want to see if you’ll fit their collaborative culture. Your job is to identify what they’re seeking and adjust your delivery accordingly. It takes practice. Don’t try to dominate the conversation. The candidate who talks for 10 minutes straight on every question isn’t showing expertise . They’re showing they can’t read social cues. And I meet at least one every other time we’re hiring. ### Answer each question with one clear solution When asked how you’d approach teaching a difficult course or handling a methodological challenge, give one thoughtful answer. And keep it crispy. Not three possibilities. Not a comprehensive overview of every option. One clear, confident response that shows you’ve thought about this before and can make decisions under pressure. If they want more options, don’t worry, they’ll ask follow-up questions. That’s actually good. It means they’re engaged and want to explore your thinking. Kudos. But if you start with multiple answers, you signal uncertainty or an inability to prioritize. Committees want to see project management skills embedded in how you structure your responses, too. Show you’re comfortable thinking on your feet. Pause for a moment if you need to collect your thoughts. A three-second pause followed by a clear answer beats a rushed, meandering response any day. ### Discuss funding without overcommitting to collaborations You need to know the basics about major funding opportunities in your field. Research them first. If you’re in Canada, that means understanding NSERC’s Discovery Grant and how it works for new faculty. If you’re in the US, know the NSF or NIH mechanisms relevant to your discipline (or whatever has replaced that type of funding in the current administration). You don’t need a detailed proposal ready, but you should mention a general direction: “I plan to apply for NSERC Discovery Grant funding focused on X research area.” That should be good enough. At the outset, position yourself as applying for grants individually. Once you’re established and tenured, you can explore collaborative funding. But in the interview, don’t promise too much. Don’t say you’ll definitely collaborate with Professor Guerrero Jr. on their big Blue Jays grant. You don’t know Professor Guerrero Jr. yet, and overcommitting makes you look naive. And they probably have their own little goals for a research homerun. ### Position yourself as a curious learner who’s open to collaboration Sound interested in various forms of collaboration without claiming expertise in areas outside your specialty. Coming across as a lifelong learner is the best thing you can do. Even if you secretly think you’re some kind of Ohtani, keep that ego in the closet during the interview. Say things like: “I’m curious about Professor Scherzer’s work with Dr. Yesavage on pitch calculation, I’d love to learn more about how that might connect with my research on batting average statistics.” This shows openness without pretending you already understand their entire research ballgame. Do basic research on committee members before the interview. Know Yamamoto from Freeman. You don’t need to read all their papers, but understand their general research areas. Identify where your work could naturally overlap. A little bit of preparation here lets you ask informed questions and make genuine connections during your interview. Super valuable. Avoid appearing too narrowly focused. If you only talk about human-computer interaction and dismiss other computer science subfields, you signal you won’t be a collaborative colleague. You’re a lone wolf, but the committee is looking for someone to howl at the moon with them. Show intellectual curiosity beyond your immediate research niche. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ### Ask targeted questions that show genuine preparation Your questions should demonstrate you’ve thought about practical realities of starting a research program. Ask about lab space and the process for setting it up. That’s always a winner. Inquire about support mechanisms for grant applications . Do they have research development staff who review proposals? What’s the timeline for accessing startup funds and how can you spend them? These questions show you’re already thinking like a faculty member that is ready to push the boat forward. Don’t be just a candidate trying to get through the interview. Ask about teaching expectations and how they balance with research time, especially in your first year. This demonstrates strategic thinking. You’re already planning how to maximize impact in both domains from day one. Prepare questions that show awareness of the institution’s structure. If it’s a new program, ask about growth plans and how your position fits into that vision. And also, how does grad supervision work? ### Use formal titles until explicitly told otherwise Address committee members as “Doctor” or “Professor” at the start of the interview (or if you have the exquisite pleasure of interviewing in Germany, you can just waffle out the whole “Herr Professor Doktor Herzensbrecher-Schleifenheimer”). That’s just common practice. And in some countries, we really dig those titles (because we worked hard for them, you know). Some committee members will immediately say “Please, call me Lennart.” Others will keep things formal throughout. Let them set the tone. Starting formal and being invited to be casual is much better than starting casual and realizing you’ve misjudged the culture. And to quote Oscar Wilde here: “the only way to atone for being occasionally a little overdressed is by being always absolutely overeducated.” If you’re unsure about someone’s title or preference, it’s completely acceptable to ask: “How would you prefer I address you?” This shows respect and awareness. And maybe even a little bit of a vulnerable opening. Either way, it’s not weakness. ### Remember you’re evaluating them too This isn’t just them hiring you. It’s you choosing them. You’re deciding if this is where you want to spend the next decade or more of your career. We don’t get a lot of decades in our life. So, let’s be conscious about this choice. Pay attention to how committee members interact with each other. Do they seem collegial? Are there obvious tensions? How do they describe the department culture? Feel out the vibe. Notice what questions they ask. Are they genuinely curious about your research, or just checking boxes? Do they seem excited about the possibility of you joining, or just going through the motions? Do you feel at home in the discussions? Focusing your mindset around your evaluation also helps you stay calm. When you’re not desperate for any offer, you interview better. Abundance thinking is always better than scarcity thinking. You’re more authentic, less performative, and more likely to have natural conversations that reveal genuine fit on both sides. Take notes after the interview about what felt right and what raised concerns. If you get multiple offers, these observations will help you make the best decision for your career trajectory. The difference between candidates who get offers and those who don’t often comes down to mastering these specific skills. Approach each interview knowing you’re evaluating them as much as they’re evaluating you. This mindset shift alone will make you more authentic and ultimately more successful. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. [​Use the preparation checklist and answer templates I’ve provided for paid Write Insight members before your next interview, and you’ll save yourself years of trial and error that most early-career researchers waste.](https://lennartnacke.com/#/portal/signup) P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## New Faculty Position Interview Guide _This post is for paying subscribers only._ ### This 10-Minute Trick Cut My Reading Time by 80% URL: https://lennartnacke.com/this-10-minute-trick-cut-my-reading-time-by-80/ Last updated: 2025-11-05T15:12:29.000Z Reading academic papers shouldn’t take 4 hours per paper when you’re juggling a full-time job, family responsibilities, and a PhD. Last week, a paid subscriber emailed they don’t take getting this advice for granted. They’ve tried my rebuttal and polite decline phrasebook for peer review from Notion and said: “it worked great for me with my ADHD.” One thing I often forget is how useful some of the tools in my paid newsletter are for people with all kinds of academic challenges. I’m glad to see them help people. Yet most part-time doctoral students waste entire weekends trying to thoroughly read every paper in their literature review. You highlight every paragraph. You take detailed notes on every section. You try to memorize the methodology. And by Sunday night, you’ve read 3 papers but you’re exhausted, your family barely saw you, and you still have 47 more papers to review before you can finally write your related works section for your paper. The reality is that reading papers this way doesn’t just burn your limited time. It keeps you stuck in coursework mode when you should be in researcher mode, which delays your publication (and possibly graduation) by months or even years. So today, we’re looking at the S³ loop, a simple 3-step system that lets you extract the core value from any academic paper in under 10 minutes. And I promise to keep it straightforward for you. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 1\. Scan the paper for its core argument Your goal in the scan phase is to answer one question in under 3 minutes: *What does this paper actually claim to contribute?* Start with the abstract. Read it once, straight through, without highlighting or taking notes. You’re looking for the research question and the main finding. Most abstracts follow a predictable structure: background context, research gap, what the study did, what the study found, and why it matters. The findings and contributions usually appear in the third or fourth sentence. Next, jump to the figures and tables. Academics love to bury their most relevant evidence in visuals because these take the most work to create. A well-designed figure tells you what the author thinks is the strongest proof of their argument. Look at the figure captions first. If they’re well done, they summarize what you’re supposed to see. Then examine the figure itself for patterns, differences, or trends that support the paper’s claim. Finally, read the first and last paragraph of the discussion section. The first paragraph usually restates the main finding in context. The last paragraph usually addresses limitations and future research directions. These two paragraphs tell you what the author actually proved versus what they hoped to prove. You now have the paper’s core argument in your head. Now, write it down. This process often only takes 2-3 minutes per paper, and you can scan 20 papers this way in an hour. ## 2\. Select one section that challenges or extends your current understanding After scanning, most papers will either confirm what you already know or offer something new. You’re, of course, looking for new stuff. Cause research. If the paper confirms your existing knowledge, you’re done. Add it to your reference manager with a two-sentence note about its main finding and move on. You don’t need to read the entire paper just because it exists. (That being said, if the paper ever piques your curiosity, you can, of course, read it anyway. We’re just trying to be efficient here.) But if the paper presents something that contradicts your assumptions, introduces a new method, or applies a familiar concept in an unfamiliar context, that’s when you select one section to read in detail. The method section is usually the best choice for this deeper read. Methods reveal how the researchers actually found their results, which helps you evaluate whether their conclusions are justified. A paper might claim that “intervention X improves outcome Y,” but the method section might only document that they tested X on 12 undergraduate students over one short session, which dramatically changes how you should interpret their findings. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Read your selected section twice. First pass is for comprehension: What did they do? Second pass is for evaluation: Does this approach actually test what they claim it tests? Most methodological flaws become obvious on the second read. Skim other sections briefly if things are still unclear. This step usually takes 5-10 minutes per paper, but you’re only doing it for papers that matter to your research. ## 3\. Synthesize the key insight by writing 3 sentences in your own words The final step converts your reading into usable research knowledge. It makes the reading of the paper valuable to you. Open your reference manager or literature review document and write exactly three sentences about the paper. First sentence: What the paper claims. Second sentence: How the paper supports that claim. Third sentence: How this paper relates to your research question. For example: “This paper reframes systematic review quality in HCI through an umbrella review methodology, which shows that CHI systematic reviews systematically underreport search strategies, quality assessment procedures, and synthesis methods compared to PRISMA standards. The paper provides domain-specific guiding questions and best practices for a practical intervention to improve methodological transparency without imposing rigid biomedical review standards incompatible with HCI’s methodological pluralism. If I conduct a systematic review on motivation factors in VR adoption and sustained use, this paper provides the quality checklist and reporting standards to ensure my review meets reproducibility benchmarks and avoids the search strategy and synthesis gaps common in HCI reviews.” (Here’s the reference for the example paper: Rogers, K., Hirzle, T., Karaosmanoglu, S., Toledo Palomino, P., Durmanova, E., Isotani, S., & Nacke, L. E. (2024). [An umbrella review of reporting quality in CHI systematic reviews: Guiding questions and best practices for HCI.](https://doi.org/10.1145/3685266?ref=lennartnacke.com) ACM Transactions on Computer-Human Interaction, 31(5), Article 57.) The act of writing forces synthesis. You can’t write three clear sentences about a paper unless you actually understood it. Yes, you could use AI for this, but it will prevent you from actually synthesizing the paper in your own head. And if you can’t write three sentences after scanning and selecting, that usually means the paper isn’t relevant enough to include in your related work (based on your current research question). These three sentences become the building blocks of the related works literature review for your current paper. When you sit down to write, you’re not starting from scratch , but it lets you assemble your pre-written summaries into a coherent narrative. A 50-paper literature review becomes a 150-sentence drafting exercise instead of a terrifying blank page. This step usually takes 3–5 minutes per paper (or a little longer if you’re new to this), but it saves you 10+ hours when you start writing your actual literature review chapter. The complete S³ loop takes 10-15 minutes per paper once you’ve practice this a little. You can process 4-6 papers per hour, which means you can review 30-40 papers in a single Saturday morning. This leaves your afternoon free for family time or the other parts of your life that matter. Most PhD students don’t understand yet that reading papers and understanding papers are two different skills. Reading is a skill that requires time that most part-time PhD students don’t have enough of, and understanding a paper requires a system that you can execute quickly. The S³ loop is such a system. When you scan before you read, select before you commit, and synthesize before you move on, you stop wasting time on papers that don’t advance your research. You focus. You build your knowledge of related work incrementally instead of all at once. And you submit your paper (or defend your PhD) months earlier because you’re not stuck in endless reading loops that get you nowhere. Start with your next paper. Set a timer for 3 minutes and scan. If it’s relevant, select one section and read it. Then write your three sentences. Do this 10 times and you’ll have processed 10 papers in under 2 hours. something that would have taken you an entire weekend using your old approach. Most reviewers really don’t care how many papers in your field you’ve read. They only care about how well you understand the literature so that you can situate your work in it properly. You want to know where your research fits. The S³ loop gives you that understanding without sacrificing your weekends, your family time, or your sanity. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Try it out and let me know how it works for you. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Content Paid subscribers this week get the exact Notion template, ChatGPT prompts, and checklists I used to read papers faster with the S³ loop. _This post is for paying subscribers only._ ### My 90-Minute Paper Rescue Routine URL: https://lennartnacke.com/my-90-minute-paper-rescue-routine/ Last updated: 2025-10-18T12:52:05.000Z Your paper’s due tomorrow. And you just realized it’s terrible. Confidence: gone. Panic sets in. Maybe your supervisor is waiting for the draft. Maybe the conference deadline is midnight. Maybe you promised your co-authors you’d submit tonight. Whatever the reason, you’re staring at a document that needs serious help. Emergency heart surgery. And you have almost no time. The problem isn’t that you’re a bad writer. You’ve put in your time in the writing den. You know how to report stuff. You just don’t have the academic experience to feel confident right now. The problem is that weak papers share three predictable flaws: a muddy contribution statement, disconnected sections, and evidence that doesn’t land. These issues make reviewers swipe left for a hard reject within the first page. When they can’t figure out what you’re arguing for or why it matters in 60 seconds, your paper gets categorized in their head as not ready before they even reach your methods section. (If they make it there that is.) So in this issue, I’m going to show you the exact triage system I use to rescue papers under extreme time pressure for [my coaching clients](https://learn.lennartnacke.com/coaching-call/?ref=lennartnacke.com) and the specific edits that make reviewers think you spent weeks revising when you really spent 90 minutes. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Ready for some open-heart paper surgery? #### **Run the 20-minute weakness scan on your draft** Open your paper and read only the abstract, the last paragraph of the introduction, and the first sentence of your discussion. If you can’t clearly state your contribution in one sentence after reading these three sections, you have a severe contribution problem. Write down on a sticky note: “Contribution unclear.” Next, open your paper to the middle, pick any random section, and read just the first sentence of each paragraph for two pages. If these sentences don’t build a logical chain where each idea flows into the next, you have a structure problem on the inside. Write down on a sticky note: “Structure broken.” Finally, find your three most important claims. For each claim, check whether you provide specific evidence (numbers, quotes, examples) within two sentences of making that exact claim. If the evidence is missing, vague, or buried five paragraphs later, you have an evidence problem. Write down on sticky note: “Evidence weak.” Now, stick them to your monitor for dramatic effect. Linger a bit in sorrow. Most papers dying tomorrow in peer review have at least two of these three problems. And I’m sorry, but you won’t be able to fix all of them tonight, it’s way late, so we’ll assume you have 90 minutes for the rest of this. #### **Fix the highest-leverage weakness first, then stop** You cannot fix everything tonight. The triage rule is simple: contribution problems kill papers fastest, so fix your **contribution** first. If your contribution is unclear, reviewers reject the paper without reading it further. Let me say that again: Communicating your contribution well is key to getting your paper accepted. It must make the contribution clear. Ideally on page one. Now, spend 20 minutes rewriting your abstract and the final paragraph of your introduction to include one crisp sentence that states something like: “We show X, which matters because Y.” Use the formula: “Unlike prior work that assumed A, we demonstrate B using method C, revealing D.” That’s your contribution. Boom. The literature was limited. So, you found something new. And now you’re slapping them in the face with it, so they can’t miss it. Put it in both places. If your contribution is already clear, move to **structure** problems. Structure problems make reviewers work too hard, which makes them annoyed, which makes them write something like *lacks coherence* in their review. Let’s avoid that. Spend 35 minutes on backward outlining (I’ll explain this more below): go through your paper and write down the first sentence of every paragraph on a separate document. [The topic sentence if you will](https://youtu.be/ErmYTPN23YI?ref=lennartnacke.com). Read that list. If the sentences don’t tell a complete story on their own, rewrite them until they do. Each first sentence should connect to the previous one and preview what’s coming. If both contribution and structure are solid, move to **evidence** problems. Evidence problems make reviewers yell that your claims need support. So, let’s spend 20 minutes adding one specific number, quote, or example right after your three main claims. That’s gotta do for now. Stop after fixing one problem properly if you’re under extreme pressure. A paper with one weakness fixed is better than a paper with three half-fixed weaknesses. #### **Add four surface edits that signal serious revision** After your main fix, you have 15 minutes left. Use them strategically. First, rewrite your title to include your key result. Reviewers assume papers with vague titles are sloppy. So, let’s be specific. Change “An Analysis of X” to something specific like “X Reduces Y by 30% in Z Contexts.” Second, add transition phrases at the start of each major section. Write: “Having established A, we now turn to B.” Reviewers skim these transitions and subconsciously register that you planned the structure. Third, check that every table and figure has a one-sentence interpretation in the text within the same page that references the figure or table. Reviewers get annoyed when they encounter a table with no explanation nearby. Write: “Table 2 shows that X exceeds Y.” Fourth, scan your introduction for passive hedging phrases and delete them. Replace “It has been suggested that X may potentially influence Y” with “X influences Y.” These four edit should take 15 minutes total and make your paper feel substantially more polished. Reviewers can’t articulate why, but they score papers higher when these details are clean. And, I’m telling you this will read more smoothly, too. #### **Use backward outlining to catch coherence breaks fast** You’ve probably heard of outlining before writing. Backward outlining is different: you outline after writing to see what you actually wrote. Here’s the emergency version. Open a blank document. Go through your paper paragraph by paragraph and copy the first sentence of each paragraph into your blank document. You should now have a list of 30–50 sentences. Read the list without looking at the original paper. Does it tell a coherent story? Can you follow the argument using only these sentences? If you can’t, your paragraphs aren’t doing their job. There is no story. I use something called the **reader’s journey framework** to help my students write a story that flows in their papers: Confusion (What’s the problem?) → Curiosity (Why does it matter?) → Evaluation (How did you solve it?) → Discovery (What did you find?) → Integration (So, what now?) You go from gap to problem to significance and stakes to your methods to key results, and then to implication and contribution. Each paragraph’s first sentence should function as a mini-headline that orients the reader and connects to the previous paragraph. If you spot a break in logic, where sentence 14 doesn’t connect to sentence 15, for example, go back to the paper and rewrite the opening of paragraph 15 to bridge that gap. It’s an easy way to discover structural problems with your manuscript that are invisible when reading the full paper because you’re distracted by all the supporting details. I’ve rescued dozens of papers this way in under just under two hours. #### **Stop revising at midnight and submit it** The hardest part of emergency revision is knowing when to stop. Papers aren’t rejected because they’re 85% instead of 95% polished. Papers get rejected because they’re unclear, incoherent, or unsupported. If you fixed your contribution statement, your structure, or your evidence tonight, you’ve addressed the fatal flaws. Your paper may yet see the light of day. The remaining issues though, like awkward phrasing, formatting inconsistencies, minor citation problems, matter far less than you think. Yet, should be addressed before your camera-ready version. Reviewers expect a few rough edges in a submissions. They do not expect to struggle to understand what you’re arguing or why it matters. Set an alarm for midnight. When it goes off, submit the paper. Revisions have diminishing returns after the first 90 minutes of focused work, and exhausted editing at 3am creates new problems faster than it solves old ones. I’ve done my fair share of all-nighters to know this. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Your paper is now better than it was some hours ago. That’s enough. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Notion with ChatGPT Prompts _This post is for paying subscribers only._ ### How to Write Discussion and Conclusion Sections That Get Papers Accepted URL: https://lennartnacke.com/how-to-write-discussion-conclusion-sections-that-get-papers-accepted/ Last updated: 2026-01-27T18:16:55.000Z Most early-career researchers think the hardest part of writing a paper is the methodology or results section. But while rigour is really important for research papers, the truth is, most researchers get trained in getting rigour right during grad school. But nobody trains them to write a great argument. So, for early-career professors, more papers get rejected because of weak Discussion and Conclusion sections than any other reason. Reviewers can spot a confused researcher from the first paragraph of these sections. They know immediately whether you understand your own research contribution or if you’re just throwing words on a page like Spaghetti. The difference between acceptance and rejection often comes down to how clearly you can interpret your findings and articulate your contribution. So, let’s discuss the 5 simple ways to write Discussion and Conclusion sections that get papers accepted at top-tier journals or at those kickass conferences that we cherish and love in my research field (HCI). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Write your discussion like a courtroom argument Your discussion section needs to make a case for why your findings matter, and the strongest discussions follow a logical argument structure. Start with your strongest evidence first. Present what your results actually mean in the context of your research question. Then systematically build your case by connecting your findings to existing literature. Show where your results align with previous work, where they diverge, and why those differences matter. Address the obvious counterarguments before reviewers raise them. If your results contradict established theory, explain why. If they support existing findings, explain what new insight you’re adding. Think of reviewers as skeptical jurors who need to be convinced your research matters. The weakest discussions jump randomly between different implications without building a coherent argument. You want to build the latter. ## Keep your conclusion focused on one key message While the discussion is a full-on argumentation for your research, your conclusion should answer one question clearly: what is the single most important thing readers should remember about your research? Everything in your conclusion should support that central message. Start with a direct answer to your research question in 1–2 sentences. Then state your core contribution in language a non-expert could understand. End with one forward-looking sentence about what this means for the field. Resist the urge to rehash everything from your discussion or introduce new ideas. Reviewers read conclusions to understand your bottom line, not to revisit your entire study. A scattered conclusion signals a researcher who doesn’t understand their own contribution. Think of your conclusion as your elevator pitch to the journal editor or the papers chairs of the conference. ## Use the inverted pyramid structure in your discussion I love journalistic technique. So many fun things to learn about writing from a good journalist. Anyways, I digress. Journalists use the inverted pyramid to put the most newsworthy information first, and your discussion should follow the same logic. Don’t dick around. Hit ’em in the face with the biggest punch you got. Channel your inner Derrick Lewis. Lead with your most significant finding and its implications. Follow with supporting evidence and connections to literature. End with limitations and future research directions. This structure serves two purposes: busy reviewers can grasp your main contribution even if they just skim (which they do when they first read your paper), and it forces you to prioritize your most important insights in clear language. Many researchers bury their key contributions in the middle of long discussions, making reviewers work to find the value. Don’t make anyone work for your goodies. Your first paragraph should make a reviewer think, “Wow, this is interesting” rather than “Where is this thing going?” Save the methodological limitations and future research suggestions for the end of your Discussion. ## End your conclusion with forward momentum Many early-career researchers get this wrong and end everything with a concise sum-up of what they did. And consequently bore reviewers to death. A little more Harry Potter, a little less Bella Swan, please. The last sentence of your paper shapes how reviewers **feel** when they finish reading, and you want them feeling energized about your contribution. You want to open up a whole world of thoughts to them. Avoid ending with phrases like “In summary” or “To conclude” followed by a rehash of what you already said. You are not a writing robot. Instead, end with a single sentence that points forward. Describe the most important implication for practice, theory, or future research. Make it specific enough to be actionable but broad enough to matter beyond your narrow research context. For example: “These findings suggest managers should reconsider how they structure remote teams during organizational crises” creates forward momentum. “This study shows that remote work affects team performance” just restates what everyone already knows. And, hey, you’re wasting my time if you are telling me again what I already read. That’s just rude. Your final sentence should make readers want to cite your work. Make it tasty. ## Write your discussion first, then extract your conclusion Most researchers write their conclusion as an afterthought, which explains why so many conclusions feel disconnected from the actual research contribution. And you’re just plain boring there, Piper Chapman. Start by writing a deep discussion section where you fully explore what your findings mean. Work through all the implications, connections to literature, and theoretical contributions. Then step back and identify the single most important insight from that entire discussion. That insight becomes the foundation of your conclusion. Extract the essential elements (i.e., your core finding, your main contribution, and your key implication) and build a tight 3-6 sentence conclusion around them. A conclusion should reflect your best thinking, not a rushed summary under deadline pressure. Your conclusion should just feel like the natural culmination of your discussion. It should wrap things up tightly. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Materials Today, we have a complete framework for writing a Discussion section for a paper, including detailed guides for the 7 paragraphs it should include (at minimum), plus an LLM prompt to get your first draft done in seconds. _This post is for paying subscribers only._ ### How I Turned Rejections Into Acceptances URL: https://lennartnacke.com/how-i-turned-rejections-into-acceptances/ Last updated: 2025-10-02T09:50:32.000Z Getting your paper rejected despite writing a rebuttal feels like academic death. Still does to me after many years in the game. Often I spend weeks on my response, address every single comment, and explain my methodology in excruciating detail. Then the editor sends back another round of revisions, or worse, a rejection. I faced it, you’re facing it. The problem isn’t your research quality. Most PhD students and early-career researchers simply don’t know how to structure rebuttal responses that actually persuade reviewers to accept their papers. You end up in revision limbo for months, your graduation timeline gets pushed back, and your research program stalls before it even starts. So today, I’m going to show you the 5 rebuttal strategies that get papers accepted, even when reviewers seem impossible to please. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **Map every reviewer comment to a numbered response with precise locations** Start your rebuttal by creating a master tracking system that treats each comment like a legal case. Paraphrase every reviewer point in one sentence, then label it R1–1, R1–2, R2–1, etc. This prevents you from accidentally missing comments or double-responding to the same issue. Most importantly, you need a consistent structure for each response that follows this exact pattern: acknowledgment → interpretation → decision → rationale → pointer. For example: “Thank you for the suggestion regarding our sample size calculation. We understand this as a request to provide a power analysis. We have accepted this request because power calculations strengthen methodological transparency. We added a power analysis showing 80% power to detect medium effects. See Methods section, Page 8, Lines 234–245, Table 2.” Reviewers can scan your responses and see what changed where without searching through your manuscript. ## **Lead with acceptance when possible, but decline with principled reasons and alternatives** Accept every reasonable request and state exactly what you changed. When accepting suggestions, use the reviewer’s language and cite specific locations. Write “we added the requested control variables (see Table 3, columns 4–6) and robustness checks using alternative specifications (see Supplement Section A.3)” instead of “we addressed your concern about methodology.” When declining, base your refusal on preregistration constraints, estimated choice, design limitations, ethical considerations, or word limits. Offer a constructive alternative: robustness check, supplemental detail, exploratory analysis, or future study plan. Reviewers won’t feel dismissed and will see you value their expertise. ## **Anchor every decision in shared academic standards and maintain transparency** Base revision decisions on external authorities, not personal preference. Reference methods textbooks, reporting guidelines like CONSORT or APA, or established journal precedents when justifying choices. Separate preregistered analyses from exploratory work clearly. Maintain a change log linking every modification to a specific reviewer comment. Provide exact page numbers, line numbers, figure references, and table locations. For example: “Following APA reporting guidelines, we retain the original analysis plan while adding the requested sensitivity analysis in Supplement Table S4\. This preserves the distinction between confirmatory (preregistered) and exploratory analyses as recommended by Nacke et al. (2025).” Reviewers respect decisions grounded in methodological standards. ## **Fix misunderstandings through better signposting instead of blaming reviewers** When reviewers misinterpret your work, the problem is usually unclear communication, not reviewer incompetence. Strengthen your definitions at first mention, add forward references that guide readers to key results, and revise figure captions to carry the main takeaway. Add schematics or directed acyclic graphs if they reduce ambiguity about causal relationships. Use neutral language that thanks reviewers for highlighting areas needing clarification. Replace defensive responses like “as we clearly stated in Section 2” with constructive ones: “Thank you for this question, which gives us an opportunity to clarify our approach. We have added a definition of our key construct at first mention (Page 5, Line 127) and expanded the figure caption to emphasize the main finding (Figure 2).” This fixes the communication problem instead of defending your original text. ## **Resolve conflicting reviewer requests by stating your governing principle** When reviewers pull you in opposite directions, choose the path that preserves study integrity and name your principle explicitly. Identify the tension (for example: R1 wants more statistical controls while R2 wants a simpler model), state the principle you’re optimizing (design integrity, theoretical fit, or causal identification), and choose one approach for the main text while documenting alternatives in supplementary material. Write something like: “We appreciate the differing recommendations from R1 and R2 regarding model specification. Because our preregistered research question focuses on causal identification, we adopt the parsimonious specification (R2’s suggestion) in the main analysis and provide the extended model (R1’s request) in Supplement Table S3.” This demonstrates thoughtful decision-making rather than arbitrary choices or attempts to please everyone simultaneously. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Bonus Content Today, we have the ultimate rebuttal and polite decline phrasebook for peer review letters in Notion, a rebuttal response structure template, a rebuttal quality checklist, and a ChatGPT prompt to optimize the tone of your rebuttal. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). _This post is for paying subscribers only._ ### How to do a power analysis quickly URL: https://lennartnacke.com/how-to-do-a-power-analysis-quickly/ Last updated: 2025-09-25T09:45:15.000Z I remember my first empirical paper like it was yesterday. I had a perfectly valid methodology. I ran a study that made me feel accomplished with a sample that I thought was representative. But I was completely new to statistical methods and empirical research, so, of course, I forgot that I needed to be clear about sample size. And I did not understand the power in my sample. The sinking feeling hit me when R2 asked about my sample size justification and whether I had done a power analysis. A power what now? Most PhD students spend weeks reading statistical theory, watching YouTube tutorials, and second-guessing themselves about power analysis. They buy expensive statistics software, hire coaches, and still feel uncertain when defending their methodology. Meanwhile, your publication sits untouched while you spiral through [Cohen’s 1988 textbook](https://utstat.utoronto.ca/~brunner/oldclass/378f16/readings/CohenPower.pdf?ref=lennartnacke.com) for the third time. The worst part? Your reviewers just want to see you followed standard practices. They’re just checking a box, not testing your statistical prowess. So, just like I felt an unburdening of worry and the quiet elation that came with it when I knew my statistical efforts were meaningful and correct, I want to show you the exact 5-minute process that gets your power analysis done without the statistical nightmares. And you, too, can feel a sense of pride and relief that the long road of research and writing has led to a successful conclusion with your next publication. Let’s get this done (fair warning, I’m not a statistician). ## Step 1: Download the only free tool you need [First, you need to download the free tool G\*Power.](https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower?ref=lennartnacke.com) This software has been the gold standard for power analysis since 2007\. Universities worldwide accept it. Your reviewers know it. The interface looks dated because it hasn’t changed in years, and that’s actually perfect for us. Install it on any computer (Windows, Mac, or Linux). The entire program takes up less space than a single PDF textbook. No subscription fees, no hidden costs, no complicated registration process. Once installed, you’re 4 minutes away from a completed power analysis. ## Step 2: Navigate directly to the right calculation Open G\*Power and click these exact options (unless you already know what you’re doing and have done more than a simple comparison of two variables): - Test family: “t tests” - Statistical test: “Means: Difference between two independent means (two groups)” - Type of power analysis: “A priori: Compute required sample size” These selections work for 90% of beginner studies comparing two groups (but again, if you need to know more, ask you local statistician to explain the details to you). If you’re doing correlational research, select “Correlation: Bivariate normal model” instead. For ANOVA with multiple groups, choose “F tests” then “ANOVA: Fixed effects, omnibus, one-way.” You can run these statistical tests without learning the underlying mathematical theory. Match your research design to the appropriate test option. ## Step 3: Enter these four standard values Type these exact numbers: - Effect size d: 0.2 - α err prob: 0.05 - Power (1-β err prob): 0.80 - Allocation ratio N2/N1: 1 ![](https://lennartnacke.com/content/images/2025/09/G-Power.webp) Cohen established 0.5 as a medium effect size in 1988, creating the field’s default standard. But a recent [meta-analysis by Gignac and Szodorai (2016)](https://psycnet.apa.org/record/2016-41819-016?ref=lennartnacke.com) found that Cohen’s benchmarks are arbitrary (small: 0.20, medium: 0.50, large: 0.80) and significantly overestimate typical effect sizes in the psychological literature. The authors recommend using 0.10, 0.20, and 0.30 as new guidelines for small, typical (median), and large effects in psychological research. If you use 0.5 as a typical effect size, you’re overestimating the norm by a wide margin (only a tiny fraction of effects reach that level in their meta-analytic data). For power calculations or interpreting magnitude, the median is closer to 0.2 for Pearson correlations. Fisher introduced the 0.05 alpha level (Type I error probability) in 1925\. Statistical power of 0.80 became the standard in the 1970s. Equal allocation between groups maximizes statistical efficiency with a 1:1 ratio. A word about the standard statistical power threshold of 0.80 though: it means researchers aim for an 80% chance of detecting a real effect if it exists. However, [newer research shows that actual effect sizes are typically much smaller](https://www.sciencedirect.com/science/article/pii/S016028962400028X?ref=lennartnacke.com#f0010) than researchers once expected. For interactions between variables, you should expect regression coefficients (β) or Cohen’s f² values around 0.01 to 0.10\. For main effects (meaning: the direct relationship between variables), correlation coefficients (r) typically range from 0.1 to 0.2. Studies using the traditional 0.80 power threshold miss smaller effect sizes common in psychology research. Most experiments expect effect sizes around d = 0.50, but actual effects often measure d = 0.20 or smaller. When researchers design studies for large effects that don’t exist, their sample sizes become inadequate. A study powered to detect d = 0.50 with 64 participants per group drops to just 33% power when the true effect is d = 0.20\. Detecting these realistic smaller effects requires sample sizes of 400+ participants per condition, which now forces researchers to rethink their resource allocation and collaboration strategies. For GPower power analysis, [Lakens (2013) recommends](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2013.00863/full?ref=lennartnacke.com) to use Cohen’s *dz* for within-subjects (paired) comparisons and Cohen’s *ds* (sometimes labeled simply as d) for between-subjects designs. GPower uses *dz* as the default effect size for within-subject t-tests. If all of this experiment design lingo sounds scary to you, I recommend starting with the wonderful book: [How to Design and Report Experiments by Andy Field and Graham Hole](https://uk.sagepub.com/en-gb/eur/how-to-design-and-report-experiments/book219351?ref=lennartnacke.com). The best approach is to use effect sizes from previous research in your research area if available, and only fall back on the classic Cohen guidelines if you genuinely have no better reference. Even then, spell out that you’re using Cohen’s defaults as a last resort, not as strict standards for what counts as practical significance. Just something to keep in mind as the standards here may vary for your field (my field, HCI, is pretty lackadaisical about this standard). Click “Calculate” and your required sample size appears instantly in the output window. (In the example screenshot above it would be 620 with two samples of 310\. And gosh, that’s a lot of people, so you get why most people prefer 0.3 or larger as their effect size, because it cuts that number down more than half or 278 people in total. Put it at 0.5 and you only have to study 102 people.) ## Step 4: Add 20% for the real world Whatever number G\*Power gives you, multiply by 1.2. If G\*Power says you need 64 participants (32 per group), your target becomes 77\. If it says 128, aim for 154\. This buffer accounts for dropouts, incomplete data, and failed manipulation checks. Committee members (and your supervisor) recognize this preparation demonstrates practical research experience. Round up to the nearest convenient number. Nobody recruits exactly 77 participants. Make it 80\. Clean numbers look more professional in your methodology section. But don’t beat yourself up if you don’t meet your goal. At the end the total sample size determines the minimum number you need for your experiment. ## Step 5: Write these two methodology sentences Copy this paragraph exactly (if you’re doing a simple A/B comparison and your results follow a bell curve or normal distribution), and just fill in your numbers: > A priori power analysis using G*Power 3.1 indicated a minimum sample size of* \[GPOWER NUMBER\] participants would be needed to detect a median effect size (d = 0.2) with 80% power and an alpha level of .05 for comparing two groups. To account for potential attrition, the target recruitment was increased by 20% to \[YOUR INFLATED NUMBER\] participants. That’s your entire power analysis section for a simple beginner study. Done. No complex equations. No theoretical justification. No lengthy explanations. ## Handle the only two reviewer questions you’ll get Reviewers usually ask two predictable questions about power analysis. And it’s usually when you put your effect size somewhere higher between 0.3 and 0.5 (which most researchers do for practical reasons, because most labs cannot afford to run large scale studies). For following, we are assuming an effect size estimate (like 0.3) that’s not conservative but also not outrageous. Here are your word-for-word responses: ### Question 1: Why did you choose your effect size? > Cohen’s 1988 guidelines established 0.5 as a conventional medium effect size, representing a difference that would be practically meaningful in applied settings. A recent [meta-analysis by Gignac and Szodorai (2016)](https://psycnet.apa.org/record/2016-41819-016?ref=lennartnacke.com) confirmed that effect sizes in psychological research typically range from 0.1 to 0.3, making 0.3 a reasonable estimate for our study. ### Question 2: What if your actual effect size is smaller? > If the true effect size is smaller than anticipated, this study would be underpowered to detect it. However, effects smaller than d = 0.3 may lack practical significance even if statistically detectable. The chosen effect size balances statistical power with practical relevance, consistent with recommendations by [Lakens (2013) for applied research](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2013.00863/full?ref=lennartnacke.com). Memorize these responses. They end most reviewer discussions quickly. ## Skip everything else the textbooks tell you You don’t need sensitivity analyses. You don’t need power curves. You don’t need to calculate post-hoc power (which statisticians actually hate). You don’t need to justify your alpha level or explain Type I versus Type II error rates. (I mean unless your thesis or paper is actually in statistics, then go hog.) Your committee has seen hundreds of dissertations. Your reviewers have seen at least dozens of other papers. Must just know the standard approach. Following convention gets you approved faster than trying to impress them with statistical sophistication, but keep in mind the finer points and studies I’ve mentioned here. The entire process (from downloading G\*Power to writing your methodology section) takes 5 minutes. Less time than reading the introduction of a statistics textbook chapter. ## Use this approach for these common designs **For correlational studies:** Select “Correlation: Bivariate normal model” and use r = 0.30 as your effect size. This gives you 84 participants minimum, or 101 with the buffer. **For three-group comparisons:** Choose “ANOVA: Fixed effects, omnibus, one-way” with f = 0.25 effect size and 3 groups. You’ll need 159 participants total, or 191 with buffer. **For regression with 5 predictors:** Pick “Linear multiple regression: Fixed model, R² deviation from zero” with f² = 0.15 and 5 predictors. Result: 92 participants, or 111 with buffer. **For repeated measures:** Select “Means: Difference between two dependent means (matched pairs)” with d = 0.5\. You’ll need 34 participants, or 41 with buffer. Each calculation takes 30 seconds once you know where to click. And while these may not be the most conservative estimates, they likely get your study rolling. ## Start your power analysis right now Your dissertation methodology chapter needs this completed. Your proposal defence requires it. Your ethics application asks for it. Open your browser. [Download the free tool G\*Power.](https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower?ref=lennartnacke.com) Run your calculation. Write your two sentences. Move on to actually collecting data. Every day you spend reading about power analysis theory is a day you’re not finishing your dissertation. Or not publishing your papers. Get it done in 5 minutes. Today. ## Bonus Materials Today, we have a power analysis quick reference template (and Notion cheat sheet), a committee response cheat sheet for your defence, and some sample size calculator shortcuts. _This post is for paying subscribers only._ ### The Brutal Truth About Rigour in HCI URL: https://lennartnacke.com/the-brutal-truth-about-rigour-in-hci/ Last updated: 2025-10-22T01:08:49.000Z Your paper got *rejected* again from your favourite high-quality venue, and you’re wondering *why*. Let me tell you the brutal truth. In my field, human-computer interaction (HCI), most papers fail peer review because they lack methodological rigour. It’s everyone’s favourite reason for rejection. Many junior researchers make the mistake to think they can just fix this by working harder, when in reality, they just have to fully understand first what rigour actually means to HCI reviewers. Most researchers think rigour means being thorough, but that’s only about 20% of the equation. The real definition? Rigour is the systematic application of methodological principles that guarantee your research is credible, trustworthy, and reproducible. Without it, even trailblazing innovative ideas get relegated to the rejection pile, your h-index stays flat, and that tenure clock keeps ticking louder and louder. #### What Is Rigour in HCI Research? ***Rigour in HCI** is the systematic application of methodological principles that ensure your research is credible, trustworthy, and reproducible. Unlike simply following checklists, rigour in HCI requires ****methodological sophistication** that demonstrates you understand the complexity of human-centred research. ****Key Definition** Rigour = Credibility + Transparency + Reflexivity + Context-Sensitive Quality To help you combat that frustration, I’m showing you 7 concrete ways to inject rigour into your next paper, the same tactics that help me and my team consistently publish 3–5 high-quality papers in top-tier venues every year. Let’s check it out. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 1\. Start with a positionality statement for reflexivity that acknowledges your biases upfront Most researchers skip positionality or reflexivity statements unless they’re doing marginalization research, but that’s a rookie mistake that signals methodological naivety to HCI reviewers. Yes, I know I’m getting field-specific here, but at CHI this is extremely common and I feel many social science venues will follow. Every HCI paper, whether it’s about algorithm performance, user interfaces, or social justice, now needs a positionality statement that addresses four critical dimensions. Some claim this is political correctness in action, but really the purpose is demonstrating you understand how your perspective shapes every research decision, from problem formulation to data interpretation. It can’t hurt to document this. Here’s what to include in your positionality statement: - **Values driving your work**: “We approach this research valuing efficiency and scalability in system design.” - **Ethical points beyond the ethics board review**: “We considered how our facial recognition research might enable surveillance.” - **Biases affecting interpretation**: “Our industry backgrounds may privilege commercial viability over community needs.” - **Identity disclosure (but only if comfortable)**: “As researchers from Western elite institutions, we acknowledge our distance from resource-constrained contexts.” Place this 150-word statement early in your methodology section. For example: “Our team’s computer science training emphasizes quantitative metrics, potentially undervaluing qualitative user experiences. We consciously adopted mixed methods to counteract this bias, though we acknowledge our comfort with statistical analysis may still privilege numerical findings.” or “As researchers embedded in Western academic traditions, we acknowledge our interpretive lens may influence our analysis of global user behaviours.” Most HCI reviewers gobble that right up. They want us as authors to be as transparent as we can about the assumptions that strengthen our work’s contribution. Reviewers see this as a sign you understand the complexity of knowledge production, not just data collection. ### 2\. Build an explicit audit trail that reviewers can actually follow Your methods section probably says “we analyzed the data thematically,” but that tells reviewers nothing about your actual process. Even if you’re citing Braun & Clarke. Instead, create a supplementary materials document with your complete audit trail. Include details of your coding process, your affinity diagrams, excerpts from any research memos, and decision logs showing how themes evolved. Upload this to OSF or GitHub and link it prominently in your paper. Even we don’t do this for every paper even though we should. Top researchers maintain three types of documentation: - Raw data files with original timestamps - Process memos documenting decisions and insights - Analysis evolution showing how codes became themes Quantitative researchers may think audit trails are just for qualitative work, but that’s wrong. Your quantitative audit trail should document stuff like: - Why you removed those 47 data points (with the specific criteria used) - How you handled missing data and why you chose that method - Alternative analyses you tried and why you rejected them - Power analysis calculations and assumption checks For example: “We initially planned ANOVA but switched to Kruskal-Wallis after normality tests failed (Shapiro-Wilk, p<0.001). See the “Quantitative Decisions” document for distribution plots and test statistics. This transparency gets you that *rigorous methodology* praise. Reviewers want to see your messy research process and double-check it, not just your clean results. Always provide documentation of analytical decisions, including abandoned approaches, to demonstrate exceptional methodological rigour. ### 3\. Triangulate everything but do it strategically, not randomly Triangulation isn’t just when you use multiple methods. Instead you strategically combine approaches to address different aspects of your research question. Map each research question to at least two data sources. If you’re studying user behaviour, combine system logs (what people do) with interviews (why they do it) and observations (how they do it). But here’s the trick: explicitly state in your paper how each method addresses a specific weakness of the others. For instance: “While interviews show participants’ preferences, system logs provided behavioural ground truth, which addressed potential social desirability bias.” Don’t look at this as making more work for you, but as you doing smarter work that changes decent studies into rigorous investigations. ### 4\. Replace vague quality criteria with paradigm-specific standards. Stop using generic terms like “validity” for qualitative research. It screams “I don’t understand my paradigm.” Instead, use paradigm-appropriate criteria. For *qualitative* HCI, implement these four: - **Credibility** (internal validity equivalent): Establish this through member checking: Send interview summaries back to participants asking “Did I capture your experience accurately?” Also use prolonged engagement (minimum 3-6 months in field studies) and persistent observation (multiple visits to research sites). - **Transferability** (external validity equivalent): Achieve through thick description (i.e., document context so thoroughly that readers can judge applicability to their settings). Include participant demographics, cultural norms, technological infrastructure, organizational structures, and temporal factors. - **Dependability** (reliability equivalent): Demonstrate via audit trails showing how your analysis evolved. Document every coding decision, theme merger, and interpretive pivot. - **Confirmability** (objectivity equivalent): Establish through reflexivity and data triangulation. Show how findings are formed from data, not researcher assumptions. For *design research*, operationalize these three: - **Relevance**: Does your solution address genuine user needs? Show evidence from formative studies and stakeholder feedback. - **Legitimacy**: Is your design process credible to practitioners? Document design rationale, iteration history, and expert reviews. - **Effectiveness**: Does it work in practice? Provide deployment data, usage metrics, and outcome assessments. For *quantitative work*, address all four validities: - **Internal validity**: Show causal relationships are real, not confounded. Document randomization procedures, control variables, and manipulation checks. - **External validity**: Demonstrate generalizability through diverse samples, replication studies, or ecological validity arguments. - **Construct validity**: Prove you’re measuring what you claim. Include validation studies, factor analyses, and convergent/discriminant validity evidence. - **Statistical conclusion validity**: Make certain that your statistical inferences are sound. Report power analyses, assumption checks, effect sizes, and correction procedures. Then (and this is crucial) operationalize each criterion. Don’t just claim “transferability,” but explain it like this: “We provide thick descriptions of our research context, including participant demographics, technological infrastructure, and cultural factors, which our readers can use to assess the applicability of our research to their contexts.” Once you pick a paradigm, make sure to use the specific criteria I mentioned here, or find others that you can operationalize for your context. As a result, you‘ll get fewer methodological concerns in your reviews. ### 5\. Report your constraints as design decisions, not limitations Every study has constraints, but framing matters immensely for the [perception of rigour of your study](https://arxiv.org/pdf/2410.04981?ref=lennartnacke.com#page=6.50). People perceive papers as having the highest rigour if they can demonstrate a clear awareness of their position within their paradigm. Change “We only studied 12 participants” into “We deliberately selected 12 information-rich cases for deep analysis, consistent with phenomenological traditions prioritizing depth over breadth.” It’s not a spin on your research when you frame this study as aligned with phenomenological traditions, because you’re showing reviewers that you understand different research paradigms have different standards for rigour. That’s accurate methodological positioning. Always use paradigm-appropriate criteria to reduce methodological concerns. Also, calibrate your certainty (i.e., how confident you are that your findings are presented in their appropriate context) to indicate to reviewer how rigorous your approach is. When you explicitly state “we deliberately selected” rather than “we only had,” you demonstrate the intentional, theory-driven nature of your methodological choices. Structure your constraints discussion in three parts: - The methodological tradition you’re following and its assumptions - Why your choices align with that tradition - What your approach shows that other approaches might miss So, rigour, in this context, acknowledges what each approach shows you, meaning what you gain from it and what you might have missed by using it. In this case, you’re not hiding any limitations of any approach, but you’re indicating to your reviewers that you understand the epistemological trade-offs that any methodological choice brings along with it. For example, you can discuss trade-offs between: - Depth vs. breadth (qualitative vs. quantitative samples) - Internal vs. external validity (controlled vs. naturalistic settings) - Precision vs. relevance (laboratory vs. field studies) Instead of: “Due to resource constraints, we interviewed 8 participants.” Write: “Following information-rich case selection principles (Smith, 2002), we interviewed 8 participants who represented maximum variation across our key dimensions of interest, which allowed us to deeply explore the phenomenon while maintaining analytical manageable data volume for rigorous thematic analysis.” Remember that rigorous research isn’t research without constraints. Instead, you aim to help reviewers understand not just what you did, but why those choices were theoretically and practically justified within your research paradigm. Explain that you’re doing research where constraints are thoughtfully chosen and explicitly justified. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) ### 6\. Create a reproducibility package before you even submit Most researchers scramble to create reproducibility materials after acceptance, but rigorous papers ship with complete packages. Before submission, prepare: analysis scripts with inline documentation, anonymized raw data, detailed protocol documents, and instrument validation evidence. Host everything on a repository like [OSF](https://osf.io/?ref=lennartnacke.com), [figshare](https://figshare.com/?ref=lennartnacke.com), [FRDR](https://www.frdr-dfdr.ca/repo/?ref=lennartnacke.com) (if you’re in Canada) or [Zenodo](https://zenodo.org/?ref=lennartnacke.com) with a DOI, and include a “Reproducibility Statement” section in your paper. Your reproducibility statement should specify: - What materials are available and where - What can and cannot be reproduced and why - Environmental dependencies and version requirements Reviewers increasingly see pre-registration and reproducibility packages as a baseline expectation, not a bonus. So it’s good to get into the habit early. ### 7\. Write certainty-calibrated claims that match your evidence The fastest way to seem non-rigorous? Just overclaim your findings. Ok, I’m not suggesting that you have to hedge every single statement in your discussion section. Rather, you should think carefully about how you discuss the impact of your findings, what you can say with certainty, and what you cannot claim. Develop a certainty vocabulary: “strongly suggests” for converging evidence, “indicates” for clear patterns, “may suggest” for emerging insights, and “warrants further investigation” for preliminary findings. Then map each claim properly to specific evidence in your results. Never write “proves” in HCI research. That’s like drenching your hand in high-fructose apple juice and then trying to smash a wasps nest with it. Enjoy the stings. Never write “all users” when you mean “all participants.” Never claim generalization without explicit evidence of transferability. Here’s a rigour-enhancing trick you can use: create a claims table in your appendix or supplementary materials that maps each discussion point to supporting evidence, certainty level, and alternative explanations. Reviewers love this because it shows methodological maturity. We often wish that rigour was just a checklist that we can follow, and I am certainly providing some checklists here in this issue for paid subscribers, but more often than not, rigour at its heart is demonstrating that you have reached methodological sophistication through systematic transparency. Ideally, you implement these seven strategies in your next paper, and, as a result, your rejection rate will plummet and your paper quality will increase. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Let me know if you feel I’ve missed anything or if rigour has even more specific properties in your own field. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### 8 LLM Prompts, 4 Templates, 2 Checklists [For paying subscribers](https://lennartnacke.com/#/portal/signup), I have 4 statement templates (qualitative research quality, design research quality, reflexivity for individual researcher and research team, ), 8 LLM prompts (quality criteria selection, reflexivity/positionality statements, audit trail planning, constraint reframing, reproducibility package planning, calibrating research claims, study rigour assessment, method section review) and 2 checklists (audit trail, general paper rigour) today: _This post is for paying subscribers only._ ### 7 Ways to Juggle Research Projects URL: https://lennartnacke.com/7-ways-to-juggle-research-projects/ Last updated: 2025-09-15T10:44:28.000Z Every academic I know is drowning in unfinished projects. I feel like this is a natural job hazard for us, given the nature of our appointments. We have almost infinite freedom to choose the task that we want to work on, but with this freedom also comes the responsibility to categorize, manage, and prioritize the different tasks that we have picked. Otherwise, you end up in a scenario where your literature review is 80% done and never makes it past the finish line. Or you’re stuck with your data analysis for the second paper that you started three months ago, but you can’t seem to get it to completion. Or even worse, you have a grant proposal due in six weeks but you haven’t made any progress. Sometimes, your collaborator is waiting on your conference abstract that you’ve promised and they still haven’t seen any updates from you. As you ponder these unfinished tasks, somewhere deeply buried inside of your desk drawer lurks this almost-ready manuscript that just needs one more revision before you submit it. And you’re afraid to even look. If all of that sounds familiar to you, then the problem isn’t that you are lazy or disorganized. No, academia rewards starting new projects but never teaches you how to actually finish the existing ones. And then, you end up in a situation where they’re all competing for your limited time and mental energy. So today, I’m going to share the 7 ways I’ve learned to juggle multiple research projects without losing momentum on any of them . Simple strategies that helped me get the grants done and submit the papers without giving in to the pain of unfinished business. Let’s walk through each one. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### **1\. Create a project pulse check every Monday morning at 9 AM** This is the simplest hack that I know, but you really have to commit to doing it regularly. Otherwise, it will not be effective. Start every week by spending exactly 15 minutes reviewing the status of every active project on your plate. This completely changed how I manage my research pipeline. Open a simple spreadsheet and list every project you’re working on (or use the tracker I provide to paid subscribers this week). Next to each one, write three things: what phase it’s in (data collection, writing, revision, etc.), what the next concrete action is, and when you last touched it. If you haven’t touched a project in over two weeks, it’s dying. Either schedule time for it this week or officially pause it. And hey, there’s no shame in pausing projects but there’s massive guilt in letting them silently die while pretending they’re still active. Make a choice and stick with it. The trick is to realize at the right time that most projects only need 2–3 hours of focused work to move to the next milestone. So there is usually never that mythical full week of uninterrupted time that you keep waiting for. Just get it done with the tiny window of working time that you have available to you right now. ### 2\. Batch similar tasks across all projects on the same day Whenever I first came across this idea, it seemed completely counterintuitive to me, because all of my projects would be completed in a linear, waterfall fashion. And I’m somebody who is familiar with agile and iterative, cyclical project management. Here’s the main idea: stop trying to make progress on one project at a time. Instead, batch similar work across multiple projects. Ok, let’s unpack what I mean by this. Dedicate Tuesdays to all data analysis across every project. Wednesdays are for writing new content. Thursdays are for editing and revisions. Fridays are for administrative tasks like formatting, references, and submission systems. Of course, you are free to assign the days and tasks according to your schedule and whatever works for you. This is just an idea of how you could do this. When you batch similar cognitive tasks, your brain doesn’t waste energy switching between different types of thinking. You can analyze data for Project A from 9-11am, then seamlessly transition to analyzing data for Project B from 11am-1pm, because you’re already in analysis mode. It’s an easy way to maintain momentum on five different papers simultaneously without having to switch to different brain modes in between. The key is protecting these themed days religiously. That can be really tough sometimes. No meetings on Writing Wednesday, no email on Analysis Tuesday. You get the main idea. ### 3\. Use the minimum viable progress rule for every project Never let a project go more than seven days without making some form of progress, even if it’s just 15 minutes. This is crucial for keeping momentum on any kind of tasks you’re working on it. I discovered this principle after too many of my projects died from neglect and I couldn’t quite figure out how I forgot about some of them. Here’s how it works: every project on your active list must receive at least one touchpoint per week. This could be reading one paper for your literature review. Writing one paragraph. Cleaning one dataset variable. Running one statistical test. The progress doesn’t have to be significant . It just has to exist. Don’t let a psychological barrier build up, because this will happen if you haven’t looked at a project in weeks and can’t remember where you left off. Set a recurring Friday afternoon slot to quickly hit any projects you haven’t touched that week, even if you only spend 10 minutes fixing a typo or reorganizing a folder. ### 4\. Build handoff packages for your future self Every time you stop working on a project, spend 3-5 minutes creating a note for your future self about exactly where to pick up. I know this sounds annoying at first, but trust me, over time this is really helpful so that you’re not confused if you actually haven’t touched a project in a while. This is the single most powerful technique for maintaining momentum across multiple projects. Before you close that manuscript, write: “Next steps: revise paragraph 3 of discussion, add Nacke 2024 citation to intro, check Table 2 formatting.” Before you leave that data analysis, note: “Just ran regression, need to check assumptions next, see diagnostic plots in folder X.” These breadcrumbs save you the 20-30 minutes you typically waste trying to remember what you were doing when you return to a project. I keep these notes in Apple Notes with a project name in the note, so I can search for it (I also recommend a hashtag like #nextsteps to organize things a bit). I’m a fan of just searching stuff in Apple Notes on any device and finding it instantly. This is one of those techniques that seems tedious in the moment, but when you’re actually picking the project up in the future, you’ll be really grateful that you put in the extra effort. ### 5\. Establish hard boundaries for project creep using the 90% rule This is one of those things you usually learn the hard way. Many projects take an exorbitant amount of effort at the very end to push them across the finish line. So when one of your projects reaches 90% completion, ban yourself from starting anything new until that project is submitted. Embracing this rule, really gave me the biggest productivity push. We all know the last 10% of any project takes 50% of the total time (stuff like formatting, final revisions, dealing with co-authors, submission logistics). It’s tempting to start something new and exciting when you’re stuck in these boring grindful final stages. Don’t. When any project hits 90% done (full draft complete, data analyzed, just needs polishing), it becomes your only priority until it’s out the door. Yes, you maintain your weekly *minimum viable progress* on other projects, but you cannot begin any new ventures. This creates urgency to actually finish things instead of accumulating a portfolio of 15 projects that are all almost done, just because you fell into the trap of the next shiny new thing. One of my tricks for this is to write a Post-it note to myself on my monitor that says, “Get this sucker submitted!” This keeps me honest and puts some pressure on myself. ### 6\. Develop a project triage system for unexpected urgencies Prioritizing is one of those things we don’t think about enough because not all of our deadlines are created equal. You really want to know which projects can be dropped when you’re hitting a crisis with one of your projects or—you know — life in general. Rank every project as either “critical path” (directly affects your degree completion or job security), “high value” (important but not existential), or “nice to have” (interesting but optional). When your supervisor drops an urgent request, or when life happens and you lose a week to illness, you know exactly which projects to pause. Critical path projects never stop. High value projects get minimum viable progress. Nice to have projects go on official hiatus until the crisis passes. This system prevents me from panicking when, all of a sudden, I see that a grant proposal isn’t quite done yet and really needs some final triage work before the deadline. I know exactly which projects I can put on pause and which ones I simply cannot. Keep this ranking visible and update it monthly as your priorities may shift. ### 7\. Track your project velocity to predict realistic timelines A lot of the time when we take on new projects, it’s a little bit of wishful thinking when it comes to the time budget that we’re allocating to it. So we really want to measure how long things actually take us, not how long we wish they will take us. Be realistic about everything. Start logging how many hours each project phase actually requires. Writing a discussion section? Track it. Data cleaning? Track it. Responding to reviewer comments? Track it. After three months, you’ll have real data about your working speed. This kills the planning fallacy that makes you think you can write a full paper in a weekend (don’t believe the AI influencers touting that non-sense). When you know it takes you 40 hours to write a first draft, you can accurately plan when to start. When you know revisions take 15 hours, you stop promising your advisor you’ll have it done by tomorrow. This data becomes invaluable for deciding whether you can realistically take on that new collaboration or if you should focus on finishing what you’ve already started. It saves you a lot of headaches down the line. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Find project management systems that work for you One of the things to keep in mind here is that juggling multiple research projects doesn’t mean you have to work harder or find more hours in every day. You only have to focus on creating systems that maintain momentum on everything while you also get things actually finished. I’d recommend to pick one of these strategies and implement it this week. Just make it a habit. And once it’s a habit, you can add another one. Within two months, you’ll be managing multiple projects like the prolific researcher that you’re meant to be. My best of luck to you. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### **The Academic Project Dashboard** A simple template for project tracking: _This post is for paying subscribers only._ ### How to Turn Your Notes Into Readable Paragraphs and Sections URL: https://lennartnacke.com/how-to-turn-your-notes-into-readable-paragraphs-and-sections/ Last updated: 2025-09-04T18:26:47.000Z #### Key Points - Trying to write, cite, argue, and edit all at once causes cognitive overload. - Use **atomic notes* to capture one idea per note with clear links. - Build a **synthesis matrix* to map arguments across sources and spot gaps. - Write using the **hourglass (PEEL) paragraph structure* for flow and clarity. - Follow a weekly staged system: notes, paragraph drafts, integration, polishing. You’re drowning. In notes. In deadlines. In the crushing weight of your dissertation that refuses to write itself. Every morning to every night, you shuffle the same fragments, rearrange the same quotes, and still. Nothing. Most PhD students try to transform their research notes into polished paragraphs in one heroic outing. The common mistake? Attempting to synthesize sources, assemble arguments, and perfect your prose all at the same time. Such cognitive overload creates paralysis. Your brain maxes out trying to juggle five different writing tasks simultaneously. You stare at the screen for hours, producing nothing but frustration. It’s like Ferris Bueller took your paragraphs for a day off and forgot to return them. And all you do is attempting to blow into the empty NES cartridge that is your mind trying to find the words in there, but knowing they just won’t load. Stop trying to do everything at once. I mean it. 🧠 Most PhD students get stuck trying to write and edit at the same time. That overloads working memory and leads to paralysis. The solution is staged writing: break the process into layers: Atomic notes, synthesis, and structured paragraphs. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### The Writing Crisis Is Real [Research shows 80+% of graduate students](https://www.google.ca/books/edition/Overcoming%5FProcrastination/9kjuAAAAMAAJ?hl=en&ref=lennartnacke.com) struggle with [procrastination](https://lennartnacke.com/i-tried-every-procrastination-hack-nothing-worked-until-i-understood-my-type/), particularly when facing complex writing tasks. Your brain betrays you, it often cannot juggle more than 7 chunks of information (chunking is grouping information into meaningful units) without dropping everything. Your working memory can only handle 4 ± 1 items of information at once (without chunking or rehearsal), but memory span varies based on factors like word length, familiarity, or speaking time. When you try to write, cite, argue, and edit simultaneously, you’re asking your brain to perform an impossible juggling act. The result? Most PhD students take three times longer to write than necessary, producing work that still needs extensive revision. Break your writing into distinct cognitive layers. Here’s exactly how to do it. ### How To Transform Notes Into Readable Paragraphs (to Finish That Thesis) You’ll learn to separate the three core writing processes that your brain naturally wants to combine. - First, create an atomic note system where each idea lives as its own complete unit with unique identifiers and explicit connections to related concepts. - Second, build paragraph skeletons using the hourglass method: broad context narrowing to specific evidence, then expanding to implications. - Third, layer in your intellectual voice through staged construction that respects your cognitive limits while maintaining academic rigour. Here’s the system: #### **Step 1: Atomic Notes** Forget massive, undifferentiated text blocks. Each note should contain exactly one complete thought in your own words. Give it a descriptive title that captures the essence, something you’ll actually remember like “Why attention fractures under cognitive load.” Link it explicitly to related concepts. To build your cognitive architecture. When you externalize your thinking into bite-sized units, you free your working memory to focus on writing rather than remembering. *In Obsidian:* Create one note per concept with a descriptive title like “Working Memory, 7 item limit (Miller 1956).” Inside, write the core idea in one sentence. Then add your interpretation and link to related concepts using \[\[double brackets\]\]. When you search “working memory,” all related notes surface instantly. *In Notion:* Build a searchable database. Each entry = one concept. Add properties that mirror how you think: Core Idea (one sentence), My Take (your interpretation), Source, and Related Concepts. Use filters to pull up everything about “cognitive load” when writing that section. The visual database lets you see patterns your linear notes hide. *In Apple Notes:* Use the folder system as your categories (e.g., “Cognitive Psychology,” “Writing Process,” “Research Methods”). Title each note with the concept + context: “Working Memory: Why we can only handle 7 things at once.” Inside, keep it simple: one paragraph for the concept, one for your take, then use hashtags for connections (#cognitiveload #writingblocks #attention). Apple’s search finds everything instantly. Type “working memory” and every related note appears, including where you mentioned it in other notes. My favourite (and slightly) lazy method. The key is to use descriptive titles you’ll actually remember. Not “Note-047” but “Why humans can’t multitask: Cognitive bottleneck theory.” When writing, you search by concept, not by code name. Turn each source insight into a discrete intellectual unit. From foundational theories to cutting-edge findings, you’re gathering pre-digested ideas your brain can now actually manipulate. Basically, you’ve gone from Jon Snow knowing nothing to Bran Stark seeing everything. #### Atomic Notes - Each note = one complete idea, in your own words. - Connect it to other concepts with links or hashtags. - Give it a descriptive, memorable title. #### **Step 2: Thematic Synthesis Matrix** ![](https://lennartnacke.com/content/images/2025/09/literature-review-synthesis-matrix.webp) This is how a literature review thematic synthesis matrix could look like. The full matrix in Notion plus tutorial and AI prompt that helps extract key information from papers into a Notion Database is available to Paid Subscribers at the end of the article. Create a grid. Rows represent your key arguments (but I also like to keep track more generally of main ideas). Columns represent your sources. Fill each cell with specific evidence that source provides for that argument. Empty cells reveal research gaps immediately. Dense cells show where your evidence is strongest. I also have a version of this that presents columns as the key arguments for an idea and the key arguments against an idea. I also like to keep track of the methodologies used to study this idea. Then I usually have a column about the gaps in research related to this specific idea. And finally, I like to keep track about the relationship to my own research. Such a visual mapping reduces the cognitive load of remembering what goes where. Your brain can focus on connections instead of recall. | **Argument/Theme** | **Smith (2023)** | **Jones (2024)** | **Brown (2022)** | **Research Gap** | **My Work** | | --------------------- | ---------------- | ---------------- | ---------------- | ---------------------------- | -------------- | | Cognitive Load Limits | ✔ evidence | | ✔ counter | Missing longitudinal studies | Core framework | #### Thematic Synthesis Matrix **Create a grid:* - ****Cells** \= specific evidence each source provides. - ****Rows** \= key arguments or themes. - ****Columns** \= sources. **Enhancements:* - Add a column for how this connects to your own work. - Add “for” and “against” arguments. - Track methodologies used. - Note research gaps. #### **Step 3: Hourglass Paragraph Structure** Start broad with context. Narrow to specific evidence from multiple sources woven together. Then expand again to implications and connections. This hourglass maps perfectly onto PEEL structure. Your Point starts broad (context). Evidence narrows the focus (specific sources). Explanation weaves them together. Link expands outward (implications). Same cognitive architecture, different labels. Short sentences anchor. Medium sentences develop as they proceed. Long sentences synthesize multiple sources into coherent arguments that advance your field. Vary the rhythm. Make it palatable. To the reader. Each atomic note becomes one E in your PEEL. Pull three related notes, you have three pieces of Evidence ready to slot in. No more staring at blank pages wondering what comes next. Your paragraph builds itself. This flow is your Millennium Falcon, jumping readers to light speed without shaking them apart. Each paragraph is a story episode: The setup is your “Previously on,” the middle drops the twist, the ending lands the cliffhanger. It starts in the Shire, wanders through Mordor, and comes back carrying fire. It’s Mario grabbing the star: familiar, surprising, unstoppable. Every section (and every paper) takes your reader on the full ride: From comfort zone, to revelation, to meaning that stretches far beyond your page. Write one layer at a time. Separate them. Turn this overwhelming task into workable steps that respect how your brain actually works. Why this works when everything else fails: #### Paragraph Structure Each paragraph follows a natural flow: 1. ****Point (broad context)** 2. ****Evidence (specific sources woven together)** 3. ****Explanation (synthesis, analysis)** 4. ****Link (implications and outward connections)** #### Paragraph Writing Tips - Short sentences for anchors. - Medium sentences to develop ideas. - Long sentences to synthesize multiple sources. This rhythm keeps readers engaged. ### Why You Should Use Staged Paragraph Construction The human brain wasn’t designed for simultaneous complex processing. Cognitive Load Theory supports that frontloading your organizational work dramatically improves writing quality. So, it’s generally a good idea to separate planning from drafting for better coherence. We have [limited capacity for simultaneous control-dependent tasks](https://doi.org/10.1016/j.tics.2021.06.001?ref=lennartnacke.com), such as synthesizing sources, constructing grammatical sentences, and maintaining argumentative flow. In practice, this means once you’ve freed working memory from overload, you can reallocate it toward structure and clarity. The real power? Your brain shifts from desperate juggling to deliberate architecture. When you separate note-taking from synthesis, synthesis from drafting, and drafting from editing, each process gets your brain’s full attention. Arguments clarify. Evidence flows. Your academic voice can now emerge with fewer constraints. #### Staged Weekly Writing System - ****Monday: Note Day:** Process research into atomic notes. - ****Tue/Wed: Paragraph Days:** Draft topic sentences, map evidence, then integrate. - ****Thursday: Integration Day**: Polish transitions using echo method. - ****Friday: Polishing Day:** Check citations, flow, and clarity. ### Advanced Implementation Protocol Here’s exactly how to implement this system this week. #### **Monday: Note Processing Day** Turn your weekly research into permanent atomic notes. Each insight gets its own note with a unique identifier. Connect related ideas explicitly. This is intellectual carpentry: Measure twice, cut once, and your argument won’t wobble as much. #### **Tuesday/Wednesday: Paragraph Construction Days** Never write the whole paragraph at once. First, draft just topic sentences (pick the strongest of three if you feel like rewriting) for your sections, bullet points. Then map your evidence visually before attempting integration. You can slot them in a sub bullet points. Only after evidence is laid out do you add some brief analytical reflection. Finally, write transitions. Or hey, just prompt any LLM to help you “segue from {current paragraph} into the next concept: {provide next topic sentence}”. Five paragraphs per day using this method beats twenty paragraphs of cognitive overload. #### **Thursday: Integration Day** Connect new paragraphs to existing structure. Refine transitions using the echo method: repeat key terms from topic sentences throughout, but with progressive development (could also put this in a prompt). You’ll be able to create both unity and momentum without clunky mechanical transition words. #### **Friday: Polishing Day** Now, and only now, do you polish. Check citation integration. Refine your academic voice. Make sure things are really clear. This is where AI tools can really help, but only for enhancement, never for complete generation. The brainwork is yours. The booster is in how it shows up on the page. ### Quick Tip for Source Integration Stop presenting sources sequentially like a grocery list. I like to call them dinner conversations (like imaging your sources are having a dinner conversation with one another). Create intellectual conversations between researchers. “While Smith (2023) argues X, Jones (2024) extends this framework by suggesting Y, though both overlook the crucial factor of Z identified in earlier work by Brown (2022).” You want to avoid a situation where you just list things off like: A said this, B said this, C said this. This is mechanical citation. But you don’t want that. You want dynamic academic dialogue. Your paragraph should turn into this moderated discussion where you’re the intellectual host guiding readers through competing perspectives toward your unique contribution. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### The Weekly Writing System That Actually Works Productive academics don’t write when inspired. They follow systems. We know the usual patterns: 90 to 120 minute focused sessions during peak cognitive hours. Daily writing, even 30 minutes, beats marathon weekend sessions. But let it flow. If you go hard, go hard. If you go light, go light, but avoid not writing. Always be writing. Your consistency will compound. Small daily progress puts an end to the anxiety of looming deadlines. But most importantly: Always respect your cognitive limits. When you feel overwhelmed, reduce scope. Focus on one intellectual connection per session. Use visual mapping (like concept maps) to offload memory burden. Never attempt full paragraph construction when cognitively depleted. The academics who master these systematic approaches don’t just write better. They write with less suffering. They transform academia’s most dreaded task into manageable, even enjoyable, intellectual practice. Writing isn’t thinking. Writing is the expression of thinking you’ve already done. So stop trying to do both at once. Build your atomic notes. Layer your paragraphs. Separate your processes. Respect how your brain actually works. Start with one note. One complete thought. One descriptive title. The rest follows. #### FAQ ****Q1: How do I know if my atomic notes are good enough?** If each note makes sense to you six months later without extra context, it’s good. ****Q2: Can I use AI tools to build my synthesis matrix?** Yes, prompt an LLM to extract key arguments, but always verify accuracy. My prompt for paid subscribers is below. ****Q3: How many sources should I integrate per paragraph?** At least 2-3, woven together into a conversation, not listed separately in the main body of sections that are not results. ****Q4: What if I fall behind on the weekly system?** Scale down. One note per day is still progress. Consistency compounds. #### Bonus: Literature Review Matrix, LLM Prompt and Notion Page _This post is for paying subscribers only._ ### How I Cracked the Code on AI-Assisted Qualitative Research URL: https://lennartnacke.com/how-i-cracked-the-code-on-ai-assisted-qualitative-research/ Last updated: 2025-08-28T12:19:38.000Z #### Key Points - ****AI accelerates coding**: LLMs can cut initial coding time, but only with structured frameworks like STECT or ACTOR. - ****Mixed evidence**: About 30% of studies show AI matches or exceeds humans in some tasks, while 30% find it unreliable without oversight. - ****Best role**: AI should handle descriptive themes; humans should interpret context, nuance, and meaning. - ****Human-AI partnership**: Acting as manager, colleague, teacher, and advocate helps you safeguard rigour. - ****Transparency is essential**: Always report AI tools, prompts, validation, and audit trails in your methods section. You’re drowning in interview transcripts while your publication deadline approaches way too soon. Every qualitative researcher knows this nightmare. Hundreds of pages of data, countless hours of coding ahead, and that sinking feeling that you might miss crucial themes buried in the text. The traditional approach to qualitative analysis (reading, re-reading, manually coding, and hoping you maintain consistency across thousands of data points) is simply exhausting. So, it’s no wonder that qualitative researchers are looking to generative AI as a hot topic for helping them create easier ways to do data analysis. Alright, are you ready for me to drop a truth bomb on you? Most “AI for qualitative research” advice is dangerously misleading. Some LLM tools promise revolutionary efficiency, but when [Dr. Llewyn Paine tested 31 researchers using identical AI prompts](https://www.linkedin.com/posts/llewyn%5Fai-userresearch-activity-7320134834679357440-Y1U9/?ref=lennartnacke.com) on the same data, she got wildly different results. Theme counts ranged from 5 to 18, and every single participant found fabricated quotes. Without consistent results, your research loses all credibility. But, despite these problems, AI can enhance your research through proper use. ![](https://substackcdn.com/image/fetch/$s_!4GDZ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9024f20b-49b2-4254-9222-2b0b9e8b84e7_800x793.png) My [Consensus deep search](https://get.consensus.app/lennartnacke?ref=lennartnacke.com) review of studies from the last 5 years. So, I ran a deep search on [Consensus](https://get.consensus.app/lennartnacke?ref=lennartnacke.com) to answer a critical question: “Can ChatGPT perform qualitative data analysis effectively?” As you can see in the figure above, the results from 23 high-quality papers show a patchwork of results: From the corpus, 30% report that LLMs can match or exceed human performance in specific qualitative tasks, 22% represent smaller or exploratory studies that show promising uses of LLMs, but make it clear that there are limitations in reliability, context, or that it needs further validation, 17% find that LLMs assist or complement humans but do not have the same nuance or require prompt iterations or just general human oversight, and, finally, 30% report that LLMs lack contextual depth, reliability, or fidelity, in particular when they are used in attempts at sophisticated analyses. This split reveals an important truth. Success with AI in qualitative analysis depends entirely on how you approach it. Looking at the citation numbers, though, it currently looks like researchers are biased to cite favourable studies for LLM use in qualitative research. | **Outcome** | **% of Studies** | **Findings** | | ----------------------------------- | ---------------- | -------------------------------------------- | | Matches/exceeds human on some tasks | 30% | Strong performance on descriptive coding | | Promising but limited | 22% | Needs validation; struggles with context | | Useful assistant role | 17% | Good for surface coding with human oversight | | Lacks reliability | 30% | Weak in interpretive/theoretical analysis | In today’s issue, I’ll show you how generative AI tools like ChatGPT can actually transform your qualitative analysis. They can cut your analysis time by up to 50%, I would estimate, and enhance the depth of your insights. [Nguyen-Trung’s recent study on ChatGPT in thematic analysis](https://doi.org/10.1007/s11135-025-02165-z?ref=lennartnacke.com) backs this. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Let’s dive into the five major applications: ## Five major AI cases in qualitative research #### AI processes large datasets in minutes, not months The most immediate benefit of using AI for qualitative analysis is pure speed. What used to take weeks can now happen in hours. [LLMs can quickly handle large datasets](https://doi.org/10.1177/16094069231211248?ref=lennartnacke.com). They spot key themes and create initial codes, saving substantial time. [ChatGPT and similar tools can quickly process hundreds of interview transcripts](https://doi.org/10.37074/jalt.2024.7.1.22?ref=lennartnacke.com). They identify patterns in large text volumes and create coding schemes. This work would take human researchers weeks to do by hand. We have to consider two things: 1. We have to make sure to have detailed and structured instructions in our prompt (see below). 2. Any data that we use should be anonymized or run through a local instance of an LLM. Here’s the tactical approach that works: Start with descriptive coding first. [Nguyen-Trung’s ACTOR framework](https://doi.org/10.1007/s11135-025-02165-z?ref=lennartnacke.com) (which is itself based on a popular prompting structure) provides a systematic approach. Define your Actor, Context, Task, Outputs, and Reference. The **actor** defines ChatGPT’s role. They link it to certain functions and clearly outline what it can do. Example: “You’re a qualitative research assistant”. **Context** provides key background for the task. It covers research questions and explains important concepts like code, cluster, and theme. This domain knowledge helps ChatGPT understand and perform its tasks well. Example: “Define code as…” The **task** provides a detailed outline of ChatGPT’s responsibilities, such as summarizing transcripts. It also specifies what to avoid, such as creating overlapping codes. Example: “Summarize this transcript…?” **Outputs** detail the expected end product. They include clear examples of how the outputs should look, along with their format and structure. Example: “…in three key bullet points.” **Reference** gives specific data or resources for the AI to use. This includes transcripts, documents, or datasets. These make certain the AI’s outputs are accurate and relevant. Example: “Here is the transcript…” Generative AI excels at surface-level pattern recognition, which gives you a rapid overview of your data ecosystem. For practical implementation, I’ve simplified this to the **STECT framework**: 1. STYLE (academic, methodologically rigorous language) 2. TASK (perform initial thematic coding) 3. EXAMPLE (give an example of how to execute the required task) 4. CONSTRAINTS (require human validation for interpretive themes). 5. TEMPLATE (demonstrate exactly how the output should look like) I’ve based on this framework on the structure that the [Write with AI Substack](https://writewithai.substack.com/leaderboard?&referrer%5Ftoken=igmn&utm%5Fsource=post) discusses (as a general AI prompting template). I believe that this makes it more accessible to non-technical researchers. #### Human-AI collaboration produces richer insights than either alone Working with AI in qualitative research is all about augmentation that makes your analysis more robust. The goal here is not to replace the complete qualitative workflow but to add quality to it. Essentially, you want to improve the robustness of your analysis. [Research emphasizes that LLMs serve best as research assistants or co-analysts](https://doi.org/10.1177/10497323241244669?ref=lennartnacke.com), where human oversight remains critical for interpreting context-dependent data. The sweet spot is using AI to handle concrete, descriptive themes while you focus on subtle, interpretive analysis that requires human judgment. [Nguyen-Trung’s Template Analysis approach](https://doi.org/10.1007/s11135-025-02165-z?ref=lennartnacke.com) demonstrates this beautifully: First, let GPT-4 generate initial codes from your first transcript. Second, review and refine these codes yourself, adding context the AI missed. Third, apply this refined coding framework across remaining transcripts with AI assistance. Fourth, use the AI to check consistency while you make final interpretive decisions. This iterative process [combines AI’s processing power with human interpretive skills for superior results](https://doi.org/10.1145/3581754.3584136?ref=lennartnacke.com). The evidence shows that AI-assisted analysis can achieve inter-coder reliability rates comparable to multiple human coders, particularly for descriptive themes, while it dramatically reduces time investment. [According to this 2024 study](https://journals.sagepub.com/doi/10.1177/00472816241260044?ref=lennartnacke.com), you could also adopt four complementary roles when working with AI. 1. Be the **Manager**. Your new job is to closely monitor all AI outputs to make sure it follows a consistent process. The next step is to direct all the AI analysis and be able to trace it back to the data. 2. Engage as **Colleague** next. Maintain analytical dialogue while you keep a critical distance. You are looking for meaning to be produced in chat conversations so that you can add to your own knowledge. You are treating the AI as a second pair of eyes. 3. Next, act as **Teacher**. Here your new responsibilities include instructing the AI on theories, research methods, or data qualities so that the output is improved. As a teacher, you are aware that the AI will make mistakes and engage in misrepresentation of the data, so you have to be willing to correct mistakes and push back. 4. Finally, become an **Advocate**. Work hard to keep participants’ voices authentic. Make sure that all perspectives are accurately represented. Your job is to anticipate how certain users interpret or use the text data provided by your research. Each role protects against AI’s specific weaknesses. If you master these roles, AI can become a powerful research assistant for you. However, the jury is still out on whether or not this will make your qualitative approach more efficient (but the goal here is quality, remember). **P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit:* [**A seven-day email course*](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) *that teaches you the basics of research methods. Or the recordings of our* [**​AI research tools webinar​*](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) *and* [**​PhD student fast track webinar​*](https://go.lennartnacke.com/thesis?ref=lennartnacke.com)**.* [Learn more ](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) #### Prompt engineering determines your analysis quality The difference between mediocre and exceptional AI-assisted analysis lies entirely in how you write your prompts. [Well-designed prompts and transparency improve ChatGPT’s effectiveness significantly](https://doi.org/10.1016/j.chbah.2025.100144?ref=lennartnacke.com), with studies showing that iterative prompt refinement can dramatically enhance accuracy. Using my STECT framework, structure your prompts systematically. Here is an example of how such a prompt would look like for [phase 1 of thematic analysis](https://www.thematicanalysis.net/doing-reflexive-ta/?ref=lennartnacke.com) (familiarization with data): _This post is for paying subscribers only._ ### 6 Ways I Turn Thesis Flaws into Power Moves URL: https://lennartnacke.com/6-ways-i-turn-thesis-flaws-into-power-moves/ Last updated: 2025-08-20T00:12:31.000Z #### Key Points - Weaknesses don’t sink your defense; hiding them does. - Examiners reward candidates who show self-awareness and critical thinking. - The ****BRIDGE method** turns vulnerabilities into power moves. - Each step (Bring, Reframe, Invite, Demonstrate, Ground, End) supports credibility. - The goal: Show examiners you think like a researcher, not just a student. After sitting through 15 years of vivas (for my British colleagues) or PhD defences (or defenses for my American friends), I discovered something that completely changed how I coach doctoral candidates. I’ve mentioned this many times before in my writing on various social media outlets. I believe that students who pass their thesis defence with distinction aren’t always those with perfect research. They are often the ones who understand how to navigate graduate studies effectively. Defending a PhD thesis relies less on having the best results and more on confidence. It’s important to show this confidence to the committee. You really want to engage in a thoughtful discussion that shows how you can engage with the ideas that you brought forward in your thesis. Students who really shine during their defence often take a bold step. They talk about their research limitations before anyone asks. Many academics might see this as career suicide, but it sets them apart. And the shocking part is that this vulnerability eventually becomes their greatest strength. Many PhD candidates see their viva as a trial. They often try to hide any imperfections they’ve faced. Some rehearse deflections and prepare defensive arguments. They hope the examiners won’t notice gaps in their methodology or sample size. This strategy seems unwise. It will make a friendly discussion feel like an interrogation of the student because of the defensive stance they are taking. Today, I want to share something with you that I’m calling the BRIDGE method. This framework may seem odd, but it turns your thesis weaknesses into strengths. It will improve your academic credibility. Let’s go over each component in the following. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## \[B\] Bring up limitations first (before examiners find them) The strongest power move strategy I’ve seen PhD students use in their viva is clearly stating their constraints within the first 10 minutes. You show that you’re proactive in finding limitations. This helps you do three important things: First, you eliminate the *gotcha* moment that some examiners love building toward. Second, you demonstrate the self-awareness that separates student thinking from researcher thinking. Third, you control exactly how these limitations are framed in the discussion. Here’s what successful PhD candidates say: “Before we go further, I want to point out three key constraints that shaped this research.” They explain each limitation using clear reasons: funding restrictions, time limits, access challenges, or ethical issues. Finally, they pivot immediately to what they did instead, showing how constraints led to creative solutions on their part. The examiner, who was fiercely ready to question your sample size, now nods. You explain that a smaller group allows for deep ethnographic insights that larger numbers can’t provide. As I said, it’s a power move. ## **\[R\] Reframe every limitation as an intentional research choice** This is where good candidates become absolute rock stars. They transform constraints into features, not bugs. Consider these reframes that can change the entire tone of a defence: - “Budget constraints forced innovative data collection methods that actually gave us richer insights” - “Geographic limitations led to the first deep longitudinal study of this specific population” - “Time restrictions required a focused scope that made clear patterns others missed” The key here is showing that every limitation produced an unexpected benefit. That narrow theoretical framework? It allowed unprecedented depth of analysis. That single case study approach? It generated transferable insights precisely because of its specificity. That missing control group? It led to innovative quasi-experimental designs that future researchers can build upon. My takeaway point here is that reframing isn’t making excuses. Instead, it shows how real research happens in an imperfect world. ## **\[I\] Invite intellectual discussion rather than defending your position** The moment you shift from defendant to discussion partner, the entire viva transforms. And that can of course feel a little bit weird because defence is in the name of the game. Show that you’ve considered different angles instead of asking examiners to solve your problems. Use phrases that show intellectual engagement while maintaining your role as the candidate: “I considered three alternative approaches here and ultimately chose this one because…” or “This limitation led me to explore compensatory strategies, including…” or “The literature suggests several ways to handle this challenge and I opted for X because it best aligned with my theoretical framework.” Watch what happens when you say: “I wrestled with this constraint extensively. The three options I considered were X, Y, and Z. I ended up choosing X because {provide specific evidence-based reasoning}. Other approaches would also be valid, but they would answer slightly different questions.” When an examiner challenges a limitation, respond with: “That’s exactly the tension I grappled with. I had to set my priorities due to the constraint. I decided to focus on depth. The literature brought to light a gap in detailed understanding, not broad surveys. Here’s how I maximized rigour within those parameters…” Now, you show the critical thinking that helped these examiners succeed as academics themselves. And you do this without asking them to do your thinking for you. ## **\[D\] Demonstrate academic maturity through deep thinking** Nothing impresses examiners more than a graduate candidate who thinks in ranges or spectra rather than binaries. Instead of defending choices as right, you want to discuss them as trade-offs. It’s fine that different approaches provide different insights. There’s no better or worse; they just offer varied perspectives. You want to show that your research is part of a knowledge ecosystem. There are many perspectives that help strengthen the field. Use phrases like: “Choosing qualitative methods meant forgoing statistical power but gaining phenomenological richness” or “This scope prioritized depth over breadth, which matches recent literature’s call for more focused studies.” Showing that you’ve thought about your choices proves to examiners that you grasp the philosophical basis of your research design. You’re not just someone doing research mechanically. ## **\[G\] Ground every justification in literature evidence** Everyone gets it. Your limitations come from practical and academic choices backed by methodical theory. But you have to make sure to point to the right sources that back up your choices. Reference the giants whose shoulders you stand on. Cite the methodological literature that supports smaller sample sizes for phenomenological research. Point to seminal papers that advocate for a narrow scope in exploratory studies. Connect your constraints to ongoing debates in your field about research priorities. When you say, “Creswell argues that saturation in qualitative research can occur with as few as 12 participants, and our 15 participants showed clear pattern saturation by interview 10,” you transform a limitation into methodological rigour. You want to make every constraint your study faces a conscious choice backed up by academic authorities in your field. ## **\[E\] End with enthusiasm about future research** After a successful PhD defence, there should be a pleased crowd. The committee should feel excited about the next steps you are taking. Everyone should look forward to the upcoming research. Share the questions your limitations revealed: “This geographic limit raised interesting questions about cultural differences that need more study.” Discuss how future researchers can build on your foundation: “The narrow scope provides a perfect baseline for comparative studies.” Express genuine curiosity: “I’m eager to see whether these patterns hold in different contexts, and that’s my next project.” Your limitations become stepping stones for the next generation of researchers. Adopt a mindset of collaboration. You’re not just a student defending past work. Instead, think of yourself as a colleague, working together to plan future contributions. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **The paradox of the PhD defence** The PhD candidates who hide limitations can quickly appear naive or deceptive. Those who talk about them openly show the critical thinking that marks academic excellence. Weaponizing your weaknesses with the BRIDGE Method transforms vulnerable viva moments into clear signs of academic growth. Before you go in, you should know that your examiners are not looking for perfect research. They very well know that this doesn’t exist. What they are actually looking for is evidence that you can think like a researcher. They want to see you as someone who makes thoughtful choices. You recognize trade-offs and understand complexity. You also contribute meaningfully, even with real-world limits. Don’t treat your viva as a test you have to pass. Instead, treat it like the wonderful opportunity to enter your first academic conversation as a peer. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Cheat Sheet _This post is for paying subscribers only._ ### 5 Writing Strategies That Helped Me Break Perfectionism URL: https://lennartnacke.com/5-writing-strategies-that-helped-me-break-perfectionism/ Last updated: 2025-08-19T20:22:17.000Z #### Key Takeaways: How to Beat Perfectionism - ****Practice Imperfect Sharing:** Quickly draft and share a version 0.1 of your work to get comfortable with the idea that scholarship is an ongoing process. - ****Prepare for Criticism:** Don’t wait for reviewer feedback to surprise you. Create a critique library with pre-written responses to common feedback in your field. - ****Separate Your Drafts:** Use one document for messy brainstorming (“thinking writing”) and a separate, clean document for polished work (“performance writing”) to avoid editing while you create. - ****Focus on Your Process:** Measure your success by what you can control (e.g., paragraphs written), not by external results you can’t (e.g., publication acceptance). - ****Create Hard Deadlines:** Use public commitments and strict, non-negotiable timelines to force yourself to finish and ****ship your work**. > Learn n8n and AI automation for UX managers with out [***Bulletproof AI System: The New Standard for Ethical UX Masterclass***](https://lu.ma/4c6bohft?ref=lennartnacke.com)***.*** Become a confident AI leader who builds trust, orchestrates intelligent workflows, and guides your team through the current AI revolution. In 2008, J.K. Rowling stood before Harvard’s graduating class and admitted something that stopped me cold when I first heard it years later, sitting in my university office surrounded by half-finished manuscripts. She’d been unemployed, a single mother “as poor as it is possible to be in modern Britain without being homeless,” writing in cafés with her baby sleeping beside her. Her manuscript had been rejected by twelve publishers. At university, she’d been described as someone who prioritized her social life over studies, lacking ambition and enthusiasm. Yet she kept writing. Kept submitting. Kept moving forward without waiting for perfection. *A note on sources: I’ve chosen Rowling’s writing journey as an example because her experience with rejection, perfectionism, and eventual breakthrough offers concrete lessons for academic writers. This citation reflects solely on her professional writing process and persistence through failure , it’s not an endorsement of her recent public statements on social issues. The strategies that follow stand independent of any particular author’s personal views.* Meanwhile, there I was, a PhD with every academic credential, every research tool, every theoretical framework at my disposal, completely paralyzed by the prospect of submitting anything less than the best work I could possibly conceive of. Here’s what I’ve learned after coaching dozens of early-career researchers: Most of us know exactly what to do in theory. We understand methodology. We’ve read the literature. We know the publication process inside out. But when we sit down at our keyboard to write, we often freeze. Perfectionism kicks in. Fear of criticism takes over. The weight of having a permanent publication on record feels crushing. You start second-guessing every sentence, every citation, every claim. The only thing that really gets you is being a researcher who can’t get their work out into the world. PhDs who spend months polishing a single paragraph. Promising academics who abandon papers halfway through because they’re not good enough yet. In today’s issue, I’m sharing five field-tested strategies that target these psychological bottlenecks directly. These aren’t your typical timeblock and set-a-writing-schedule-type tips but more the stuff you should try when the basics aren’t enough. Let’s break the perfectionist paralysis once and for all. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **1\. Run the 72-hour idea-to-preprint sprint to rewire permanence anxiety** Most academic perfectionism stems from treating publication like it’s carved in stone forever. The reality is though that all good scholarship is iterative. Set up a deliberate impermanence system to get used to this idea. ### **The 3-day sprint to rewire your brain** 1. **Day 1: Scope & Outline.** Choose one small, specific claim you can support with your current data. Write a one-paragraph abstract and list five bullet points of what you know for sure. 2. **Day 2: Draft the Core.** Write only three short sections: your main result, a minimal description of your methods, and a limitations section admitting what could change your mind. 3. **Day 3: Position & Post.** Add a short *positioning note* citing 6-8 key articles. Post it as a preprint (even just for your lab group) labeled *Version 0.1* with a note like: “Provisional statement; updates planned in 4 weeks.” This trains your nervous system that publication is a sequence of reversible commitments, not a one-shot perfection test. You’re building confidence through controlled exposure to imperfect public sharing. ## **2\. Build immunity to criticism before it arrives** Fear of reviewer judgment paralyzes us because criticism feels unpredictable and devastating. But most academic criticism is surprisingly predictable. #### ****Create a critique library** 1. ****List Common Critiques:** Write down the 12 most common criticisms in your field (e.g., “sample size is too small,” “novelty is unclear,” “theory is weak”). 2. ****Pre-Write Your Replies:** For each critique, draft a 3-sentence template for a polite and constructive response. 3. ****Run Fire Drills:** Ask a trusted colleague to act as a harsh reviewer for 10 minutes. Practice responding calmly using your templates. Create a critique library with the twelve most common hits reviewers dish out in your field: Insufficient power, unclear novelty, weak theory, confound X. For each, pre-write a three-sentence reply template and a one-paragraph revision plan you could execute in forty-eight hours. Then run monthly *reviewer fire drills* where a trusted peer plays merciless reviewer for ten minutes while you respond using your templates. Put yourself in the hot seat. When real reviews arrive, you’ll have practiced this mindset for responses and scoped fixes ready. The cognitive load drops dramatically because you’ve already solved similar problems in advance. ## **3\. Separate thinking writing from *performance writing* to kill perfectionist churn** Perfectionism hits max volume when you try to discover ideas and communicate them simultaneously. Your brain can’t optimize for exploration and presentation at the same time. #### ****Maintain two writing tracks** - ****Track A (The Messy Notebook):** This is a private digital file for your eyes only. Free-write, dump half-formed ideas, record doubts, and use voice-to-text apps (like [Wispr Flow](https://wisprflow.ai/r/LENNART23?ref=lennartnacke.com)) to talk through your thoughts. No formatting, no citations, no rules. - ****Track B (The Performance Draft):** This is your clean, reader-facing document. It should contain less than 2,000 words and focus on six clear elements: Claim, Why It Matters, Method, Evidence, Limitations, and Next Steps. Maintain dual writing tracks: Track A is your messy lab notebook. Free-write analysis, doubts, half-arguments in a file that never gets shared. It’s just for you and your shenanigans. No citations, no formatting, voice memos if faster (I’ve been loving an app called [Wispr Flow](https://wisprflow.ai/r/LENNART23?ref=lennartnacke.com) lately to do voice dictations with AI). Track B is your reader-ready core. Six fixed elements under 2,000 words: 1. Claim 2. Why it matters 3. Minimal method 4. Evidence snapshot 5. Limitations 6. Next steps Daily rhythm: Twenty minutes in Track A dumping thoughts, then forty minutes in Track B shaping one or more paragraphs. End by moving one sentence from A to B. Weekly checkpoint: Share only Track B with one colleague for a single question: “What’s the smallest change that would increase your confidence?” ## **4\. Detach self-worth from acceptance outcomes through explicit learning bets** Our anxiety spikes when our identity depends on reviewer decisions, but we *can’t control* those. But we ***can control*** our learning rate and research capability. #### ****Measure your process, not outcomes** - ****Create a** ***Researcher Operating Agreement** **:** Write a one-page document defining your success based on weekly actions: 5 paragraphs drafted, 1 figure sketched, 1 critique solicited from a peer. - ****Make** ***Learning Bets** **:** Instead of saying “I will get this published,” set a testable learning goal like, “In six weeks, I will be able to defend why Method A is better than Method B for this problem.” - ****Score Yourself on Process:** Each month, grade yourself **only* on whether you completed your planned actions and achieved your learning goals. Rejections and acceptances are just data points, not a grade on your value. Write a one-page *researcher operating agreement* that defines your success metrics that you control weekly: Stuff like five paragraphs drafted, one figure sketched and explained, one external critique solicited. Add your values and non-negotiables. Then make public (again, in front of your lab or mentorship group is fine if you don’t want to do this completely in public), falsifiable *learning bets* per project: “In six weeks, I’ll defend why design X outperforms Y in setting Z with three pieces of evidence.” Each month, score yourself only on process metrics and learning outcomes. Acceptance and rejection get noted but not scored. This moves your identity to practice and capability growth, which are immune to reviewer variance. ## **5\. Engineer finishing through hard external constraints** Perfectionism expands to fill available time and open-endedness. Something something about [Parkinson’s law](https://en.wikipedia.org/wiki/Parkinson%27s%5Flaw?ref=lennartnacke.com), right? So, you want to replace vague goals with non-negotiable structures that force shipping. ### Engineer your finishing process | **Week** | **Deliverable** | **Exit Criteria** | | -------- | --------------- | --------------------------------------------- | | **1** | Abstract | States one testable claim and one limitation. | | **2** | Seminar Handout | Includes one key figure and one table. | | **3** | Workshop Slides | Passes a 10-item quality checklist. | | **4** | Short Preprint | Ready for internal or public sharing. | | **5** | Full Submission | Sent to target journal. | I would commit to a public deliverable ladder with each step exactly seven days apart: - Week 1: Abstract - Week 2: Seminar handout - Week 3: Workshop slides - Week 4: Short preprint - Week 5: Full submission Lock exit criteria in advance: Abstract states a testable claim and one limitation, handout includes one figure and key table, preprint passes a ten-item checklist. Use *venue-first scaffolding*: Start by mapping your target journal’s main article structures and paste section headings into your template before writing. It’s usually some version of IMRAD. Write to the existing shape rather than creating from scratch. Add co-author service level agreements: forty-eight hours for comments on early artifacts, five days for full submissions. Whatever suits your working style and expectations. The bottom line I’m getting is that as [Rowling discovered at rock bottom](https://www.larry-lewis.com/3233/a-failure-to-success-story-j-k-rowling?ref=lennartnacke.com): “I was set free, because my greatest fear had been realized, and I was still alive, and I still had a daughter whom I adored, and I had an old typewriter, and a big idea. And so rock bottom became a solid foundation on which I rebuilt my life.” Often we see perfectionism as a simple measure that imbues our incredibly high standards, when in reality this is just fear masked in academic clothing. Fear of judgment, fear of failure, fear of permanence. But when you systematically practice imperfection, prepare for criticism, separate modes, focus on learning, and engineer constraints, that stupid fear loses its power over you. Your research matters too much to stay trapped in the perfectionist’s paralysis. Start with strategy one this week. Set that seventy-two hour timer and prove to yourself that good enough to share is infinitely better than perfect but never published. That’s a crunch that might actually be worth it. The world needs your fresh ideas. Don’t let perfectionism keep them locked away. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## FAQ: Answering your top questions #### ****Q1: What does J.K. Rowling have to do with academic writing?** A: J.K. Rowling’s story illustrates a universal principle: rock bottom can be a powerful launchpad. Her success came **after* she let go of the fear of failure because she had already experienced it. For academics, this teaches us that we can practice imperfection to free ourselves from the paralysis of trying to be perfect. #### ****Q2: Is it realistic to produce a preprint in 72 hours?** A: The goal of the 72-hour sprint isn’t to produce a perfect, final paper. It’s a psychological exercise to create a **minimum viable product*. The point is to prove to yourself that you can share an idea quickly and that scholarship is iterative. It’s to build a habit of sharing, not to create a masterpiece in three days. #### ****Q3: Is perfectionism just a fancy word for procrastination?** A: Not exactly. While they can look similar, [procrastination](https://lennartnacke.com/i-tried-every-procrastination-hack-nothing-worked-until-i-understood-my-type/) is often about avoiding a task, while perfectionism is about avoiding a judgment on the finished task. Perfectionists often **do* the work but get stuck in an endless loop of refining and polishing because they fear their work will never be **good enough* to be judged. #### ****Q4: Which strategy should I start with if I’m feeling overwhelmed?** A: Start with ****Strategy 3: Separate Your Drafts**. It’s the easiest to implement immediately and provides the quickest relief. If you give yourself permission to be messy in one document, you immediately lower the pressure and make it easier to just start writing. ## Systematic Notion checklists for all 5 systems _This post is for paying subscribers only._ ### The PhD Supervision Secret That Universities Don't Want You to Know URL: https://lennartnacke.com/the-phd-supervision-secret-that-universities-dont-want-you-to-know/ Last updated: 2025-08-19T23:27:33.000Z #### Key Points - ****The real goal**: Show your supervisor you’re becoming an independent scholar, not just a task completer. - ****ACRES framework**: Anticipate needs, Communicate with structure, Research proactively, Execute consistently, Support their reputation - ****Strategic mindset**: Manage up; understand their priorities and pressures. - ****Proactive communication**: Send structured updates and lead with solutions, not problems. - ****Mutual benefit**: Align your progress with their goals and lab success. > Learn n8n and AI automation for UX managers with out [***Bulletproof AI System: The New Standard for Ethical UX Masterclass***](https://lu.ma/4c6bohft?ref=lennartnacke.com)***.*** Become a confident AI leader who builds trust, orchestrates intelligent workflows, and guides your team through the current AI revolution. Every PhD student has been on this rollercoaster. Sitting outside their supervisor’s office, palms sweaty, knees weak, arms heavy, but at least no vomit on your sweater from mom’s spaghetti. You’re hoping this conversation doesn’t turn into another list of everything you’re doing wrong. But more often than not, you walk away feeling defeated, confused about next steps, and wondering if your supervisor actually wants you to succeed. And no offence to any supervisors here, we struggle as much as you do in these situations. Meanwhile, you watch other students in your program cruise through milestones with other supervisors who seem genuinely invested in their success. What’s the secret sauce here? Is it just chemistry, or a fantasy maybe, caught in a landslide, no escape from reality? The truth is, most PhD students never learn how to manage UP. They think academic success is just about grinding through research, writing great papers, and hoping their supervisor just notices all their hard work. But the students who thrive? They’ve cracked the code on turning their supervisor from a roadblock into their biggest champion. Pom-poms, megaphone, and all that jazz. So, today, I’m going to show you the ACRES framework, five strategic shifts that transform advisor meetings from dreaded obligations into career-accelerating conversations. It will make your life better as a PhD student—and, hey, if you’re a supervisor yourself, giving this to your students might just get you some relief from struggling to advise better. > Most PhD students struggle with supervisors because they treat meetings like progress check-ins instead of strategic collaborations. The solution is the **ACRES framework**:Five shifts that turn supervision from stressful to career-accelerating: **Anticipate, Communicate, Research, Execute, Support**. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## The problem most PhD students get wrong There’s a common misconception that derails most doctoral students. They think it’s all about checklists and that supervisors want to see them completing tasks. Check off research objectives. Submit drafts on time. Show up to meetings with updates. You’re golden. But, hey, there’s more to it if you believe me. Turns out what supervisors actually want is evidence that you’re becoming an independent scholar who makes their life easier, not harder. They want early warning systems that allow them to support you when necessary. And they hate surprises in this journey as much as you do. They want to look good to their department, too. Nobody wants to field awkward questions about why their student is struggling. We are humans and feel embarrassed about this stuff, too, you know. The ACRES framework gives you a systematic approach to delivering exactly what your supervisors need, when they need it. When you anticipate their pressures and priorities, you become the student they actually want to work with. And you’ll see how they advocate for you even more. ## A: Anticipate their needs before they ask #### Here’s how - Learn your supervisor’s research agenda. Offer help beyond your dissertation (e.g., literature reviews for their grant proposals, slide reviews for conference talks). - Before meetings, play devil’s advocate on your own work; arrive ready with answers to predictable critiques. - Anticipation builds trust and shifts meetings toward strategic thinking, not problem-spotting. Your supervisors are ecosystems in themselves. It’s good advice to stop thinking only about your own dissertation and contribute to your supervisor’s broader research agenda. Look for ways to support their academic goals as well as your own. Ask what they’re working on and identify contribution opportunities. If they’re writing a grant proposal, offer to help with literature reviews. If they’re preparing for a conference, volunteer to review slides. Before every meeting with your supervisor, spend 20 minutes playing devil’s advocate with your own work. What are the obvious weaknesses? Which findings seem too convenient? What would a skeptical reviewer ask? Come ready to address concerns proactively: *“I know the obvious question about this result is \[X\], so I ran additional analyses to check \[Y\]. Here’s what I found…”* It’s a good way for you to show intellectual maturity and lets you use the meeting time for deeper strategic discussions instead of just identifying obvious problems. ## C: Communicate with structured reports #### Here’s how Replace scattered updates with a ****clear, consistent reporting format**: 1. ****Progress**: Key wins and milestones tied to thesis objectives. 2. ****Challenges**: Specific obstacles + your proposed solutions. 3. ****Support Needed**: Concrete asks with context and deadlines. 4. ****Next Week’s Goals:** Flag upcoming work and potential issues. **Also:* - Keep a detailed research log with the ***why** behind each decision. - Send a post-meeting summary within 24 hours that outlines agreed actions and timelines. Don’t send random email updates. Most of these get buried in their inbox anyways. Instead, send them just one weekly or monthly report (whatever they prefer) that positions you as organized, proactive, and easy to supervise. One great way for a report that contains regular updates is: 1. Your **progress** since last week and your current **focus**. Three key milestones tied directly to your thesis objectives. Progress updates on your top 3 research priorities. Any meaningful wins or breakthroughs. 2. Your **blockers** and challenges. 1–2 specific obstacles you’re facing. Your proposed solutions for each challenge. Resources or support you need to move forward. 3. Your needed **support.** You want to have specific asks with clear context and deadlines. Questions that show you’ve done preliminary thinking. And decisions you are facing where you need their expertise 4. Your **goals** for next week. Report on what’s coming up that they should know about. Potential issues you’re monitoring. Opportunities where your supervisor could provide strategic input. This format does three things: it shows you’re managing your project strategically, it gives them easy wins when they provide guidance, and it prevents those painful meetings where you’re both scrambling to remember what you discussed last time. Keep detailed research logs that track not just what you did, but why you made specific decisions. When questions arise months later about your methodology or approach, you can quickly provide context instead of trying to reconstruct your thinking. ## R: Research proactively beyond your immediate project #### Here’s how - Set [**Google Scholar*](https://scholar.google.com/?ref=lennartnacke.com) alerts for your keywords. - Spend 30 minutes a week scanning new publications. - Share relevant findings with a short note explaining why they matter for your work. This positions you as a ****thinking partner**, not just a task executor, and can lead to co-author invites and stronger references. Supervisors are more often than not drowning in research deadlines, grant applications, and administrative responsibilities. When you surface relevant findings, connect dots between emerging literature and your project, or flag important developments in your field, you position yourself as an intellectual partner to them instead of just another student needing deep instructions. Set up [Google Scholar alerts](https://scholar.google.com/scholar%5Falerts?view%5Fop=create%5Falert%5Foptions&alert%5Fparams=hl%3Den&hl=en&ref=lennartnacke.com) for key terms in your research area or use research discovery apps like [Researcher.Life](https://researcher.life/?ref=lennartnacke.com). Dedicate 30 minutes weekly to scanning new publications. When you find something relevant, send a brief note to your supervisor (but this also works with academic mentors in your field, who you’ve already talked to): *“Saw this new study on X that seems relevant to our discussion about Y. Key finding is Z, which might impact our approach to \[specific aspect of your project\].”* This positions you as an intellectually curious partner and it shows your supervisor that you’ve got your pulse on academic debates and emerging studies. This makes you the kind of student who gets invited to co-author papers even with other researchers outside of your research group and receives strong recommendation letters. ## E: Execute consistently on every commitment #### Here’s how - Always deliver when promised (and slightly early if possible). - If you commit to a draft, include methodology notes and context. - After every meeting, confirm next steps in writing. Reliability is one of the fastest ways to turn a supervisor into an advocate. Nothing destroys supervisor relationships faster than missed deadlines and broken promises. You want to build unshakeable reliability. You job as a great student is to build predictable systems your supervisor can rely on. This goes beyond consistency. It facilitates your work structures, too. Most students miss this opportunity of system building. If you say you’ll send a draft by Friday, send it by Thursday. If you commit to analyzing a dataset, include a brief methodology note with your results. If you schedule a follow-up conversation, come with an agenda and action items from your last discussion. Make it easy for them to know where you’re at every time they interact with you. After every meeting, send this within 24 hours: *“Thanks for today’s discussion. My understanding is that we agreed on \[X\], I’ll take action on \[Y\] by \[date\], and we’ll revisit \[Z\] in our next meeting. Let me know if I missed anything important.”* Your supervisor should never have to wonder about the status of your work or chase you for updates or outcomes. When they think of you, the word that should come to mind is “reliable.” The student who delivers what they promise, when they promise it, at the quality level they expect. Of course, this street goes both ways and you should seek out a supervisor with similar systems, of expected reliability (of deliverability or non-deliverability of their tasks). Document everything to prevent miscommunication. ## S: Support their reputation and success #### Here’s how - In healthy supervisory relationships, their success accelerates yours. - Contribute to **lab culture*: Mentor junior students, organize events, help with grant writing. - Publicly acknowledge their guidance in talks and papers. - If the relationship is toxic or exploitative, prioritize getting out: These principles only apply in mutually respectful environments. Your supervisor’s reputation directly impacts your career prospects. When they look good, you benefit. When they succeed, you succeed. This is true in most cases. But I know there are abusive situations in supervisory relationships (where it is clear students do no benefit at all) and if you find yourself in one of these, these rules do not apply. In such cases, you should do everything in your power to get out of that research group and the impact circle of that supervisor. But in most cases, your supervisor’s success isn’t just measured by your individual progress. They’re evaluated on their overall lab productivity, their reputation in the field, and their ability to train the next generation of researchers. In such positive environments, you want to look for ways to contribute to the collective success of your research group. Volunteer to mentor incoming students. Help organize lab meetings or journal clubs. Grow the culture together with your supervisor. Assist with grant applications or conference presentations. In presentations, acknowledge their guidance: “As Dr. \[Name\] suggested, we approached this problem by…” In papers, credit their theoretical insights. In conferences, mention how their research group’s approach influenced your methodology. Demonstrate that you understand that academia is collaborative and that you’re invested in the success of the broader research community. When colleagues see that their students are engaged, supportive, and contributing to their success, your supervisor becomes known as someone who trains exceptional researchers. This reputation benefits every student in their lab. When your supervisor introduces you to colleagues, they should be able to say: “This is \[your name\], one of my PhD students who really understands how research groups function and contributes meaningfully to our lab culture.” (And, hey, if you’re a supervisor reading this, make a point of saying this praise about your students in public, please.) ## Solve problems, don’t just flag them But here’s the advanced move you can do: **Lead with solutions first, never just problems.** This is a communication rule that separates mature PhD students from those who remain in perpetual student mode. Instead of: “I’m having trouble with my statistical analysis” try: “I’ve identified an issue with my regression model. I’ve researched three potential approaches: \[Option A\], \[option B\], and \[option C\]. Based on similar studies in our field, I’m leaning toward \[option A\] because \[specific reasons\]. Could we discuss the tradeoffs and get your input on the best path forward?” You don’t just want to flag problems to your supervisor, but show that you’re in the driver’s seat and that you’re taking real ownership of finding possible solutions to your problems. Your supervisor becomes a strategic advisor helping you choose between viable options, rather than a problem-solver who has to rescue you from basic research challenges. It’s a more mature relationship. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **Use the terminology and frameworks your supervisor values most** #### Speak Their Language - Note their preferred frameworks, terminology, and models. - Frame your work using these where appropriate . It makes your proposals easier to support. Every supervisor has preferred ways of thinking about and discussing research. Pay attention to the concepts they return to, the terminology they use, and the frameworks they find most compelling. If they frequently reference certain theoretical models, become fluent in those models and use them to frame your work. If they value specific methodological approaches, learn to articulate your research decisions using their preferred language. And, no, I’m not saying you should change your research to just match their preferences, but you should communicate your work in ways that resonate with their intellectual framework. When you speak their language, your ideas become more accessible and compelling to them. It’s a simple trick to easily get buy-in (and funding) when you want to try some new hot sh\*t. So, yeah, the most successful student are just the smartest or hardest working. The ones, who really kick butt in grad studies have gained a deep understanding of managing their supervisor relationship, which I think anyways is a crucial professional skill that will serve you long beyond your PhD. Master the ACRES framework, and you’ll change your supervisor meetings from dreaded obligations into strategic fun conversations that accelerate your career and theirs. Your supervisor will become your immediate supporter, your advocate, or even your champion. And, trust me, that makes all the difference in completing a successful PhD. Now go send that monthly progress report. ## Typical vs. ACRES PhD Student Comparison | **Aspect** | **Typical Approach** | **ACRES Approach** | | ----------------------- | --------------------------------- | --------------------------------------------- | | Meeting Prep | Arrives with updates and problems | Arrives with proactive solutions and insights | | Communication | Scattered emails | Structured, predictable reports | | Research Engagement | Focused only on thesis | Tracks field-wide developments and shares | | Reliability | Meets deadlines inconsistently | Consistently delivers early or on time | | Supervisor Relationship | Passive, dependent | Strategic, collaborative | ## FAQ #### Q1: What if my supervisor is unresponsive or vague? Start with structured reports anyway. They create a record and nudge clarity. #### ****Q2: Can ACRES work in competitive or political departments?** Yes, but be selective about how much you align your work with theirs if trust is low. #### ****Q3: How soon should I start applying ACRES?** Immediately. Early habits set the tone for the whole PhD. #### ****Q4: What if I’m in conflict with my supervisor?** First try reframing communication and clarifying expectations. If issues persist, involve a graduate coordinator or mentor. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). *Have you successfully applied ACRES? Leave a comment below.* ## Cheat Sheet _This post is for paying subscribers only._ ### What No One Teaches You About Tenses in Academic Writing URL: https://lennartnacke.com/what-no-one-teaches-you-about-tenses-in-academic-writing/ Last updated: 2025-08-13T21:18:59.000Z #### Key Points - Present tense builds credibility for established knowledge and current data relevance. - Past tense signals completed research methods and results. - Present perfect connects past studies to ongoing significance. - Future and conditional tenses allow for cautious projections and academic humility. - Strategic tense shifts clarify whether you’re stating facts, describing actions, interpreting results, or suggesting possibilities. > Reviewers subconsciously judge your professionalism from your tense use. Inconsistent tenses create cognitive friction, which make your work harder to follow and trigger doubts about your attention to detail. Consistent, purposeful tense use removes that friction and keeps the focus on your ideas. [Get the Academic Cheat Sheet PostersLearn everything about the academic storytelling framework, review paper structure, and quantitative paper structures.![](https://lennartnacke.com/content/images/icon/favicon-1.ico)![](https://lennartnacke.com/content/images/thumbnail/sAQZTsP9dzHxvnVjvfLjgh-1)](https://posters.lennartnacke.com/?ref=lennartnacke.com) > Get deep grant peer review feedback, manuscript writing support, turn research findings into posters & presentations, run initial lit reviews, do patent searching, visualize your data, and discover new grants all with SciSpace’s new AI Agent feature: [​Get 40% off on add-on credits now with code: **LENNASA40**](https://www.scispace.com/chat?via=lennart-pm&ref=lennartnacke.com) Last month, I was reviewing manuscripts for a top conference when something struck me. I could predict which papers would get harsh reviews within the first few paragraphs and it wasn’t because of weak research, but because of how they were written. Papers with inconsistent tenses created cognitive friction (not the sexy kind) that made me work harder to understand the authors’ logic. It was a mess. I found myself constantly translating between things like “the study showed” and “research demonstrates” and “findings will indicate” in the same paragraph. Of course, I’m exaggerating, but you get my point. One thing I’ve noticed after being an AC and papers chair for many years, is that when reviewers struggle to follow your writing, they become less charitable toward your ideas. They just get a little bit meaner. They start questioning your attention to detail. They wonder if the same carelessness that shows up in your verb tense use might extend to your methodology or analysis. It’s not fair, but it’s human nature. Clear, professional writing makes reviewers more receptive to your contributions, while confusing tense usage puts them in a critical mindset before they even reach your findings. You don’t want that. But, I’m ready for the rescue, because today I’ll show you exactly how to master academic tenses so your papers flow smoothly from sentence one , so you keep reviewers focused on your research instead of decoding your grammar. Ready to belly-flop into the details? I sure am. > Academic writing uses **different tenses for different rhetorical purposes**: present tense for established facts, past tense for your own completed actions, present perfect for linking past research to current understanding, and future/conditional for projections. Strategic tense shifts help readers follow your logic and trust your work. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **Present tense anchors established knowledge and builds immediate credibility** #### Use present tense for - Scientific laws or accepted theories - Findings that remain true and relevant - Your current interpretation of your own data ****Examples** - **Climate change affects global weather patterns.* - **The data indicate a strong correlation.* ****Why it works**: **Positions your paper within current scientific consensus and keeps findings relevant now.* Most academic writers think they need to use past tense for everything because all the research just happened in the past. Well, wipe that bird poop of your DeLorean, Doc, because that is is wrong — and it’s costing you credibility with your reviewers. When you discuss established knowledge, scientific laws, or widely accepted theories, present tense signals authority and permanence. “Climate change affects global weather patterns” carries more weight and meaning than “Climate change affected global weather patterns.” The first version positions your work within current scientific consensus, while the second makes it sound like climate change is no longer relevant. And as someone writing this while the sun is burning his neck in Canada of all places, it’s relevant alright. Present tense also works for discussing what your data shows. “The data indicate a strong correlation” is more powerful than “The data indicated a strong correlation” because it emphasizes the ongoing relevance of your data. Your analysis exists now, in the present moment, as readers engage with your paper. Let’s feel the flurry of excitement as our ANOVA produces some significant results with large effect sizes. So, one thing we want to remember is that present tense makes our research feel immediate and applicable rather than historical and outdated. ## **Past tense chronicles your research journey and demonstrates methodological rigour** #### Use past tense for - Methods and procedures - Data collection steps - Observations made during the study period ****Examples** - **We collected data from 500 participants.* - **Temperature readings increased by 3.2 degrees.* ****Why it works**: **Creates a clear timeline and signals rigorous, finished research.* Everything you actually did (like the process that happened) during your research should be described in past tense. This includes your methodology, data collection procedures, observations, and experimental steps. “We collected data from 500 participants over six months” clearly signals completed action. “Participants completed surveys between January and March 2025” provides the kind of temporal clarity that reviewers expect in rigorous academic work. Past tense also applies to what you observed during your study. “Temperature readings increased by 3.2 degrees” describes a specific observation that occurred during your research period. This creates a clear narrative timeline that helps reviewers follow your research process. The beauty of past tense in methodology sections is that it demonstrates you’ve actually completed the work. It builds trust by showing reviewers exactly what happened, when it happened, and how it happened. (And, of course, that you’re the one who did it.) ## **Present perfect connects historical research to current understanding** #### Use present perfect to - Show that past studies still influence current understanding - Indicate findings with ongoing relevance ****Examples** - **Studies have consistently demonstrated this relationship.* - **Researchers have identified three key factors.* ****Why it works**: **Turns your literature review into a current conversation rather than a historical list.* Here’s where most academic writers get confused (and where you can gain a significant advantage). Present perfect tense (“have discovered,” “has demonstrated,” “have shown”) bridges past research with present relevance. “Studies have consistently demonstrated this relationship” is more powerful than “Studies demonstrated this relationship” because it emphasizes ongoing significance. Marty McFly is coming back to the future. You’re not just citing old research here for funsies, but building everything on established patterns that remain crucial today. This tense works exceptionally well for literature reviews because it shows how past work contributes to current knowledge. “Researchers have identified three key factors” positions those factors as currently relevant discoveries, not historical artifacts. It creates academic continuity by linking what others found with what you’re contributing today. The strategic advantage? Present perfect makes your literature review feel like a living conversation rather than a museum tour of zombie research. ## **Future and conditional tenses project possibilities while maintaining academic humility** #### Use future tense or modal verbs (**could, might*) to - Suggest further research - Indicate potential applications - Avoid overstating certainty ****Examples** - **Further research could explore this relationship.* - **These findings might suggest broader applications.* ****Why it works**: **Shows academic humility and invites collaboration.* Academic writing demands intellectual honesty about what you don’t know. This a crucial part of writing any paper. Future tense and modal verbs (“could,” “might,” “may”) allow you to suggest directions without overstating certainty. “Further research could explore this relationship” acknowledges limitations while opening doors for future work. “These findings might suggest broader applications” presents possibilities without making claims you can’t support. This approach serves two critical purposes: It demonstrates academic humility and invites collaboration. Reviewers appreciate writers who acknowledge uncertainty rather than making grandiose claims. “This framework will revolutionize the field” sounds arrogant, while “This framework could provide new insights” sounds thoughtful and measured. It is a smart use of hedging to convince your peers of value. Clever use of conditional language more often than not protects you from reviewer criticism. When you present possibilities rather than certainties, you’re less likely to face pushback for overstating your contributions. ## **Tense shifts signal rhetorical purpose and guide reader understanding** #### Tense shifts as rhetorical signals Shifting tense intentionally tells readers your purpose: - ****Past** → ****Present**: Move from describing what you saw to interpreting it - ****Present Perfect** → ****Conditional**: Move from established patterns to future possibilities ****Examples** **We observed increased activity.* (past) **These findings suggest active engagement.* (present) The most sophisticated academic writers use tense strategically to signal different types of claims. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. When you move from past tense (“We observed increased activity”) to present tense (“These findings suggest active engagement”), you’re signalling a shift from description to interpretation. Readers unconsciously understand this progression, making your arguments easier to follow. That’s a bold power move for any writer. Similarly, moving from present perfect (“Research has shown consistent patterns”) to future conditional (“Future studies could examine variations”) takes readers from established knowledge to open questions. These tense shifts create logical flow that sophisticated readers expect in professional academic writing. The key insight here? Tense is so much more than just grammar. It lets readers navigate and understand what kind of claim you’re making at each moment in your paper. Tense always signals rhetorical purpose. So, shift tenses when the purpose of your communication shifts (e.g., fact, action, interpretation, projection). Do not treat my suggestions above as immutable grammar law. Follow the communication goal. Our takeaway today: Clear, consistent tense usage removes friction from your writing. It lets reviewers focus on what matters most: Your research contributions. Best of luck with your manuscripts. ## Which tense to use when (Comparison) | **Section / Purpose** | **Tense** | **Example** | | ------------------------------------ | -------------------- | ----------------------------------------------- | | Established knowledge / theory | Present | *Photosynthesis converts sunlight into energy.* | | Methods / completed actions | Past | *We conducted three experiments.* | | Past findings with current relevance | Present Perfect | *Studies have shown a link between X and Y.* | | Projections / limitations | Future / Conditional | *Further research could confirm these effects.* | ## FAQ #### ****Q1: Should I use past or present tense for my abstract?** Use past for what you did, present for what the results mean now. #### ****Q2: What tense is best for literature reviews?** Mix present perfect (link past to present) and present tense for widely accepted facts. #### ****Q3: Can I use multiple tenses in one paragraph?** Yes, if each tense clearly signals a different purpose — fact, method, or interpretation. #### ****Q4: Is it wrong to write the entire paper in past tense?** Not wrong, but you lose rhetorical nuance and risk making findings feel outdated. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Cheat Sheet PDF _This post is for paying subscribers only._ ### 5 Synthesis Techniques for Literature Reviews URL: https://lennartnacke.com/5-synthesis-techniques-for-literature-reviews/ Last updated: 2025-08-06T11:58:25.000Z > Learn n8n and AI automation for UX managers with out [***Bulletproof AI System: The New Standard for Ethical UX Masterclass***](https://lu.ma/4c6bohft?ref=lennartnacke.com)***.*** Become a confident AI leader who builds trust, orchestrates intelligent workflows, and guides your team through the current AI revolution. Got 99 research paper problems but can’t synthesize even one? I feel your problem. Most doctoral students think they’re synthesizing when they’re actually just summarizing with better organization. They’ll write things like “Smith (2021) found X, while Jones (2022) discovered Y, and Brown (2023) concluded Z.” That’s not synthesis, my friend. That’s an elaborate annotated bibliography. True synthesis creates *new knowledge* by integrating existing studies to say something that none of them said individually. The whole is more than the sum of its parts. It’s the difference between reporting what the field knows and advancing what the field currently understands. [Many of the students and some of the professors I have coached in the past](https://learn.lennartnacke.com/coaching-call/?ref=lennartnacke.com), struggle with this exact phase. So today, I’m going to show you the 5 synthesis techniques that transform your literature review from a summary into a genuine scholarly contribution that justifies your literature review. Let’s dig into them. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### **Technique 1: Detect patterns that show hidden narratives** The most powerful synthesis technique involves identifying trends that emerge when you step back from individual studies and look at the forest instead of the trees. Instead of listing what each study found, construct an overarching narrative about how the field has evolved to here. For example, rather than writing “Study A found correlation between social media and anxiety, Study B also found this correlation,” try something with a little more friction that shows a progression like: “A clear trend emerges in the recent literature, with research shifting from establishing whether social media affects mental health to investigating the specific mechanisms through which platform features like infinite scroll and algorithmic curation mediate these effects.” And then add the citations that back this up. Using such an angle shows sophisticated understanding of intellectual progression in your field. You’re moving beyond being a reporter of findings to a writer that takes the reader on a journey of academic thoughts. Look for temporal patterns, methodological evolution, theoretical shifts, and emerging consensus areas. You want to become a narrator of your field’s story that keeps the progression interesting. ### **Technique 2: Exploit contradictions to generate insights** The most valuable insights often hide in the tensions and disagreements within your literature, not in the areas of perfect agreement. Everyone loves a bit of friction if you know what I mean. 👀 When studies contradict each other, most students panic and try to explain away the differences. Instead, lean into such contradictions. Here you can really get your synthesis on. For example: “While large-scale surveys by Smith (2021) and Jones (2022) suggest robust correlations between screen time and depression, qualitative research by Chen (2023) reveals that online communities provide vital social support for isolated individuals. Hence, the relationship isn’t linear but depends entirely on engagement quality, not quantity.” Boom, what a nice qualifier curveball. This technique allows you to switch out apparent problems into analytical opportunities. You’re going from just noting simple disagreements to using them to propose an argument for a better understanding. Look for contradictions in findings, methods, populations, or contexts, then argue what these tensions show us about the complexity of your phenomenon. ### **Technique 3: Map gaps onto intellectual problems** You should already [know my feelings about people throwing around just gaps without problems in papers](https://lennartnacke.com/why-research-gaps-are-bullsh1t/) from my past writing. Because really a gap, the way most academics use it, isn’t something you just find, it’s really more of an argument you construct using the evidence you’ve systematically gathered. And then why not just turn it into an outright problem that needs fixing? Most students treat gap identification like a scavenger hunt: “Oh look, no one studied X population. Must be knowledge gap!” And infinite knowledge means you can find infinite gaps in knowledge. Welcome to the gap pandemic in modern academic writing and the club of easy research paper motivations. But more sophisticated gap-mapping involves four more strategic approaches. Knowledge gaps point to understudied populations, untested interventions, or unmeasured outcomes (and yes, most people still use these everywhere). Methodological gaps critique how topics have been studied (maybe everyone uses cross-sectional designs when longitudinal studies are needed). Theoretical gaps suggest applying new frameworks to existing findings. Contradiction gaps position your research as resolving debates between conflicting studies. I talk more about what has been written about the [7 research gaps in an older issue of the newsletter](https://lennartnacke.com/the-7-research-gaps/). The key though is justifying why your identified gap matters most. Don’t be lame and just say “this hasn’t been studied before.” Instead, I challenge you to argue why studying it would advance understanding, inform practice, or resolve theoretical debates. Your gap should feel like it’s giving rise to an inevitable problem based your synthesis, and not just be arbitrary. ### **Technique 4: Build bridges between disconnected literature** One of the most powerful contributions involves demonstrating previously unrecognized connections between separate fields of study. This technique requires intellectual courage, though. Because here you’re arguing that scholars in different domains are actually studying related phenomena without realizing it. And many researchers are not fans of getting finger-pointed at. Even if it’s from people outside of their field. For example, a good way to pitch it is like this: “We integrated insights from urban planning and public health research. Thus, this systematic review proposes a ‘socio-ecological model of community well-being’ that shows how built environment features interact with social determinants to influence mental health outcomes.” Start by identifying concepts that appear in lots of literature under different names. Environmental psychology’s “restorative environments” might connect with health research on “stress recovery” and urban planning’s “biophilic design.” (Don’t get me started on this, because I work in a field, where the differences between human-computer interaction, human-centred design, user-centred design, user experience, and many more names, are rather ephemeral and everyone is just trying to claim their stakes.) Your synthesis reveals these connections and proposes integrated frameworks that neither field developed independently. And maybe reviewers will take to it or not. It’s a bold strategy, Cotton. Let’s see it if pays off for ‘em. ### **Technique 5: Advance theses that rise above individual studies** The highest level of synthesis involves arguing that collective evidence points to conclusions more advanced than any individual study reached. It’s like you are watching the magic of the 1996 Chicago Bulls, knowing that Michael Jordan, Scottie Pippen, and Dennis Rodman were all superstar players, but the dream team was the synergy that Coach Phil Jackson was able to coordinate between every single player on the team. This isn’t about cherry-picking your studies based on whether they support predetermined arguments. Instead, you’re demonstrating that when synthesized systematically, the evidence shows patterns (or even principles) that individual researchers couldn’t see from their limited vantage points. Your thesis emerges from the synthesis process. That’s quite different from being imposed on it. For example, individual studies might each find modest effects of different interventions on academic performance. Your synthesis might reveal that all effective interventions share common features (like personalized feedback and spaced practice), leading to a new thesis about the essential elements of educational effectiveness. You’re proposing something new while remaining grounded in the existing evidence. The goal when using this technique is intellectual leadership. It allows you to position yourself as someone who can see patterns and possibilities that others missed (not because you’re smarter, but because you’ve done the systematic work of gathering and analyzing evidence in depth). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Remember, in this last phase of your literature review, you have the opportunity to transform it from competent scholarship into a strong scholarly contribution. You’re no longer just demonstrating that you’ve read everything. No, now, you’re actually showing that you can think with everything you’ve read. Master these five synthesis techniques in today’s issue, and your literature review becomes the foundation that makes your literature review feel not just justified, but simply inevitable. ## Ultimate Literature Review Synthesis Cheat Sheet Today, [paid subscribers get an exquisite cheat sheet](#/portal/signup). _This post is for paying subscribers only._ ### The 8-Factor System That Separates Real Research From Academic Garbage URL: https://lennartnacke.com/the-8-factor-system-that-separates-real-research-from-academic-garbage/ Last updated: 2025-10-30T19:09:22.000Z 💡 The ****GRADE framework** evaluates scientific evidence across eight key factors that either downgrade or upgrade certainty. It transforms evidence review from opinion into a reproducible, transparent method used by WHO, Cochrane, and 100+ global health organizations. #### Key points - GRADE turns evidence evaluation into a structured, transparent system. - 5 factors downgrade certainty: bias, inconsistency, indirectness, imprecision, publication bias. - 3 factors upgrade certainty: large effects, dose–response, and plausible confounding. - Four evidence levels: High, Moderate, Low, Very Low. - Used worldwide to determine which research actually deserves trust. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) I remember the exact moment I realized most people (including many researchers) fundamentally misunderstand how to evaluate scientific evidence. I was deep into reviewing yet another systematic review paper in HCI when I hit my breaking point. The authors had dutifully collected dozens of studies, categorized them neatly, and drawn sweeping conclusions about user experience interventions. But as I read through their methodology, a familiar frustration crept in. I could not see them using a systematic way to assess whether we could actually trust any of this evidence they had collected. Sure, they’d noted which studies were experimental versus observational studies. They’d counted sample sizes and noted statistical significance. But nowhere did they grapple with the fundamental question that should drive every research synthesis: How confident should we be in these findings? | **Category** | **Factor** | **Effect on Certainty** | **What It Means** | | ---------------- | ------------------------------ | ----------------------- | ---------------------------------------------------------------- | | **Down- grades** | **Risk of Bias** | ↓ | Weak design or poor execution (e.g., no blinding, high dropout). | | | **Inconsistency** | ↓ | Conflicting results without clear explanation. | | | **Indirectness** | ↓ | Different population, dose, or outcome than your question. | | | **Imprecision** | ↓ | Small samples or wide confidence intervals. | | | **Publication Bias** | ↓ | Missing or unpublished negative studies. | | **Up- grades** | **Large Effect Size** | ↑ | Clear, strong effect unlikely to be due to chance. | | | **Dose–Response Relationship** | ↑ | Larger exposure consistently yields larger effect. | | | **Plausible Confounding** | ↑ | Biases would make the effect smaller, not larger. | And I wasn’t just upset the study didn’t use [​the many critical appraisal tools that I talked about in last week’s newsletter​](https://lennartnacke.com/how-to-filter-10-000-papers-down-to-50-studies-that-matter/) (though those are sorely lacking in HCI). What I found in front of me was the complete absence of a framework to evaluate evidence certainty. A paper could cite fifty studies and still leave readers with no clear sense of whether the conclusions were rock-solid or dipped deep into Jello. ![The hierarchical levels of evidence displayed as a pyramid/triangle structure.](https://lennartnacke.com/content/images/2025/07/Systematic-Reviews---Meta-Analysis.webp) The Evidence Pyramid As an aside, yes, the [​hierarchy of evidence​](https://openmd.com/guide/levels-of-evidence?ref=lennartnacke.com) definitely also matters for finding quality research. Meta-analyses and systematic reviews sit at the top of this evidence pyramid, because they synthesize findings from multiple studies. Similarly, critically appraised syntheses and articles count as the top category. Below these are RCTs, which provide strong evidence through careful experimental design. Observational studies, cohort studies, regular literature reviews, and non-RCTs are in the middle of the hierarchy, while case reports, animal, and in-vitro studies occupy lower levels of the hierarchy but still contribute valuable insights in specific contexts. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Knowing how this hierarchy works lets you choose better sources for your paper. For example, if you’re researching a medical treatment’s effectiveness, a meta-analysis combining results from multiple RCTs would provide stronger evidence than a single case study. However, critical thinking is still essential when applying the hierarchy. While meta-analyses and systematic reviews generally provide the strongest evidence, you should also consider factors like study recency, relevance to your specific question, and potential biases. Anyways, after that frustration when reviewing that paper was when I discovered the [​GRADE framework​](https://www.bmj.com/content/328/7454/1490?ref=lennartnacke.com), a systematic approach that over 100 organizations, including the World Health Organization and Cochrane, use to cut through the noise and determine which evidence actually deserves our trust (see the [​GRADE framework handbook​](https://gdt.gradepro.org/app/handbook/handbook.html?ref=lennartnacke.com)). ## The problem with how we think about evidence Most people treat evidence evaluation like the simple hierarchy pyramid that I introduced above. Systematic reviews and randomized controlled trials sit at the top levels, observational studies somewhere in the middle, and everything else falls closer to the bottom. But this oversimplified view misses a crucial point: quality varies dramatically within each category. A poorly designed RCT with serious bias problems might actually be less reliable than a well-executed observational study with clear results. The study design is just the starting point for a good paper but not the final word. This is where GRADE changes how we evaluate evidence. Instead of relying on crude hierarchies, it asks the right question: “After finding and appraising all the relevant studies, how confident should we be in the estimate of effect?” The beauty of GRADE lies in its systematic approach to answering this “so what?” question. After you’ve found and appraised all the relevant studies, GRADE forces you to step back. The system produces four distinct certainty ratings: High, Moderate, Low, or Very Low. Each rating comes with a clear interpretation that removes ambiguity from evidence evaluation. Your starting point depends entirely on study design. Evidence from randomized controlled trials begins at high certainty (e.g., these studies are designed to minimize bias and establish causal relationships). Observational studies start at low certainty because they’re more susceptible to confounding and can only establish associations, not causation. But here’s where it gets interesting: that initial rating is just the beginning of the evaluation process. The real work happens when you apply the eight factors that can modify this starting point. ## **Five factors that drag your evidence certainty down the ladder** Risk of bias is the first culprit that can sink your evidence rating. If your body of evidence consists mostly of studies with serious methodological flaws (think inadequate randomization, lack of blinding, or high dropout rates) your certainty gets downgraded. Tools like [​RoB 2​](https://doi.org/10.1136/bmj.l4898?ref=lennartnacke.com) for randomized trials and [​ROBINS-I​](https://www.riskofbias.info/welcome/robins-i-v2?ref=lennartnacke.com) for observational studies help identify these issues systematically. Inconsistency hits when studies show wildly different results that can’t be explained. If one study shows a treatment reduces heart attacks by 50% while another shows no effect at all, and you can’t determine why, your confidence in the overall estimate should plummet accordingly. Indirectness occurs when the evidence doesn’t directly answer your research question. Maybe the studies looked at a different population than the one you’re interested in, or they used a different intervention dose, or they measured outcomes differently. Each mismatch between your question and the available evidence chips away at certainty. Imprecision shows up when results are statistically fragile: Small sample sizes, few events, or confidence intervals so wide you could drive a big Mack Truck through them. If the true effect could be substantially different from what the studies suggest, you can’t be confident in the estimate. Publication bias is the sneakiest one. If there’s strong suspicion that studies with negative or unfavourable results never saw the light of day, your evidence base is skewed toward positive findings. Funnel plot analyses can help detect this problem, but it’s often difficult to quantify, so remain on alert. ## **Three factors that drive observational evidence ratings up** Here’s where GRADE gets really sophisticated. For observational studies that start at low certainty, certain conditions can actually upgrade the rating. A large effect can bump up your confidence level. If observational studies consistently show a massive effect (say, a relative risk greater than 2 or less than 0.5) and there’s no plausible confounding that could explain it away, you can upgrade from low to moderate certainty. The logic here is simple: It’s harder for bias to create a large effect than a small one. A dose-response gradient strengthens the case for causation. If higher doses or more intense exposures lead to greater effects in a predictable pattern, this biological plausibility increases confidence that you’re looking at a real causal relationship rather than just a correlation. Plausible confounding that would reduce the observed effect is the trickiest upgrade factor. If all the confounding factors you can think of would have actually made the intervention look worse, not better, then the true effect might be even stronger than what the studies show. ## **The final ratings give you actionable confidence levels** After working through all the upgrading and downgrading factors, you end up with one of four certainty levels, each with clear practical meaning. High certainty means you’re highly confident the true effect lies close to the estimate. This is your gold standard: The kind of evidence that should drive clinical guidelines and policy decisions without much hand-wringing. Moderate certainty indicates you’re reasonably confident, but there’s a possibility the true effect could be substantially different. You might proceed with recommendations but acknowledge the uncertainty and monitor for new evidence. Low certainty means your confidence is limited. The true effect may be substantially different from the estimate, so recommendations should be conditional and preferences should weigh heavily in decision-making. Very low certainty is essentially a warning flag. You have only little confidence in the effect estimate, and the true effect is likely to be substantially different. Any recommendations should be tentative at best. ## Why this framework changes everything The beauty of GRADE lies in its systematic transparency. It transforms evidence evaluation from an art into a science. Instead of vague statements about study quality, you get transparent, systematic assessments that anyone can understand and replicate. This transparency is crucial because it forces explicit consideration of all the factors that affect evidence quality. No more black-box decisions where experts mysteriously conclude that evidence is *good* or *bad* without explaining their reasoning. For researchers, clinicians, and policymakers, GRADE provides a common language for discussing evidence quality. When someone says the evidence is moderate certainty, everyone knows exactly what that means and what factors led to that rating. The framework also helps you identify where future research efforts should focus. If evidence is downgraded for imprecision, you need larger studies. If inconsistency is the problem, you need to figure out why studies disagree. This targeted approach makes research more efficient and impactful. The GRADE framework gives us a common language for discussing evidence quality. When someone says the evidence has “moderate certainty,” everyone knows exactly what that means and what factors led to that rating. This helps us make better decisions with incomplete information, which is what science and life are really about. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Learn more about GRADE GRADE Working Group. (2004). [​Grading quality of evidence and strength of recommendations​](https://doi.org/10.1136/bmj.328.7454.1490?ref=lennartnacke.com). *BMJ*, *328*(7454), 1490. ### Paid Subscriber Bonus _This post is for paying subscribers only._ ### How to filter 10,000 papers down to 50 studies that matter URL: https://lennartnacke.com/how-to-filter-10-000-papers-down-to-50-studies-that-matter/ Last updated: 2025-09-04T19:09:41.000Z > We're running a new responsible AI Masterclass called [Preventing AI Harm in the Real World](https://lu.ma/4c6bohft?ref=lennartnacke.com) and we are offering only 30 spots for the live Masterclass on Thursday, August 28, 2025, 10:00 AM - 1:00 PM EDT. [Join us before all spots are taken.](https://lu.ma/4c6bohft?ref=lennartnacke.com) I was in yet another Tuesday midnight session, where the clock was pushing 2 AM as I was staring at 8,347 search results on my flickering laptop screen. Coffee cups littered my desk. My eyes burned. I had been researching for three weeks straight and my knees were letting me know. But I was jumping from paper to paper like a cocaine rabbit. My notes would make very little sense the next morning, I thought. My last reviewer’s words echoed in my head: “Your literature review lacks systematic rigour.” You’re in deep water, and it shows, man. What am I even doing here. That night, I made a decision that changed everything. Forever. All at once. Instead of reading yet another random paper, I would take the time to learn how actual researchers (you know, the ones whose systematic reviews get published in top journals) actually filter thousands of papers into meaningful insights. What I discovered was a lifeline that transformed my relationship with academic research forever. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Information overload is real Here’s what nobody tells you about academic research: The ability to find information has never been the problem. I mean, we were pretty damn slow at it once, but with computers everything just got much fast. Now, PubMed alone contains over 34 million citations. Google Scholar? Millions more. The real challenge, though, is learning to systematically eliminate 99% of what you find. That’s the secret trick that separates successful researchers from those who burn out. Most researchers approach this totally backwards. They try to read everything. They hope that the patterns in the literature will just magically emerge from this grindwork. Even worse, they might give up or resort to cherry-picking studies (both of which are career killers). The difference between a published systematic review and an abandoned project often comes down to mastering Phases 3 and 4: the screening and synthesis phases. [I’ve briefly hinted at these in my last newsletter issue](https://lennartnacke.com/4-strategic-planning-phases-to-master-lit-reviews/#3-systematic-screening-and-quality-assessment-with-documentation-protocols). But high-impact systematic reviews work differently. You build them on a methodological backbone that transforms your mountain of papers into defensible, publishable insights. Without a systematic approach here, you’ll either miss crucial studies, include low-quality research ,or — worse—get torn apart by peer reviewers who spot your methodological flaws. After studying the methodology behind dozens of published systematic reviews and implementing this approach across multiple research projects, I’ve learned that successful literature reviews don’t rest on reading more but hinge on making better decisions with the literature you’ve found. ### First search the titles and abstracts Your first job is to eliminate 70–90% of your retrieved records through rapid title and abstract screening. This process typically involves: 1. **De-duplication.** The first action is to import all search results from all databases into a reference management software program like [Zotero](https://www.zotero.org/?ref=lennartnacke.com) (free), [Paperpile](https://paperpile.com/?ref=lennartnacke.com), or [EndNote](https://endnote.com/?ref=lennartnacke.com). These programs have built-in tools to identify and remove duplicate records. This alone can cut your workload by 20–30%. Basically, you an get a clean starting set for your literature review. 2. **Applying Inclusion/Exclusion Criteria.** You then read each unique record’s title and abstract and evaluate it against the pre-defined inclusion and exclusion criteria from your protocol. Common reasons for exclusion at this stage include irrelevant topic, wrong population, or wrong study design. 3. **Using Screening Tools.** For large reviews, specialized software can facilitate this process. Tools like [Covidence](https://www.covidence.org/?ref=lennartnacke.com) or [Rayyan AI](https://www.rayyan.ai/?ref=lennartnacke.com) or provide a platform where reviewers can quickly vote “include,” “exclude,” or “maybe” on each title/abstract, and this is particularly useful for managing the process when two independent reviewers are involved. 4. **Conservative Decision-Making.** The guiding principle at this stage is to be conservative. If there is any doubt about a study’s relevance based on its title and abstract, it should be promoted to the next stage of full-text review. It is far better to assess a few extra full texts than to prematurely and incorrectly exclude a potentially crucial study. ### Deep analysis (where quality matters) Every paper that survives your abstract screening gets the full-text treatment. This is where your research skills truly matter, because not all published studies deserve equal weight in your conclusions. For every paper you exclude at full-text review, document the specific reason. Not “didn’t meet criteria” but “Excluded: intervention duration was 4 weeks, protocol required minimum 8 weeks.” After screening hundreds of papers, you’ll start recognizing instant elimination patterns: - Wrong population (paediatric studies when you need adults) - Wrong timeframe (historical studies when you need current interventions) - Wrong geography (developing nation contexts when you’re studying US healthcare) - Wrong study type (case reports when you need controlled trials) This meticulous documentation serves three purposes: 1. **Transparency:** Anyone can follow your decision-making process 2. **Consistency:** You can’t accidentally apply different standards to different papers 3. **Defense:** When questioned about your methodology, you have specific, documented answers ### The PRISMA Flow Diagram The [PRISMA flow diagram](https://www.prisma-statement.org/prisma-2020-flow-diagram?ref=lennartnacke.com) is more than just the academic decoration that comes with a systematic review paper, but it’s often used as your shield against accusations of cherry-picking. A well-constructed diagram tells the story of your systematic decision-making. ![](https://cdn-images-1.medium.com/max/800/0*mnq8coym0oKMCv9F.png) An example of a [PRISMA flow diagram](https://www.prisma-statement.org/prisma-2020-flow-diagram?ref=lennartnacke.com). Your diagram should document: - **Initial Search:** Total records identified across all databases - **After De-duplication:** Clean dataset for screening - **Abstract Screening:** Number screened and excluded with reasons - **Full-Text Assessment:** Number assessed and excluded with specific reasons - **Final Inclusion:** Studies included in your synthesis When an reviewer sees “1,247 full-text articles excluded” with documented reasons, they immediately understand you conducted a systematic, not arbitrary, process. Every number needs to be defensible. Your PRISMA flow diagram tells the story of your research journey from thousands of potential records to your final curated set. ### Quality appraisal The most sophisticated insight from systematic review methodology is that publication doesn’t equal quality. Some studies are methodologically rigorous; others have serious flaws that undermine their conclusions. I’ve mentioned tools like [CASP](https://casp-uk.net/?ref=lennartnacke.com), [ENTREQ](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-12-181?ref=lennartnacke.com), and [JBI](https://jbi-global-wiki.refined.site/space/MANUAL?ref=lennartnacke.com) before. [But I actually curated 31 different appraisal tools on a Notion page for my paid subscribers in this issue today](https://lennartnacke.com/#/portal/signup). Each appraisal tool checklist (or tool) helps you check if research is done well and reported clearly. Choose the tool that matches your study type, and remember that higher scores or better ratings usually mean you can trust the research more. They are systematic ways to identify which studies you can trust and which require cautious interpretation Applying the wrong tool is a serious methodological error that signals you don’t understand study design fundamentals. The quality assessment isn’t an endpoint but a critical input for your synthesis phase. You want to be able to weight studies appropriately and discuss limitations intelligently. ### From papers to insights via synthesis This phase, lasting approximately 2–3 weeks, serves as the bridge between collecting literature and generating new insights. Here, we systematically extract key information from the included studies and organize it in a way that facilitates analysis. The raw, unstructured text of research papers gets transformed into structured, analyzable data. Typically this is a spreadsheet or a dedicated form within review software. That data is the groundwork for the synthesis to come. A standardized data extraction form is essential. Before full implementation, this form should be pilot-tested on two or three included papers and you should really check that it captures all necessary information clearly and unambiguously. A typical extraction form would include fields for: - **Bibliographic Details:** Author(s), Year of Publication, Journal Title, DOI. - **Study Characteristics:** Country, Setting, Study Design (e.g., RCT, cohort study), Study Aims. - **Population Details:** Sample Size, Key Demographics (e.g., age, gender), Inclusion/Exclusion Criteria for participants. - **Intervention/Exposure:** A detailed description of the intervention, treatment, or exposure being studied. - **Comparison Group:** A detailed description of the control or comparison condition. - **Outcomes:** The specific outcomes measured, the tools or methods used for measurement, and the time points at which they were assessed. - **Key Findings:** The main results of the study, including quantitative data (e.g., effect sizes, p-values, confidence intervals) and key qualitative findings. - **Quality Assessment:** The final judgment from the quality appraisal conducted in Phase 3 (e.g., “Low risk of bias”). - **Reviewer Notes:** A space for the reviewer’s qualitative observations, important direct quotes, or reflections on the study’s contribution. Once you have your final studies, resist the urge to summarize them one by one. Instead, create what systematic reviewers call [a synthesis matrix](https://acagamic.notion.site/Literature-Synthesis-Matrix-22befbdbbfa380b4bb74edf9afcd7540?ref=lennartnacke.com) (a table with [studies as columns and key ideas or themes as rows](https://case.fiu.edu/writingcenter/online-resources/%5Fassets/synthesis-matrix-2.pdf?ref=lennartnacke.com)). [![Literature Matrix](https://cdn-images-1.medium.com/max/800/1*d5n6fOCXyI3wBCe7EdvMSQ.png)](https://acagamic.notion.site/Literature-Synthesis-Matrix-22befbdbbfa380b4bb74edf9afcd7540?ref=lennartnacke.com) Example of a [Literature Synthesis Matrix](https://acagamic.notion.site/Literature-Synthesis-Matrix-22befbdbbfa380b4bb74edf9afcd7540?ref=lennartnacke.com) This simple reorganization transforms your cognitive process. Instead of vertical reading (one paper at a time, which encourages summarization), you shift to horizontal reading (comparing what all papers say about specific themes, which forces analysis). For example, when you read across an *intervention fidelity* row line and see that Study A had trained therapists, Study B used graduate students, and Study C provided no training details, you’re identifying variables that might explain conflicting results and are not just cataloguing results. You can also use [thematic analysis](https://link.springer.com/rwe/10.1007/978-1-4614-5583-7%5F311?ref=lennartnacke.com) to identify patterns across your extracted data. Thematic analysis is a qualitative method used to identify, analyze, and report patterns (or themes) within a dataset. For a literature review, the dataset consists of the findings extracted from the included studies. The themes identified through this process often become the subheadings and core arguments of the final written review. The process is iterative and typically follows several steps : 1. **Familiarization:** You deeply engage with the data. In a literature review, you carefully read the full-text in screening and data extraction stages. 2. **Generate Initial Codes (Open Coding):** You attach short, descriptive labels (codes) to excerpts of the extracted data. For instance, a finding like “women were only paid 2/3 of what men were for doing identical tasks” could be coded as `pay inequity`. This initial stage is often *inductive.* The codes come directly from the data rather than being predetermined. 3. **Search for Themes (Axial Coding):** You interpret and examine the initial codes and begin to group them into broader, more abstract categories or themes. This is where your own analytical contribution begins. The act of grouping codes like `pay inequity`, `social ridicule`, and `assignment to inferior planes` under a higher-level theme such as `Systemic Institutional Resistance` is an act of interpretation that proposes a conceptual structure for understanding disparate findings. 4. **Review and Refine Themes:** The potential themes are then reviewed against the dataset. Are they coherent? Is there sufficient evidence to support each one? Are they distinct? Some themes may be merged, some split, and others discarded. 5. **Define and Name Themes:** Once the final thematic structure is settled, each theme is given a clear, concise name and a detailed definition that explains the concept it represents. This process can be either primarily inductive or deductive. An **inductive approach** is bottom up. So, themes are freely formed from the data, which is ideal for exploratory reviews. A **deductive approach** is top down, where you begin with a pre-existing theory or framework and search the data for evidence related to those specific concepts. Many PhD literature reviews use a combination of both approaches. ### What this means for your research journey Mastering systematic literature screening changed more than my PhD trajectory. It fundamentally altered how I approach any complex information challenge. The same principles that help you filter 10,000 papers to 50 studies also help you make better decisions about job opportunities, investment choices, or even which Netflix series deserves your limited time. Work those research skills, Sheldon. The researchers who excel aren’t the best read ones, but the ones with systems that let them identify what matters. They understand that in an age of information abundance, the scarcest skill isn’t access to knowledge but the ability to filter signal from noise. That’s your best skill. When you can defend every inclusion and exclusion decision with documented criteria, your literature review turns into evidence of systematic thinking that will serve you throughout your career. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Your implementation strategy Start your next literature review with this systematic foundation: **Week 1:** Develop and document your five-pillar inclusion criteria **Week 2:** Complete title/abstract screening using the three-bucket system **Week 3:** Conduct full-text screening with documented exclusions **Week 4:** Complete quality assessment and build your synthesis matrix You want a systematic review and not a literature dump, so success here is not determined by the number of papers you can cite but the transparency and rigour of your selection process. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## 31 Research Appraisal Tools _This post is for paying subscribers only._ ### 4 Strategic Planning Phases to Master Lit Reviews URL: https://lennartnacke.com/4-strategic-planning-phases-to-master-lit-reviews/ Last updated: 2025-07-02T11:19:22.000Z What separates grad students who complete exceptional literature reviews in 3–4 months from those who struggle for over a year with mediocre results? It’s not access to better databases, AI, writing talent, or even research experience. The difference lies in understanding that proper literature reviews (the kinds you publish as full papers) follow a systematic, phase-based process that builds momentum and quality when executed strategically. Most students treat literature reviews as one giant, overwhelming task instead of four distinct strategic phases, each with specific objectives, methods, and success criteria. They jump between searching, reading, and writing randomly, creating inefficiency and inconsistency that sabotages their entire research foundation. Elite researchers understand that mastering each phase sequentially creates compound advantages that accelerate their timeline while dramatically improving the quality of their literature review. Alright, my friend, let’s break down the 4 strategic planning phases that transform literature reviews from overwhelming academic exercises into systematic research advantages. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 1\. Strategic planning and research question architecture Top-tier researchers begin with formulating a systematic question that drives every subsequent decision in their literature review process. This phase involves using proven frameworks to construct precise, actionable research questions. A few examples here. [PICO (Population, Intervention, Comparison, Outcome)](https://www.sciencedirect.com/science/article/pii/S1471595321002304?casa%5Ftoken=DtpeHxgoi3kAAAAA:XDqVIifi8Aq5WSClKZ4fy7utn-kSweeV6VQ5dGYuE%5FvLt0Kb5zlczdRfXIPgLvk1tlGCtmpC9ZbW&ref=lennartnacke.com) structures intervention-focused questions, while [PECO (Population, Exposure, Comparison, Outcome)](https://pmc.ncbi.nlm.nih.gov/articles/PMC6908441/?ref=lennartnacke.com) works for observational studies. [SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research Type)](https://journals.sagepub.com/doi/abs/10.1177/1049732312452938?ref=lennartnacke.com) provides the framework for qualitative research questions. These aren’t just cool acronyms, they’re helpful tools for your entire methodology. But here’s the strategic insight most PhD students miss: this phase also requires methodology selection based on your specific timeline and research complexity, not simply personal preference. Systematic reviews demand 6–12 months for narrow, focused questions with substantial existing literature. Scoping reviews work better for broad topic exploration within 2–4 month timelines. Mapping studies excel for demonstrating deep field understanding while identifying focused research opportunities. Yes, AI can summarize papers nicely, but you have to do the leg work for a proper review. The methodology you choose in this phase determines your resource allocation, timeline, and final deliverable quality. ## 2\. Thorough search strategy development and execution Successful early-career researchers implement multi-database search strategies that maximize coverage while maintaining systematic rigour. This phase starts with database selection based on disciplinary coverage: [Google Scholar](https://scholar.google.com/?ref=lennartnacke.com) for broad academic scope and citation tracking (but never just settle on this as your only database), [PubMed](https://pubmed.ncbi.nlm.nih.gov/?ref=lennartnacke.com) for health research with MeSH precision searching, [IEEE Xplore](https://ieeexplore.ieee.org/Xplore/home.jsp?ref=lennartnacke.com) and [ACM Digital Library](https://dl.acm.org/?ref=lennartnacke.com) for technology domains, and [Scopus plus Web of Science](https://www.scopus.com/?ref=lennartnacke.com) for multidisciplinary coverage with citation analysis capabilities. The strategic advantage comes from using Boolean operators (AND, OR, NOT) to create precise search strings that capture related terminology while filtering irrelevant results. And, yes, most of the AI tools you are using are based on [Semantic Scholar](https://www.semanticscholar.org/?ref=lennartnacke.com), which is a massive database that uses semantic (and not keyword-based searching, so a better approach), but it still misses a large chunk of existing literature. Don’t take shortcuts. Combine different databases for search. A great technique in this phase is systematic citation chaining through backward and forward snowballing (as long as you do it with more than one reviewer, at least two). Backward snowballing examines reference lists of relevant papers to identify foundational research, while forward snowballing uses citation tracking to discover recent studies building on existing work. Tools like [Litmaps](https://go.lennartnacke.com/litmaps?ref=lennartnacke.com), [Research Rabbit](https://www.researchrabbit.ai/?ref=lennartnacke.com), and [Inciteful](https://inciteful.xyz/?ref=lennartnacke.com) provide visual citation networks that reveal knowledge connections impossible to find through database searching alone. This approach typically uncovers additional relevant literature compared to database-only strategies. So, just relying on a single database is a critical error that will almost certainly lead to a biased and incomplete collection of literature. It’s like trying to understand the entire world by only ever looking out your own bathroom window. You’ll know an awful lot about your neighbour’s garden gnome and the exact shade of their roof shingles, but very little about anything beyond. Just don’t do it. A rigorous search is built on a multi-pronged strategy that maximizes coverage and uses the unique strengths of different search methods. This strategy rests on three core pillars: 1. **Systematic Database Searching:** This is the core of the search process, involving the systematic querying of major academic databases using the predefined search strings from the protocol. Just like what I mentioned above. 2. **Citation Chaining (or “Snowballing”):** As I mentioned, this is an iterative, exploratory method that follows the intellectual trail of citations both backward (reviewing reference lists of key papers) and forward (finding newer papers that have cited key papers). This is crucial for finding foundational work and tracking an idea’s evolution. 3. **Manual and Grey Literature Searching:** Many don’t do this. This involves searching beyond traditional academic databases to find relevant material in other sources, such as conference proceedings, dissertations, clinical trial registries, and the websites of relevant organizations. This is important for mitigating publication bias, as not all research findings make it into peer-reviewed journals. ## 3\. Systematic screening and quality assessment with documentation protocols If you really want to master literature reviews, you have to understand that screening and quality assessment require predetermined criteria and systematic documentation to make your research transparent and reproducible. This phase begins with applying inclusion and exclusion criteria established during planning correctly: publication date ranges, study types, population characteristics, geographical scope, language restrictions, and methodological quality thresholds. All of these must be reported. The [PRISMA framework](https://www.prisma-statement.org/?ref=lennartnacke.com) provides the gold standard for documenting this process, creating flow diagrams that demonstrate methodological rigour to supervisors and examination committees (or [ENTREQ if you are doing qualitative research](https://link.springer.com/article/10.1186/1471-2288-12-181?ref=lennartnacke.com)). However, you are not done if you are just creating the flow chart. You have to also do quality assessment of the literature. Quality assessment in this phase uses validated tools matched to your study types: [CASP checklists for qualitative studies](https://casp-uk.net/casp-tools-checklists/qualitative-studies-checklist/?ref=lennartnacke.com), [JBI critical appraisal tools](https://jbi.global/critical-appraisal-tools?ref=lennartnacke.com) for specific research designs, or custom assessment criteria for interdisciplinary reviews. The strategic advantage comes from applying these assessments consistently and documenting decisions systematically, creating an audit trail that supports your final synthesis and enables future review updates. If you really want to update your reporting quality, check out [the tips in Table 2 from our recent umbrella review](https://dl.acm.org/doi/full/10.1145/3685266?ref=lennartnacke.com#tab2). ([Rogers et al., 2024: An Umbrella Review of Reporting Quality in CHI Systematic Reviews: Guiding Questions and Best Practices for HCI. ACM Trans. Comput.-Hum. Interact. 31, 5](https://doi.org/10.1145/3685266?ref=lennartnacke.com)) A great protocol is the cornerstone of a transparent and reproducible review. It should be a written document that formally details the entire methodology *before* the review commences. Key components include: 1. **Background:** A brief rationale for the review. 2. **Research Question(s) and Objectives:** The specific, structured question and the review’s stated goals. 3. **Inclusion and Exclusion Criteria:** A detailed list of the criteria for study selection, covering aspects like population, study design, date range, and language. 4. **Search Strategy:** The list of databases to be searched, the full search strings for at least one major database, and plans for other search methods like citation chaining or grey literature searching. 5. **Study Selection Process:** A description of how studies will be screened (e.g., title/abstract then full text) and by whom. 6. **Data Extraction Strategy:** A list of the variables to be extracted from each included study. 7. **Quality and Risk of Bias Assessment:** The specific tools that will be used to appraise the quality of included studies (e.g., [Cochrane RoB 2](https://methods.cochrane.org/bias/resources/rob-2-revised-cochrane-risk-bias-tool-randomized-trials?ref=lennartnacke.com), [Newcastle-Ottawa Scale](https://www.ohri.ca/programs/clinical%5Fepidemiology/oxford.asp?ref=lennartnacke.com)). 8. **Data Synthesis Plan:** A description of how the findings will be synthesized (e.g., narrative synthesis, thematic analysis, or statistical meta-analysis). Clarification before the search begins is your most effective defense against scope creep, post-hoc decision-making, and other sources of bias that can derail a proper literature review. ## 4\. Analysis, synthesis, and strategic documentation for research advancement. Finally, methodical researchers approach synthesis as strategic knowledge construction that identifies gaps, patterns, and opportunities for original research contributions. This phase involves systematic data extraction using standardized forms that capture information needed to answer your research questions: study characteristics, methodological details, key findings, and theoretical frameworks. The analysis method depends on your review type (e.g., narrative synthesis for heterogeneous studies, meta-analysis for quantitative data, or thematic analysis for qualitative evidence). But here’s what separates exceptional literature reviews from mediocre ones: Identifying strategic gaps and mapping research opportunities as you begin the review. This phase culminates in clearly articulating what knowledge exists, what remains unknown, and where your original research can make meaningful contributions. Remember that the job of a literature review is not just to summarize existing knowledge but to construct the intellectual foundation that justifies your research direction and positions it within scholarly conversations. Throughout the search process, meticulous documentation is non-negotiable. A detailed *search log*, typically maintained in a spreadsheet, is essential for making the review is transparent, reproducible, and reporting it accurately in the final manuscript, particularly for methodologies that follow PRISMA guidelines. The **search log** should record, for each database searched: 1. The date of the search. 2. The exact search string used. 3. The date range and any other filters or limits applied (e.g., language, publication type). 4. The number of results retrieved for that specific search query. This log becomes a critical appendix to a research report, paper, or dissertation, because it provides clear evidence of the systematic nature of your search process. The documentation in this phase creates multiple strategic assets: comprehensive reference databases for future use, methodology templates for subsequent reviews, and evidence synthesis that becomes the foundation for your thesis literature review, publication manuscripts, and research proposals in the future. ## Your compound advantage These four phases work synergistically because each phase builds systematic advantages that accelerate subsequent phases while improving overall quality. And it leads you to contribute original ideas to advance a field of study. Researchers who master this phase-based planning approach complete literature reviews faster, produce better coverage, and create stronger foundations for breakthrough research that advances their fields rather than just meeting degree requirements. It sets you up for a great literature review. Next week, we’ll get into screening and thematic extractions from the literature. Talk soon. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Bonus Tables In this bonus material, I have compiled an overview of the major academic databases, a practical guide to advanced search strings, and an overview of the AI literature mapping tool functionalities explained for paying subscribers: _This post is for paying subscribers only._ ### How to Choose the Right Literature Review Approach URL: https://lennartnacke.com/how-to-choose-the-right-literature-review-approach/ Last updated: 2025-07-01T19:15:42.000Z I’ve noticed that new PhD students often mix up literature reviews and related work sections. They’re actually quite different, though. Let me explain. Think of a literature review as a deep dive into everything we know about a topic. It stands on its own. It’s a standalone document that systematically examine the current state of knowledge on a particular topic. In my experience, literature reviews usually show up as chapters in theses or as standalone papers. They’re detailed and evaluate existing research thoroughly. Most of my students spend weeks or sometimes even years synthesizing all the studies on their relevant topic in their dissertation chapters. Consider this a research methodology with its own systematic approach, quality criteria, and contribution to knowledge. It follows rigorous protocols for searching, selecting, analyzing, and synthesizing research. Some literature reviews can be entire dissertations by themselves, and the best ones actually generate new theoretical insights or practical recommendations. Related work sections, on the other hand, are more like targeted summaries that go in research papers. When I write these, I only focus on studies that directly connect to my specific research question. This section positions your research within the existing body of knowledge. I usually summarize what others have done and explain how my work fills a gap. Think of it as the context-setting for your specific research question. You’re not trying to be comprehensive — you’re being strategic about which studies to include based on their direct relevance to your work. The key differences come down to size, detail, and goal. I’ve found that literature reviews paint the big picture of a research area. They help us spot what’s missing and build a strong foundation for our work. On the flip side, related work sections just show we’ve done our homework and explain why our new research matters. Think of it like this: A literature review is like a detailed map of an entire city, while a related work section is more like directions to a specific restaurant. I’ve seen many students get confused about which one to use. Knowing the difference helps you pick the right tool for your academic writing project. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. The key difference: related work serves your research, while a literature review is research. ## Most PhD students default to the wrong literature review type When PhD students do decide to conduct a formal literature review, they almost always gravitate toward what they think is a systematic review because it sounds the most rigorous. But here’s the problem: Systematic reviews require exhaustive searching, formal quality assessment protocols, and often take 12–18 months to complete properly. They’re designed to answer specific clinical or policy questions with clear inclusion/exclusion criteria. If your research question is broader, more exploratory, or theoretical in nature, a systematic review might actually limit your ability to make meaningful contributions. For example, if you’re exploring an emerging technology area where there aren’t many high-quality empirical studies yet, insisting on systematic review criteria might leave you with too few papers to analyze meaningfully. You’d be better served by a scoping review or mapping review that can capture the breadth of available literature and identify research gaps. The key is matching your review type to your research goals, not just choosing what sounds most impressive. ### The 14 literature review types you need to know **1\. Critical Review:** Demonstrates you’ve extensively researched literature and critically evaluated its quality, going beyond description to include analysis and conceptual innovation. Perfect for theoretical contributions. **2\. Literature Review:** The generic umbrella term for examining published literature, which can vary widely in scope and comprehensiveness. Often what students write without realizing there are more specific alternatives. **3\. Mapping Review/Systematic Map:** Maps out and categorizes existing literature to identify gaps and inform future research. Ideal when you need to understand the landscape of a research area. **4\. Meta-Analysis:** Statistically combines results from quantitative studies to provide more precise effect estimates. Essential when you have multiple studies measuring the same outcomes. **5\. Mixed Studies Review:** Combines quantitative and qualitative research evidence, useful when your research question requires both types of evidence to answer comprehensively. **6\. Overview:** Provides a broad summary of literature, often used synonymously with other review types but typically less rigorous in methodology. **7\. Qualitative Systematic Review:** Integrates findings from qualitative studies by looking for themes and constructs across studies, perfect for understanding experiences or phenomena. **8\. Rapid Review:** Uses systematic review methods but within time constraints, trading some comprehensiveness for faster turnaround. Great for preliminary assessments. **9\. Scoping Review:** Provides preliminary assessment of literature scope and size, identifying the nature and extent of research evidence. Excellent for exploring emerging areas. **10\. State-of-the-Art Review:** Addresses current matters and cutting-edge developments, often offering new perspectives or identifying future research directions. **11\. Systematic Review:** The gold standard for evidence synthesis, using rigorous methods to search, appraise, and synthesize research evidence according to established guidelines. **12\. Systematic Search and Review:** Combines critical review elements with comprehensive search processes, producing “best evidence synthesis” for broad questions. **13\. Systematized Review:** Attempts to include systematic review elements but falls short of full systematic review rigour, often used for student assignments. **14\. Umbrella Review:** Compiles evidence from multiple reviews into one accessible document, focusing on broad conditions with competing interventions. #### Choose your review type based on your research goals The biggest mistake PhD students make is choosing their literature review type based on what they think their supervisor wants to see or what they think is the strictest approach, rather than what actually serves their research questions. Don’t make this mistake. If you’re trying to map out an emerging field, don’t force yourself into a systematic review framework that requires you to exclude perfectly relevant papers because they don’t meet arbitrary quality criteria. If you’re building theory, a critical review might serve you better than a scoping review. If you’re informing practice, a rapid review might be more appropriate than spending two years on a deep systematic review. Here’s my easy recommendation: Start by clearly articulating what you want your literature review to accomplish. Are you trying to identify gaps? Build theory? Inform methodology? Answer a specific question? Once you’re clear on your purpose, the right review type becomes obvious. Keep in mind that your literature review should demonstrate your expertise in the field and contribute something valuable to the conversation. Don’t treat it like a checkbox on your thesis requirements. #### Methodology matters more than you think Your choice of literature review type will determine everything from your search strategy to your analysis methods to the claims you can make about your findings. Don’t just default to writing related work when you could be conducting a rigorous literature review that becomes a significant contribution in its own right. And definitely don’t choose a review type just because it sounds impressive. Choose the one that actually serves your research goals and timeline. The best PhD students understand that their literature review is an opportunity to demonstrate methodological sophistication, not just summarize what other people have done. *Now get out there and choose your literature review type strategically. You got this.* ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus _This post is for paying subscribers only._ ### How to Structure Your Related Work Like a Pro URL: https://lennartnacke.com/how-to-structure-your-related-work-like-a-pro/ Last updated: 2025-07-21T22:13:47.000Z I remember those early days of my PhD. The smell of freshly brewed tea and utter cluelessness in the murky hallways of a satellite campus. I was doing a PhD away from home. I was close to a new culture, but far away from any help of my supervisor. And consistently, I was staring at a pile of papers wondering how on earth I was supposed to turn those into a coherent related work section for my next paper. Most first-year PhD students struggle with this exact challenge. You’ve read dozens of papers, dug up some key findings, and taken juicy notes — but when it comes time to write, you end up with a disorganized “shopping list” that reads like: “Paper A did this. Paper B did that. Paper C found something else.” Reviewer 2 is about to let you know about their frustration of this. Your readers will be confused. And—worst of all—you’ll fail to position your research effectively. Without proper organization, your related work section becomes a missed opportunity to demonstrate your deep understanding of the field. You just won’t build a case for why your research matters. And that sucks. So, today, we’ll tackle four proven organizational strategies that will change your related work section from a random collection of summaries into a strategic, compelling narrative. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### **1\. Use chronological organization to show how your field evolved** Chronological organization works best when your research area has clear historical development or when you need to demonstrate paradigm shifts. Start with early foundational work from the 1990s-2000s, then move through the development of key theories in the 2000s-2010s, and finish with recent advances from 2010s-present. Chunking the research into a timeline of paradigms creates a natural narrative arc that shows how understanding has progressed over time. It’s a compelling journey for your readers. For example, if you’re studying machine learning for medical diagnosis, you might begin with early rule-based expert systems, progress through statistical approaches, and conclude with deep learning breakthroughs. The chronological flow helps readers understand why current approaches emerged and what problems they solved. However, avoid using chronology just because it’s easy. Make sure the temporal progression actually adds value to your argument. ### **2\. Use thematic organization when multiple approaches coexist** Thematic organization is a strategy that groups literature around topics or concepts rather than time periods. It’s perfect for complex research areas. Structure your themes logically, moving from broad methodological approaches to specific theoretical frameworks to application domains and evaluation metrics. Each theme should contain 3–5 papers that you synthesize rather than simply list. For instance, if you’re researching online learning platforms, you might organize themes around: pedagogical approaches (constructivist vs. behaviourist), technology platforms (web-based vs. mobile), student engagement strategies, and assessment methods. Such a structure lets you deeply explore each aspect while you keep your focus on your research questions. The key is that each theme must directly relate to your research and that themes flow logically from one to the next. ### **3\. Use methodological organization to highlight technical contributions** Methodological organization works exceptionally well for engineering and computer science papers where comparing different research methods is crucial. I do this all the time, when I want to make a case for which methods I’m using in my user studies related to the phenomenon I’m exploring. Organize sections around quantitative approaches, qualitative methods, mixed-methods studies, and computational approaches. Within each methodological category, group papers that use similar techniques and analyze their collective strengths and limitations. For example, if you’re studying sentiment analysis, you might compare rule-based approaches, machine learning methods, and deep learning techniques. This organization naturally highlights where your methodological contribution fits and what gaps you’re addressing. Remember to explain why certain methodological choices matter for your specific research problem — don’t just catalog different approaches. --- Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). _This post is for paying subscribers only._ ### How I Became The Professor Everyone Asks About AI Tools URL: https://lennartnacke.com/how-i-became-the-professor-everyone-asks-about-ai-tools/ Last updated: 2025-09-04T19:18:11.000Z Are you tired of watching colleagues either blindly embrace AI tools or completely dismiss them — while you know there’s a smarter middle path? The truth is, most academics are approaching AI literacy all wrong. They’re either using AI without any critical evaluation (leading to embarrassing mistakes and ethical violations) or they’re avoiding it entirely (missing massive opportunities to enhance their research and teaching). Meanwhile, administrators are scrambling to create AI policies without understanding the technology, students are using AI in ways that undermine learning, and departments are making expensive tool purchases based on marketing hype rather than actual value. Today, I’m going to show you exactly how to position yourself as the trusted AI expert on your campus — the person everyone turns to for guidance, evaluation, and strategic thinking about AI in academia. Let’s walk through each strategy. ### **Way 1: Master the fundamentals of prompt engineering beyond basic commands** Most academics think prompt engineering means asking ChatGPT to “write me a literature review” — but that’s amateur hour. Real prompt engineering for academics involves understanding structured frameworks that consistently produce reliable, high-quality outputs. We've discussed many of them in our [​AI for UX Designers Masterclass yesterday​](https://lu.ma/u3lmwczm?ref=lennartnacke.com). While there are dozens of prompt frameworks available, four stand out as particularly powerful for academic work. Start by learning the **CLEAR** framework: Context (provide background), Length (specify output length), Examples (show desired format), Audience (define who this is for), and Role (tell the AI what perspective to take). Instead of “Write me an introduction to my research paper about social media,” here’s an example using the CLEAR framework for structuring an academic introduction: “You are an experienced professor of academic writing. Write a 600-word introduction for a research paper on the impact of social media on academic discourse. Use the CARS (Create A Research Space) model with clear moves showing the research territory, niche, and occupation. Base the structure and tone on this example introduction: \[insert example introduction\]. Focus on establishing the research gap or problem regarding how platforms like Twitter have changed scholarly communication patterns. Write for an audience of peer reviewers at an HCI journal.” The **RACE** framework (Role, Action, Context, Expectation) excels when you need the AI to assume specific academic expertise. For example: “Role: You are a senior literature review specialist in cognitive psychology. Action: Analyze these 15 research papers for methodological gaps. Context: I’m preparing a grant proposal on working memory interventions for ADHD students, and I need to identify unexplored research directions. Expectation: Provide a 400-word analysis highlighting 3 specific methodological gaps with suggested research questions for each.” The **APE** framework (Action, Purpose, Expectation) works brilliantly for straightforward academic tasks where clarity is paramount. Try this: “Action: Summarize the key findings from this 50-page report. Purpose: To brief department colleagues who need to understand the implications for our curriculum review process. Expectation: Create a 2-page executive summary with bullet-pointed action items and a timeline for implementation.” For complex, multi-step academic processes, the RISE framework (Role, Input, Steps, Expectation) delivers exceptional results. Here’s an example: “Role: You are an experienced journal editor in environmental science. Input: This draft manuscript on microplastic contamination in freshwater systems attached as PDF. Steps: First, evaluate the literature review draft for completeness. Second, assess methodology for rigour. Third, analyze results presentation for clarity. Fourth, review discussion for logical flow. Expectation: Provide specific feedback for each step with concrete suggestions for improvement.” Practice this with different academic tasks until you can consistently get publication-quality outputs that still require your expertise to refine and validate rather than completely rewrite the output. ### **Way 2: Develop a systematic framework for evaluating AI tool accuracy and bias** The fastest way to lose credibility is recommending an AI tool that produces biased or inaccurate results — yet most academics have no systematic way to test these tools. Create a standardized evaluation protocol that you can apply to any AI tool. Test the tool with questions where you already know the correct answers, probe for biases by asking about controversial topics in your field, and document systematic weaknesses you discover. For research tools, verify citations and fact-check claims. For writing tools, test for consistent voice and argument structure. Keep a running document of your evaluations, noting which tools excel at specific tasks and which consistently fail. This becomes your secret weapon when colleagues ask for AI recommendations — you’ll have data-driven answers instead of hunches. ### **Way 3: Establish yourself as the department’s AI ethics and policy consultant** Every academic department needs AI guidelines, but most faculty have no idea how to create them — this is your opportunity to lead. Draft practical AI policies that address academic integrity, data privacy, intellectual property, and pedagogical best practices. Core values that I think should be in such AI policies: - **Academic Integrity**: AI use must maintain honesty, trust, and fairness in all academic endeavours while preserving the fundamental value of original human scholarship. - **Transparency**: All AI assistance must be appropriately disclosed and documented to maintain scholarly transparency and reproducibility. - **Educational Value**: AI tools should improve not replace critical thinking, creativity, and core disciplinary skills. - **Privacy and Security**: Student and institutional data must be protected according to applicable privacy regulations (FERPA, GDPR). - **Equity and Accessibility**: AI implementation should promote not hinder equal access to educational opportunities. Focus on creating policies that are specific enough to be useful but flexible enough to evolve with the technology. Address questions like: When can students use AI for assignments? How should faculty disclose AI assistance in research? What data privacy considerations apply to institutional AI tool adoption? Present these drafts at faculty meetings and volunteer to chair the AI policy committee. Position yourself as someone who understands both the technical capabilities and the academic implications. ### **Way 4: Build and maintain a curated repository of tested AI tools and use cases** Your colleagues don’t have time to test dozens of AI tools — but you can become the person who has already done that work. [​I’m sending paid subscribers a Notion link to my list today.​](https://lennartnacke.com/#/portal/signup) Create a living document that categorizes AI tools by academic function: research assistance, writing support, data analysis, course design, grading efficiency, and student engagement. For each tool, include specific use cases, pricing information, learning curve assessment, and integration capabilities with existing academic workflows. Update this repository monthly and share highlights in department newsletters or faculty development sessions. When someone needs an AI solution for a specific problem, you’ll be the go-to expert with tested recommendations. ### **Way 5: Design and deliver AI literacy workshops tailored to academic contexts** Generic AI training doesn’t address the specific needs of academics — but workshops designed by academics for academics will be in high demand. Something like our [​AI Research Tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) is a great way to start. Develop modular workshops that address different academic constituencies: “AI for Research Efficiency,” “Ethical AI Use in the Classroom,” “AI Tools for Administrative Tasks,” and “Critical AI Evaluation for Faculty.” Make these workshops hands-on, with participants working through real academic scenarios using the tools and frameworks you’ve mastered. Start by offering these workshops to your own department, then expand to other departments, and eventually position yourself to lead campus-wide AI literacy initiatives. ### **Way 6: Establish office hours specifically for AI consultation and troubleshooting** Position yourself as the person colleagues can turn to when they’re stuck with AI tools or need guidance on specific applications. Dedicate one hour per week to “AI office hours” where colleagues can bring specific challenges: a research project that needs AI assistance, a student assignment policy that needs AI considerations, or a departmental workflow that could benefit from automation. Document common questions and successful solutions — this becomes the basis for future workshops and policy recommendations. This regular availability establishes you as approachable and knowledgeable. It builds the trust necessary to become the campus AI authority. ### **Way 7: Stay strategically ahead of AI developments through targeted learning and networking** AI changes monthly, and maintaining your expert status requires strategic learning that goes beyond surface-level news consumption. Follow key academic AI researchers, subscribe to technical newsletters that focus on research applications, and join professional communities where academics discuss AI implementation. Set aside time each week to test new tools and features, but focus on developments that have clear academic applications rather than chasing every new AI trend or every new tools that a former grad student is trying to get funded. Create a system for sharing your discoveries — whether through social media, department emails, newsletters, or informal conversations — so colleagues begin to see you as their source for relevant AI updates. You don’t want to become a technical AI developer, but the trusted interpreter who helps your academic community navigate AI developments thoughtfully and strategically. What are you waiting for, my friend? P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## AI Tool Evaluation Worksheet Below is a PDF worksheet that provides a structured approach to AI tool evaluation. And my comprehensive **AI Research Tools Database in Notion**. _This post is for paying subscribers only._ ### Why Early Career Researchers Can't Say No (And How To Fix It) URL: https://lennartnacke.com/why-early-career-researchers-cant-say-no-and-how-to-fix-it/ Last updated: 2025-09-04T19:05:55.000Z One year into my assistant professor job, I was drowning. Twelve different projects, seven committees, four teaching assignments, and exactly zero time to write more meaningful publications. Why is it that smart early-career researchers consistently sabotage their own success by saying yes to everything? You know the pattern damn well. You accept every committee invitation, volunteer for every conference panel, agree to peer review papers in fields you barely know, and somehow find yourself teaching three different courses while trying to secure tenure. Meanwhile, your actual research — the work that will define your career — sits neglected on your desk, buried under a mountain of cool opportunities that seemed important at the time. The result? You’re exhausted, your research progress has stalled, and you’re watching peers with fewer commitments publish breakthrough papers while you’re stuck answering emails about meetings you don’t remember agreeing to attend. Today, I’m going to break down exactly why early-career researchers struggle to say no, and give you a framework to protect your research time without burning bridges. Let’s get started. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Reason 1: You believe every opportunity is your last opportunity Most early-career researchers operate from a scarcity mindset that makes every invitation feel like a career-defining moment. I got news, McFly. It’s not. When Dr. Frankenstoner asks you to join the curriculum committee, your brain immediately jumps to: “What if this is my only chance to work with a respected faculty member? What if saying no means I’ll never get another opportunity? What if they think I’m not committed to the department?” This fear-based thinking ignores a fundamental truth about academic careers: opportunities compound over time. The more established you become in your research, the more opportunities naturally flow your way. But when you’re overcommitted and your research suffers, you actually become less attractive for future opportunities. Your opportunity surface area shrinks. Here’s what successful researchers get right at this stage: saying no to mediocre opportunities creates space for exceptional ones. ### Reason 2: You confuse being busy with being productive Academic culture glorifies the hustle (even more than solopreneurship does and at least those guys are getting paid), and early-career researchers often mistake a packed schedule for career progress. This is not a sandwich. You don’t need extra toppings for better taste. You tell yourself that reviewing papers, attending conferences, and serving on committees is building your reputation and being a good academic citizen. And while these activities have value, they become career killers when they consume all the time and mental energy you need for research. And trust me, you need that time at the beginning. Do this little mental exercise with me: Would you rather be known as the person who serves on every committee but publishes mediocre work, or the researcher who produces smashing solid studies but carefully selects their service commitments? The answer should be obvious (at least for me, it was), but the academic pressure to do it all makes this choice feel impossible. The reality is that your research output will define your career trajectory far more than your service record ever will. Let this truth sink in the day you become a professor. ### Reason 3: You lack a clear framework for evaluating opportunities Without criteria for decision-making, every request feels equally important and urgent. You’ll get lost and buried under a mountain of work. Most early-career researchers evaluate opportunities based on emotions rather than strategic thinking. Someone asks, you feel flattered or guilty or afraid, and you say yes before considering how the commitment aligns with your research goals. This reactive approach to your career puts you at the mercy of other people’s priorities instead of your own. You end up building other people’s careers while neglecting your own research agenda. Take back control. ### The solution: Install a three-filter system for every opportunity Before saying yes to any request, run it through these three filters: - **Filter 1: Does this directly advance my research agenda?** If the opportunity doesn’t help you publish, secure funding, or develop critical research skills, it should be a **hard no** during your early career years. Your research is your primary job, and everything else is secondary. - **Filter 2: Will this create meaningful connections with people who can impact my career?** Some opportunities are worth considering even if they don’t directly advance your research, but only if they connect you with potential mentors, collaborators, or decision-makers in your field. Coffee with a graduate student about their unrelated project? Probably not worth your time. Co-organizing a workshop with senior researchers in your area? Potentially valuable. Do it. - **Filter 3: Can I do this well without sacrificing research quality?** Even if an opportunity passes the first two filters, you need to honestly assess whether you can deliver high-quality work without compromising your research. If you’re already stretched thin, adding more commitments will hurt everything you’re trying to accomplish. ### How to say no without burning bridges The key to saying no gracefully is to be honest, brief, and helpful when possible. It’s as simple as that. Try this template if you’re feeling a bit uncomfortable at first: “Thank you for thinking of me for \[opportunity\]. I’m currently focused on \[specific research project/goal\] and need to decline so I can give it my full attention. I’d be happy to suggest \[alternative person\] who might be interested and would do excellent work.” You accomplish three things with this approach: you show appreciation for being considered, provide a clear (research-focused) reason for declining, and offer to help in a different way. Most people will respect your focus on research, and many will appreciate the alternative suggestion. Well done, my friend. Keep this mental note: every yes to something unimportant is a no to something that could transform your career. Make an active choice. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Your research deserves better than your leftover time and energy The academic world will always have more opportunities than you can possibly accept, but you only have one research career. Don’t let the ever-growing Kraken of requests swallow you whole. Put yourself on the helm and steer ship, Captain Sparrow. If you observe successful researchers like I do, you’ll see that those who build exceptional careers aren’t the ones who say yes to everything but they’re the ones who protect their research time fiercely and choose their additional commitments strategically. They understand that depth beats breadth, that focus beats hustle, and that sometimes the most important thing you can do for your career is absolutely nothing except research. No better time than to start practicing this today. After you finish this email and before you say yes to your next request, ask yourself if it passes all three filters. I wish you the most success with this. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Custom GPT and Cheat Sheet I wrote a Custom GPT that interviews you about an opportunity and will help you make a decision about whether or not you should accept the request. _This post is for paying subscribers only._ ### Why Your AI Prompts Suck (And 10 Rules to Fix Them) URL: https://lennartnacke.com/why-your-ai-prompts-suck-and-10-rules-to-fix-them/ Last updated: 2025-08-14T00:29:50.000Z #### Key Points - Vague prompts = vague answers; specificity is non-negotiable. - Break big tasks into smaller, sequenced prompts. - Give context, examples, and the exact audience you’re targeting. - Treat AI as a collaborator: iterate and polish outputs. - Format prompts in plain language that’s easy to interpret. AI isn’t the magic academic advisor you imagine. If you type *make this better* without direction, you’ll get generic, shallow output. The LLM works with what you feed it, and if that’s fuzzy, your results will be too. --- It was another afternoon in my university office. I was hunched over my laptop, surrounded by textbooks and empty coffee cups. I could just feel the concerned glances of Twitter influencers that made using AI look easy as I stared at my screen with the vacant expression of someone who has been debugging code for six hours straight. Another AI response, another letdown. I was frustrated. I was typing crap like “make this paragraph sound better” into the prompt box for what felt like the hundredth time, hoping for this flint and tinder to light the productivity fire everyone was promising. Instead, I received output that read like a first-year student's rushed assignment. No thank you, ChattieG. You tried. My realization came between my third and fourth skinny Latté. The AI was not the issue - my approach was. I had been treating it like some omniscient academic advisor, throwing vague requests into the void and expecting detailed, perfectly tailored responses. The classic computer science principle of garbage in, garbage out suddenly felt very personal. My imprecise inputs were leading directly to unfocused outputs. It was time to become more structured. > Your AI prompts are underperforming because they’re vague, lack context, and fail to guide the model toward your real goal. Treat prompts like instructions for a junior research assistant: specific, clear, and structured for the exact outcome you want. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## The 10 Rules So I started treating my prompts like I was writing instructions for an undergrad research assistant. Specific. Clear. Detailed. No room for artistic interpretation. And here are the 10 rules, I recommend to follow for better results: Recording of our free webinar: The 10 Commandments of AI Prompting #### 10 best practices for writing AI prompts that deliver accurate, actionable results ****1\. Be Specific** ****Bad:** Review this research paper. ****Better:** Review this psychology paper’s methodology section. Focus on experimental design, participant sampling, and statistical test choice. ****2\. Break Down Complex Tasks** Split multi-step jobs: 1. Generate 10 open-ended interview questions on daily productivity habits. 2. Identify recurring frustration themes from transcripts. 3. Summarize top 5 pain points with task management apps. ****3\. Provide Context** State who you are, your project, and why it matters. **I’m a doctoral candidate in educational psychology studying digital literacy interventions. Help me code qualitative data from 30 teacher interviews about technology adoption barriers.* ****4\. Remove Ambiguity** Avoid vague verbs like “improve” or “optimize.” ****Bad:** Make this better. ****Good:** Restructure this literature review to follow APA 7th edition, with clear topic sentences and logical paragraph flow. ****5\. State Clear Goals** Tell AI what the finish line looks like. **Generate 12 semi-structured interview questions exploring faculty experiences with remote teaching, focusing on pedagogical challenges and institutional support.* ****6\. Target the Right Audience** ****A:** Explain data encryption. ****B:** Explain data encryption to a high school student who uses Snapchat but doesn’t trust big tech. ****7\. Show Examples** Feed in the style or structure you want and say “now do it like this.” ****8\. Iterate** Treat the first answer as a draft: “That’s close. Make it more playful.” “Good. Now, shorten to under 10 words.” ****9\. Use Multi-Shot Prompting** Feed several versions or steps so the AI can refine and cross-compare. ****10\. Keep It Simple** Plain, human language works better than jargon. **Analyze participant responses for recurring themes related to technology acceptance.* ### 1\. Embrace Specificity If your prompt could apply to a thousand different situations, it’s probably not going to give you a good result. Don’t say: "Review this research paper." Instead, say: "Review this psychology paper’s methodology section. Focus on the experimental design, participant sampling method, and whether the statistical tests chosen are appropriate for testing the stated hypotheses." The more precise your input, the better the output. ### 2\. Break Down Complex Tasks Into Steps Don’t dump a 10-step job on it at once. Break it up. Start with: “Generate 10 open-ended interview questions about daily productivity habits.” Then: “Based on these transcripts, identify recurring themes in user frustrations.” Finally: “Summarize the top 5 pain points users experience with task management apps.” Micro-prompts = macro clarity. ### 3\. Context Is King Give the AI a sense of the big picture. Who are you? What are you working on? Why does it matter? Example: "I’m a doctoral candidate in educational psychology studying digital literacy interventions. Help me code qualitative data from 30 teacher interviews about technology adoption barriers." Now your AI has a mental map. Expect better results. ### 4\. Eliminate Ambiguity Words like “improve,” “enhance,” or “optimize” are death traps. They mean everything and nothing. Instead of: “Make this better” Try: "Restructure this literature review to follow APA 7th edition standards with clear topic sentences and logical flow between paragraphs" Be crystal clear about what you want. Otherwise, you’ll get mush. ### 5\. Set Clear Goals Always say what you’re trying to accomplish. Instead of just asking for ideas, tell the AI what kind of output you need and why. "Generate 12 semi-structured interview questions to explore faculty experiences with remote teaching technologies, focusing on pedagogical challenges and institutional support needs." Let the AI know where the finish line is. ### 6\. Prompt for the Right Audience The AI isn’t reading your mind. Tell it *who* you’re prompting for. Prompt A: “Explain data encryption.” Prompt B: “Explain data encryption to a high school student who uses Snapchat but doesn’t trust big tech.” Big difference. ### 7\. Provide Examples If you want your AI output to sound a certain way or follow a particular structure, show it the way. “Here’s an example of the tone I like: ‘Think of this as your brain’s inbox. We’re just helping you sort through it.’ Now write a similar line for a focus timer feature.” Monkey see, monkey do. ### 8\. Embrace the Feedback Loop Your first prompt won’t be perfect. That’s okay. Think of AI as a collaborator. Ask. Read. Revise. Ask again. “That’s close. Now try making it more playful.” “That’s good. Now shorten it to under 10 words.” Iteration is how good becomes great. ### 9\. Use Multi-Shot Prompting Don’t stop at one. Prompt the AI with a few variations of what you want: First: Extract themes → Second: Define each theme → Third: Find supporting quotes → Fourth: Create thematic map with relationships. The AI can compare, contrast, and improve if you feed it multiple data points. ### 10\. Keep It Simple Big words don’t make you sound smarter. They make your prompt harder to interpret. "Analyze participant responses for recurring themes related to technology acceptance" That’s it. No MBA jargon. No academese. Just plain, human talk. ## Weak vs. Strong Prompts Comparison | **Weak Prompt** | **Strong Prompt** | | ------------------------ | ----------------------------------------------------------------------------------------------------------------------------------- | | "Make this sound better" | "Rewrite this conclusion in active voice, under 50 words, highlighting the novelty of our results for environmental policy makers." | | "Summarize this" | "Summarize this 800-word article into 5 bullet points for a policy briefing aimed at non-technical government staff." | ## Wrap Up *These rules work because they treat AI as a collaborative partner, not a magic solution. The best human-AI interactions happen when we provide structure, clarity, and context.* Start with one rule per week. Pick a routine task—maybe analyzing survey responses or writing summaries. Apply that week's principle and see your results improve. Your next breakthrough might be just one well-written prompt away. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. #### FAQ ****Q: How long should my prompt be?** As long as needed to remove ambiguity, often 2-4 sentences. ****Q: Should I always give examples?** Yes. Examples anchor style, tone, and structure for the AI. ****Q: Does AI understand my field without context?** It can guess, but your results will be far better with field-specific framing. ****Q: Can I reuse the same prompt?** Yes, but adjust variables (tone, audience, format) to fit the task. ****Q: How do I know if my prompt is good?** If a human assistant could follow it without asking for clarification, it’s good P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Cheat Sheet and Bonus Prompts _This post is for paying subscribers only._ ### I Tried Every Procrastination Hack. Nothing Worked Until I Understood My Type URL: https://lennartnacke.com/i-tried-every-procrastination-hack-nothing-worked-until-i-understood-my-type/ Last updated: 2025-05-28T14:04:51.000Z The cursor blinked. Again. Pinching a tiny black slice into the white canvas of my Word document. That relentless, mocking little bastard was at it for the last two hours. And my room was startin to smell faintly of desperation (never mind the distracting stains on my coffee mug). Another huge paper for my dissertation was due, like, yesterday, and it was breathing down my neck, all hot and bothered. And me? I was writing. Yeah, right. More like I was deep-diving into the internet’s best cat videos. Anything but *the* thing. My gut was doing that familiar washing machine churn of guilt, anxiety, and a particularly potent strain of self-loathing. You know the feeling. You get it. But this wasn’t a one-off back then. This was my life. My own personal Groundhog Day of delay. It always started with this cold dread, then a manic flurry of anything *but* the actual work, cleverly disguised as essential preparation. Finally, it would be the mad, panic-fuelled scramble, pouring caffeine down my throat like it was a cure for incompetence, just to slap *something* together with more anxiety than I wanted. I’d thrown every trick in the book at it. Pomodoro timers? Made me hate tomatoes and time itself. Bullet journals? Became these pristine tiny monuments to all the sh\*t I wasn’t getting done. Chirpy motivational quotes? Love you, Bob Ross, but these were tiny, brightly coloured slaps in the face. No happy accidents in my time pressure. Not Nothing. Ever. Stuck. I was convinced I was just… defective. A lazy, undisciplined human, destined to swim in a sea of half-finished tasks and crushing disappointment. My brain felt like it was running on Windows 95 in a world demanding a quantum computer. The lag was unbearable. Then, one night much later in my career, probably during an intense session of avoiding taxes by trying to master the art of origami, a tiny little thought wormed its way in. What if I wasn’t *just* a procrastinator? What if this wasn’t some giant, singular monster I was wrestling? What if my procrastination had its own quirks? A personality? What if I was throwing punches in the dark because I didn’t even know what my opponent looked like? That little flicker of an idea? It was like someone finally switched on a light in a dark, messy room. It sent me on a different kind of research binge. And what I found out? It pretty much blew my mind and changed how I stare down that empty page every day. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Procrastinator Isn’t a Moniker, It’s a Mood Ring Here’s the first truth that hit me like a ton of bricks. Being a procrastinator isn’t a permanent tattoo on your soul. We sling that word around like it’s a simple, straightforward diagnosis, but the reasons we put things off are as varied and complex as your Netflix recommendations. Think about it for a second. If you have a cough, you don’t just grab any old lozenge, right? You figure out if it’s a cold, allergies, or maybe you just inhaled too much cat hair. Treating every bout of delay with the same generic “just do it” advice is like trying to fix a plumbing leak with a band-aid. Pretty useless, mostly frustrating. And the basement will start to smell soon. That’s exactly what I’d been doing. Applying those one-size-fits-all productivity hacks to a problem that had way more layers than I realized. Turns out, some folks actually *thrive* on that last-minute pressure. They use it. I have students like that. But for a lot of us, it’s a genuine struggle, a real glitch in our self-regulation, as the classic view puts it. The trick is figuring out *your* own, unique flavour of foot-dragging. I stumbled into the work of psychologist [Linda Sapadin](https://www.goodreads.com/book/show/635910.It%5Fs%5FAbout%5FTime%5F?ref=lennartnacke.com), and reading her breakdown of six procrastinator types felt useful. ### Meeting the Usual Suspects Let’s see if any of these characters feel a bit too familiar. Because honestly, recognizing your own brand of delay-demon is the first, massive step toward actually finding strategies that don’t make you want to throw your laptop out the window. And as you go through life you might go through them all. #### **1\. The Perfectionist** > **It’s not done ’til it’s divine (which is never)** Oh, the Perfectionist. This one hits too close to home for so many of us. Their motto is basically, “If I can’t make it award-winninly perfect, then why even bother starting?” They get tangled up in the tiniest details, overthink every single comma, and live in mortal fear that their work won’t ascend to the god-tier level of their imagination. Fatality! Starting feels like trying to sculpt David with a butter knife because perfect is a target that’s always just out of reach. I once spent an entire weekend — no joke — perfecting the font kerning and colour palette for a single lecture presentation slide. The content? Still wasn’t done. Whoopsies. The result of all that perfecting was another last-minute, caffeine-soaked scramble that was, ironically, far from perfect. The real trick I learned was to brutally lower my standards for the first pass. And build a system. Aim for good enough, or even passably mediocre to just get *something* on the page and move on with your content. Give yourself explicit permission to create a truly awful first draft. I realized my perfectionism wasn’t really about achieving high standards. Some of it was a sneaky way to avoid the fear of judgment. Action, however messy and imperfect, became my antidote to that fear. And it’s working until today. #### **2\. The Avoider** > **If it feels bad, it must be tomorrow’s problem** The Avoider sees their to-do list not as a set of tasks, but as a minefield of negative emotions. They’re always worried. Too hard. Too boring. Or simply: What if I fail spectacularly and everyone laughs? That’s what they worry about. They put things off because the task itself, or the thought of doing it, triggers anxiety, fear of failure, or just a profound sense of yuckamole (not the stuff with delicious avocados). My email inbox used to be a digital haunted house because replying to certain messages felt like gearing up for a cage fight. I’d rather have scrubbed the bathroom grout with a toothbrush. It’s a vicious cycle, because the more you avoid, the bigger and scarier the task becomes in your head, and the more shame and guilt pile on. However, I learned later that an email inbox is just someone else’s todo list. So, I fully embraced that and only reply to the things that matter instead of worrying about inbox zero. The breakthrough for me was learning to identify the core feeling. Was it really fear of failure? Or was it just profound boredom? Then, I’d play a little game: shrink the task. Just open the doc and work on this thing for five minutes. Five. You can survive five minutes. More often than not, just dipping a toe in the water made the whole ocean seem less terrifying. Sharks and all. The momentum, however tiny, started to dissolve the dread. It’s all about managing your *emotions*. #### **3\. The Crisis-Maker** > **I only sparkle under pressure (like a cheap firework)** Meet the Crisis-Maker. The one who proudly proclaims, “I work best under pressure.” Looking at you, Joe. They’re the ones who seem to intentionally wait until the clock is screaming red, because that adrenaline surge, that thrill of the looming deadline, is what gets their engine roaring. It makes them feel sharp, focused, alive. I used to wear this badge with a weird kind of pride. Sometimes, I’d even pull off these minor miracles, producing something decent in an impossible timeframe. I felt like a productivity ninja. Sweet adrenaline. But let’s be real, the collateral damage was immense. My nerves were perpetually frayed, my relationships strained by my last-minute meltdowns, and the quality of the work? Honestly, it was often rushed and rarely my actual best. Plus, living in a constant state of low-grade panic isn’t exactly a recipe for a long and happy life. What helped was learning to create *artificial* mini-deadlines with actual, albeit small, rewards. And finding healthier ways to get that adrenaline rush: a tough workout, a competitive board game, anything that didn’t involve jeopardizing my career or sanity. It’s about recognizing that while the pressure *feels* productive, it’s often just a very stressful way to operate, and there are less chaotic ways to find your focus. #### **4\. The Visionary** > **My ideas are amazing! The execution, uh… later.** The Visionary is a fountain of world-changing ideas. They get incredibly excited about the big picture, the dazzling possibilities of new projects. Their enthusiasm is infectious. The problem? When it comes to the actual, often tedious, nitty-gritty details of making those visions a reality, their energy deflates like a sad balloon. Their hard drives and notebooks are often graveyards of half-started next big things. I know mine certainly was at some point. The initial spark of an idea felt like fireworks. But the actual work of building the rocket felt like hauling bricks uphill in the mud. The most effective strategy I found was to brutally force myself to pick ONE vision to chase at a time. Then, break that nebulous vision down into ridiculously small, concrete, almost boring action steps. A task like writing a novel becomes “draft one single paragraph of chapter one.” Finding an accountability partner, preferably someone a bit more grounded and pragmatic, also works wonders. That’s what got me into fitness and I still work out almost every day. They were the ones who could gently pull my head out of the clouds and ask, “Okay, but what’s the *very next thing* you need to do?” #### **5\. The Defiant** > **You can’t make me! (Even if ‘you’ is actually me)** The Defiant is a fascinating character. They resist. They resist schedules, they resist expectations, they resist being told what to do. Even if the person telling them is their own rational brain. Procrastination for the Defiant often becomes a subconscious (or sometimes conscious) act of rebellion, a way of asserting their autonomy and control in a world that feels too demanding. If my boss, or even a well-meaning friend, *told* me I *had* to do something, even if it was something I genuinely wanted to do or knew was important, a stubborn little part of me would instantly want to dig in its heels and do the exact opposite. What can I say. I don’t like being told what to do. It wasn’t always a loud, “Hell no!” It was often a quiet, passive-aggressive foot-dragging. The key for me was learning to reframe tasks so they felt like *my* choice, aligned with *my* values and goals. I had to find the “why” that resonated deeply with *me*, not just with external pressures. And, where possible, giving myself choices in *how* and *when* I did the work helped dial down that internal resistance by upping my sense of control. #### **6\. The Overdoer** > **Yes! Yes! Yes! Oops, no time for my own life anymore.** And finally, the Overdoer. These are the folks who just can’t seem to say no. But that’s a magical skill in its own right. Their default answer to any request is “Yes, of course.” And they go help with the thing. They take on too much, overcommit their time and energy, and then, predictably, find themselves completely swamped. Legs sticking in quicksand. The tragic irony is that while they’re busy being everyone else’s hero, their *own* important priorities, their personal goals, end up getting neglected and delayed. My calendar used to look like a game of Tetris played during one of those scary Japanese earthquakes. Chaotic and dangerously overfilled. I was so caught up in fulfilling everyone else’s urgent requests that my own crucial, soul-feeding projects were constantly pushed to the back burner. And that was no good. Remember the thing I said about your email inbox beind someone else’s todo list. Don’t let them do that. Learning the incredible power of a polite but firm default no, or even a “not right now, but maybe later,” was an absolute revelation. I just started saying no by default. It felt terrifying at first, like I was letting people down. But you can’t pour from an empty cup. Hurting feelings because you prioritize isn’t hurting feelings. Actually scheduling my *own* important tasks as non-negotiable appointments in my calendar became my lifelines. Time-blocking became my winning system. And I was way more productive. ### It’s all about knowing your operating system Discovering these types wasn’t like finding a new flaw to obsess over. It was like someone finally handed me the instruction manual for my own slightly peculiar brain. Suddenly, my constant struggles weren’t because I was inherently lazy or undisciplined; they were because I was using the wrong tools for *my* specific make and model of procrastination. And man, that brought such a wave of relief, of actual self-compassion. I wasn’t broken. I was just… me, with a brain that had its own unique set of preferences and escape routes. And that meant I could finally stop fighting *against* my brain and start figuring out how to work *with* it. This isn’t about uncovering the one magic “hack” that will cure you forever. It’s about getting curious about your own personal triggers and go-to delay tactics. Are you putting off decisions because you’re terrified of making the wrong one (that’s Decisional Procrastination, a close cousin to some of these types)? Or are you sidestepping a task because it just feels like climbing Mount Everest in flip-flops (hello, Avoidant tendencies)? The *why* you’re delaying is so much more important than the *what* you’re delaying. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### My Messy, Ongoing Journey to Actually Getting Stuff Done For me, realizing I was a potent blend of Perfectionist and Overdoer was my big revelation. Instead of beating myself up for not instantly diving into a massive project, I started asking myself, “Okay, what’s the absolute smallest, least intimidating, most ridiculously easy step I can take right now?” Sometimes, that was literally just opening the damn document. Other times, it was challenging myself to write one truly terrible, cringe-worthy sentence, just to break the seal. Or just posting content online. I didn’t suddenly transform into a productivity cyborg. But I got that sweet self-awareness in tailoring my approach. It was in the dawning realization that motivation isn’t some mythical creature you have to wait for to appear. No, it’s simply a muscle you build, a tiny spark you create with action, however small and imperfect that action might be. Look, this isn’t a fairy tale with a perfect ending. I still have days where the jiving song of “Later, alligator” is almost too tempting to not start dancing to. But now, I have a better map. I can recognize the warning signs. I can usually identify which of my inner procrastinator types is trying to take the wheel. And then, I can pull out a strategy that actually has a fighting chance of working, instead of just flailing around in the dark. I've put it all together in a cheat sheet for you. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Cheat Sheet _This post is for paying subscribers only._ ### Most Abstracts Suck. Here’s How I Finally Wrote One That Didn’t URL: https://lennartnacke.com/how-to-write-better-academic-abstracts/ Last updated: 2025-05-28T14:05:35.000Z It’s probably 3 AM. I was looking at a stale pizza while facing the kind of desperation that makes you question all your life choices. The paper deadline was in two hours and I was tired AF. But I hadn’t written the abstract to my paper yet. Bollocks. That was me once. I treated my abstract like finding a semi-clean sock to wipe a coffee spill. A frantic — almost comedic — last-gasp gesture slapped onto the paper seconds before the submission portal slammed shut like a guillotine. Eeek! And then? Crickets. Or worse, those beautifully brutal rejection letters. Written with such a precision that they were almost poetic in their cruelty. Enough of those arrived during my PhD to create a rather fetching — if depressing — wallpaper motif for my office. It took an embarrassing number of these digital face-slaps before the realization hit me. Probably during a particularly soul-crushing Reviewer #2 comment. My abstract isn’t some easy little summary. It’s not a book report for grown-ups. Hell, no! It’s the damn velvet rope. It’s the athletic Jack-Reacher-lookalike bouncer with the clipboard deciding if your precious, blood-sweat-and-tears manuscript even gets a *glance* inside the club. Glitzy people call the abstract a movie trailer. Sure, Woody Allen. But I’m thinking it’s more like that one — perfectly curated — slightly deceptive photo on your dating app (which you probably beautified with too much AI). The one that screams, “Swipe right or regret it forever, fool!” That’s right, Mr. T. This tiny paragraph is your paper’s entire freaking pickup line, its peacock display, its desperate plea to a jaded reviewer, a time-starved colleague, or that mythical future citation, begging them to finally *care*. And yet, here we are, most of us treating it like the appendix of a boring legal document . Necessary. Dull. Written as if we’re already dead inside. We’re basically sending Paper Barbie into the world wearing yesterday’s Cheeto-stained T-Shirt and hoping for a dashing miracle to bedazzle her. Turns out, that’s how research goes to die quietly in the citation graveyard. Real crypt-keeper stuff. Solemn. Sad. Avoidable. Now, I’ve been on both sides of the academic publishing machine: submitting papers with crossed fingers and reviewing submissions with a critical eye. After examining too many abstracts across disciplines, I’ve noticed a strong pattern that’s almost universal. The difference between papers that gain traction and those that collect digital dust often comes down to how their abstracts are constructed. If you want to dive deeper into the actual rhetorics of an abstract, [Ken Hyland is your guy](https://books.google.ca/books/about/Disciplinary%5FDiscourses.html?id=TA2FAAAAIAAJ&redir%5Fesc=y&ref=lennartnacke.com). The idea behind a good abstract (and paper for that matter) is simple: If your readers don’t get the problem, they’re not going to care about your solution. So, yeah, I’m going to use a word that academics really don’t like but the job of your abstract really is to *sell your paper*. And the currency is attention, which eventually converts to citations (and that is the strategy behind a successful tenure track job [that I implement with many of my clients](https://learn.lennartnacke.com/coaching-call/?ref=lennartnacke.com)). The traditional advice for writing abstracts is technically correct but catastrophically incomplete. Yes, include your problem statement, methodology, results, and conclusions. All of that stuff is needed, but how you [frame these elements makes all the difference](https://lennartnacke.com/how-to-make-your-papers-pop-with-good-framing/). I call this rhetorical layering, where you’re creating a compelling argument for the attention of your reader. The most successful abstracts are so much more than just informative, they’re written in a way that is strategically persuasive. Yes, they tell readers what the paper contains but at the same time they convince them why they should care. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Here are the five rhetorical moves that will increase your citation potential: ### 1\. Forget “what I did.” Start with “why this matters.” A typical abstract begins with a vague mention of the problem area. A compelling abstract, however, precisely identifies a research gap and the associated problem using specific language that signals originality. Academic readers aren’t browsing for fun. They’re hunting for relevant, credible research. So the opening move of your abstract has one job, which is to show why your topic matters. You’re strategically establishing context. Don’t just name-drop a field. Signal urgency. Bad: > *“This paper examines X in the context of Y.”* Better: > *“Rising burnout rates in surgical residents have intensified calls for evidence-based interventions. Yet little research has evaluated…”* The second opening doesn’t just state a topic but strategically identifies a specific gap in the literature that creates intellectual tension. That’s the difference between being scrolled past and bookmarked. This tension hooks the reader by activating their curiosity because they now have a challenge they want to see solved. ### 2\. Show the gap and make it sting Your second move is critical because here you want to make readers feel the gap in existing knowledge. That’s how you justify your research. Most papers typically use one of these gap-signalling strategies. Use these rhetorical tactics: - **Absence**: “No study has examined…” It shows very few studies exist on a particular topic. This can be a bit of a gamble if you don’t know the literature extremely well. - **Inadequacy**: “Prior work has overlooked…” Here, you point out specific shortcomings in previous research approaches. - **Tension**: “While A suggests X, B finds Y, which leaves the issue unresolved.” The most popular approach is to address contradictory findings in the literature. Readers want to see how you’ll resolve the intellectual tension. Instead of this simple methodological description: > *“We conducted a survey of 200 participants using a 5-point Likert scale.”* More interesting papers do this: > *“We applied network analysis to longitudinal survey data to reveal previously undetectable patterns in organizational behaviour during periods of institutional change.”* See the difference? The second version doesn’t just tell readers what was done; it signals why the methodological approach gets them new insights. It converts methodology from a procedural note to a value proposition. ### 3\. Describe what you found, not just what you did Here’s where most people mess up. They treat methods like a checklist. A typical abstract presents results as neutral findings. But a compelling abstract frames results as contributions to knowledge. > *“We used a mixed-methods approach and surveyed 300 undergraduates…”* Cool. So what? Instead, frame your method as action. As something that produced insight. > *“Using a mixed-methods design, we identified five previously undocumented motivations for online civic engagement.”* Your method is the road. Your findings are the destination. Talk about both in that sentence. Challenge a dominant assumption that X necessarily leads to Y. Show some new relationships if you can. Your goals is to frame the finding in terms of how they transform existing knowledge. This is crucial because potential citing authors in your field are looking for papers that provide them with useful conceptual tools, not just data points. ### 4\. Lead with the finding. Then frame the impact. The heart of your abstract? Results. But don’t just drop numbers. Make it about your discovery. Compare: > *“Results showed that 68% of participants preferred…”* versus > *“Contrary to existing theories, over two-thirds of participants favoured…”* See the difference? Same result. Sharper framing. More impact. Then. Go meta. What does this finding *mean* for the field? ### 5\. Avoid a lame shrug at the finish and close it with a punch Too many abstracts fade out with vague takeaways. The most common mistake I’ve seen in academic abstracts is ending with tepid implications. > *“These findings may inform future research.”* Yawn. This is where my attention goes to die. It’s just so damn boring. Be more specific. Tell us the exact impact. I would use an i*mplication* to drive home why your paper matters: > *“These findings challenge the dominant model of student motivation and suggest a need for new engagement frameworks in hybrid learning contexts.”* Don’t hedge unless you have to. Use hedges like “suggests” only when necessary. We all fear reviewer 2, so you might have to hedge. But then use booster words like “demonstrates” when your data is strong. Articulate exactly how your findings reconfigure understanding. Here’s an example: > *“We establish the mediating role of power distance in knowledge-sharing behaviours. This study, hence, provides a theoretical framework for predicting innovation outcomes in cross-cultural collaborations and offers practical intervention points for multinational project teams.”* This approach explicitly signals to potential readers how they might use your work in their own research. You’re not just concluding your paper to be done with it, but you’re opening doors to future research that builds on yours. You’re not just telling readers what you did. You’re telling them why they should care. That’s what gets you cited. ### Language that make your abstract more credible The linguistics of great abstracts reveal fascinating patterns some of which are discussed in [Hyland’s work](https://books.google.ca/books/about/Disciplinary%5FDiscourses.html?id=TA2FAAAAIAAJ&redir%5Fesc=y&ref=lennartnacke.com). While low-impact abstracts often hedge with tentative language (“may suggest,” “could indicate”), high-impact abstracts use more definitive language that signals confidence and importance. High-impact abstracts typically: - Use strong, active verbs (“demonstrates,” “establishes,” “transforms”) instead of weak ones (“shows,” “looks at,” “suggests”) - Include linear and constant thematic patterns to improve readability and coherence. - Mix tenses smartly: - Present tense: research problem, aim. Use the present tense for stating contributions, which signals ongoing relevance. - Past tense: methods, findings. The stuff you’ve done for your study. - Present perfect: Connecting past work to present needs. - Strategically place stance markers that signal novelty (“surprisingly,” “contrary to previous assumptions”) and appropriate hedges, boosters, and self-mentions that establish scholarly authority. - Clear transition markers (”However” to signal the problem) between rhetorical moves to help readers follow the logical structure. - Avoid unnecessary jargon while using precise disciplinary terminology that signals insider knowledge and improves searchability. Drop fillers. Say what matters. This authoritative language positions it as significant and worthy of attention. Of course, your results must back this up. ### Do abstract conventions vary across fields? Yes, these rhetorical moves manifest differently across disciplines. [STEM abstracts](https://www.atlantis-press.com/article/125956030.pdf?ref=lennartnacke.com) emphasize methodological novelty and quantitative results, while [humanities abstracts](https://www.semanticscholar.org/paper/VARIATIONS-IN-RHETORICAL-MOVES-AND-METADISCOURSE-IN-Obeng-Wornyo/4092370ae31afeed045947a14532cfb006ce5943?ref=lennartnacke.com) emphasize theoretical framing and interpretative contributions. [Social science abstracts](https://www.semanticscholar.org/paper/VARIATIONS-IN-RHETORICAL-MOVES-AND-METADISCOURSE-IN-Obeng-Wornyo/4092370ae31afeed045947a14532cfb006ce5943?ref=lennartnacke.com) often bridge these approaches. The most successful ones I’ve read effectively blend empirical evidence with theoretical significance, using language that signals both methodological rigour and conceptual importance. For example, in psychology journals, successful abstracts often emphasize the practical applications of findings, while in sociology, they typically show theoretical implications, too. Understanding these disciplinary differences is crucial for tailoring your abstract to your discipline’s audience. ### Don’t make your abstract conventional, make it citable Successful abstracts are well-structured. But they are also strategically written marketing documents for research. They use rhetorical moves that: 1. Create intellectual tension through specific gap identification 2. Position methodology as enabling new insights 3. Frame results as transformative contributions 4. Explicitly articulate broader implications 5. Use authoritative language to signal significance And they do all this while respecting disciplinary conventions. The papers that consistently gain the most citations don’t always have the most groundbreaking research. Rather, they have abstracts that most effectively communicate the value of their research to potential readers. ### A final note The most successful abstracts don’t just tell readers what you discovered. No, they show readers how your work creates space for their own work. They signal how citing your paper would enhance others’ arguments. And because citations are academic currency. When authors cite your work, they’re acknowledging that your research has value for their own. Your abstract needs to make that value proposition explicit and compelling. Some academics might worry that focusing on rhetorical moves feels like marketing rather than scholarship. But I’d argue that communicating your research effectively is an ethical responsibility. What good is your research if no one reads it? If your research has value (and I believe it does), then it’s your duty to make it reach the audience who can use it. Using these rhetorical strategies doesn’t mean exaggerating your findings or making false claims. It means communicating the genuine value of your work in ways that help others recognize its relevance to their own academic pursuits. The academic papers that make the biggest splash are the ones that most effectively communicate their value to the research community. And that communication begins with a strategically written abstract. You got this. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### The perfect abstract formula Here’s your cheat sheet: _This post is for paying subscribers only._ ### A Simple Guide for Early-Career Researchers to Build a Coherent Research Program URL: https://lennartnacke.com/how-to-build-a-coherent-research-program/ Last updated: 2025-05-28T14:06:47.000Z Do you remember the day you defended your dissertation? That dazzling moment when committee members finally called you Doctor and you thought to yourself, *“I’ve actually done it!”* Fast forward to your first faculty meeting as an assistant professor, where your new colleagues casually ask about your research program and suddenly your facial expression changes from confident lion out for a delicious gazelle brunch to deer caught in the headlights of a freight train. Yeah, I’ve been there. Many of us arrive at our first faculty position as accomplished researchers in a narrow field. We’re experts at executing our supervisor’s vision or publishing on whatever caught our interest during grad school. When I started my tenure-track job, I had a folder full of 99 ideas and no clue how they connected. Like many assistant professors, I came from a PhD program where success meant publishing one good paper at a time. What mattered was getting it done, getting it out, and moving on. But when I hit the tenure track, that wasn’t good enough anymore. Here, I needed a coherent, fundable, multi-year research program that could support multiple graduate students. It was a whole different game. Tenure committees, funding agencies, and even graduate students aren’t just evaluating individual projects. They’re looking for a coherent program of research that: - Demonstrates sustained intellectual contribution to your field - Shows clear progression and builds toward something meaningful - Provides structured opportunities for student training - Appeals to external funders - Distinguishes you from your PhD supervisor’s research - Makes your work recognizable and associated with you In other words, a research program is your academic brand. Without one, you’re just publishing random papers. That shift hit me hard. It’s like going from being really good at playing the lead guitar (think: Slash of Guns N’ Roses) to telling the band how to record their songs (like the famous Butch Vig, look him up, music lovers). So if you’re feeling that pressure too, here’s the truth: You cannot build an academic career one unconnected hit paper at a time. You need a story. A through-line. A program that grows with you. Here’s how to make one: ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. #### 1\. Create a research vision (without sounding like a beauty pageant contestant) The foundation of your research program is a clear vision that excites you enough to sustain 5–10 years of work. You need to ask yourself: What do I want to be known for? And, no, sorry, this is not about wanting world peace, but about articulating a specific intellectual contribution you want to make within your field. And it’s much more than just branding but a real direction. Think beyond the specific problem your dissertation tackled. Ask: 1. What’s the *bigger* problem behind the work I’ve already done? What big, meaningful question drives your curiosity? What problem keeps you up at night? 2. What do I believe needs to change in my field? Here you want to get specific about your niche. Instead of saying “I study human-computer interaction,” try something like “I research the emotional impact of virtual reality technology on adult learners.” 3. What’s a 10-year goal I’d love to keep working toward? Force yourself to articulate your big-picture goals concisely. This becomes your **research vision**. It’s not a topic. It’s not a simple result. It’s a destination for your research journey. Write it out in one page. No buzzwords. No jargon. Just get clear on the most desirable outcome of your research. I spent weeks refining my research vision statement until it felt both ambitious enough to matter and focused enough to be achievable. This became my pointer for evaluating every potential project that came my way. > *Example: “I want to make AI-generated feedback more human, starting with education and extending to healthcare and civic tech.”* That’s not a project. That’s a proper path. #### 2\. Build a strategic research pipeline Vision is long-term. Projects are how you get there. Every student in your lab needs a clear, scoped goal. Every paper should connect back to your vision. This makes your research program look coherent. Think of your research program like a sequential series rather than random episodes (compare it to Netflix shows, more like “You” less like “Black Mirror”). Each project should advance the overall narrative. (Ideally, you want it to feel like one of the better seasons of “Game of Thrones,” build that research world of yours, Khaleesi.) A research program isn’t just a collection of related projects but a thoughtfully designed arc where each project builds on or complements the others. Most successful research programs include three types of projects: ![Weighting of projects for the research program.](https://lennartnacke.com/content/images/2025/05/Prof-Project-Planning.webp) Different types of projects for your research program. - **Safe Bet**: Publishable. Feasible. Grad-student friendly. Doable, publishable work that builds directly on existing knowledge. For example: Let an MA student run a pilot study replicating known results in a new population (1–2 years). - **Middle Ground**: Ambitious, but tractable with a team. Projects that stretch into new territory while maintaining reasonable risk. For example: Let a PhD student develop a tool or method from scratch (2–5 years). - **High Risk**: Moonshot. If it works, it changes everything. High-risk, high-reward work that could be transformative (but might also fail). Propose a new paradigm yourself; test edge use cases with your postdocs (4–8 years). Now stagger them. For example, Stanford HCI professor Michael S. Bernstein structured his research program quite smart. His safe project extended his dissertation work on crowd-powered systems, his middle-ground project applied those insights to collaborative writing platforms, and his moonshots explored dense image annotations in computer vision systems that could transform how people work together online. For each project, explicitly articulate how it: - Builds on previous work (yours or others’) - Addresses a specific aspect of your research vision - Feeds into future projects - Provides training opportunities for students So draft your research pipeline like a good trilogy (like back in the day when Star Wars was still good entertainment). 1. **Phase 1**: Foundations (collect early data, publish proofs of concept) 2. **Phase 2**: Expansion (scale methods, secure funding, build collaborations) 3. **Phase 3**: Integration (develop a framework, produce a capstone paper or tool) Draw it. Literally. Flowchart it. I’ve found creating this visual map incredibly helpful (Venn diagrams or concept maps work, too). This means literally drawing the connections between projects to identify gaps or overlaps. Put it in your grants. Bring it to your annual review. It’s a story. You’re the author. #### 3\. Align student projects without exploiting their labour Graduate students shouldn’t just be your paid research assistants. But they should be developing as independent scholars within your research framework. Your research program needs to guide students while respecting their academic autonomy. Think of yourself as a research navigator rather than a dictator. You’re showing students possible paths within your program while letting them chart their own course within the boundaries. **The key is here balance:** - **Scaffold projects appropriately.** Match project complexity to student experience level and capabilities. For example, new Master’s students typically need well-defined projects with clear milestones and regular check-ins, while senior PhD candidates can handle more open-ended research questions and greater autonomy. I would assign pilot studies or replication work to beginning students, then gradually increase complexity as they develop research skills and confidence. - **Create contained contributions.** Design projects that students can realistically complete in their program timeframe. For Master’s students, aim for projects that take 2–3 years. Let them focus on well-defined research questions with clear milestones. For PhD students, plan for 4–6 year projects that allow for deeper exploration while guaranteeing they graduate on time. I would break larger projects into manageable phases with clear publication goals along the way. - **Build connection points.** Show how their work connects to the bigger program, but also allow space for their scholarly identity. I’d want them to see the direct connections between their research and the program’s overarching goals. For example, a student studying AI feedback systems might explore a specific aspect that particularly interests them, like cultural adaptation or accessibility, while still contributing to the program’s broader goals. One of my most successful strategies has been creating research themes, where students work on complementary aspects of a larger question (could also be streams). This creates natural collaboration and peer mentoring while advancing my overall program. #### 4\. Make your research program fundable (but don’t sell your soul) Now that you’ve got the structure down — make it rain, baby. 🤑 Here, I’d start by identifying what part of your arc appeals to which funder. Use tools like [Pivot-RP](https://pivot.proquest.com/session/login?ref=lennartnacke.com) or [GrantForward](https://www.grantforward.com/index?ref=lennartnacke.com) or get help from your local research office. Don’t just chase any call here. Match them to your vision. A research program without funding is just a wish list. But chasing grants without a coherent vision leads to intellectual whiplash. Start with small, pilot grants. Build credibility. Then scale. Always target funders that align with your vision rather than completely reshaping your work. Here’s a good rhythm: - Year 1: Internal funding + student RAships. Create a funding plan showing how early projects generate preliminary data for larger grants. - Year 2–3: National or discipline-specific small grants. Develop relationships with program officers who can provide guidance. Prepare for rejection and use feedback to improve - Year 4–5: Big government, federal, or foundation grants (NSERC, SSHRC, CIHR, NSF, NIH, Horizon, etc.) Always have one grant under review, one in prep, one that just got submitted. Rotate like clockwork. I’ve found that the most successful grant strategies don’t try to fund the entire research program at once. Instead, they slice the program into fundable chunks while showing how each piece contributes to the larger vision. Mixing and matching funding lets you leverage your grants against one another to maximize the funding you get. #### 5\. Build an adaptable program (because it never works out as planned anyways) Here’s the part we don’t talk about enough. Things never work out as planned anyways. Something about the journey being the destination if you know what I mean. No research program ever unfolds exactly as planned. The sooner you accept this, the happier you’ll be. (It’s the reason why we love watching movies like “The Hangover” because sh\*t hitting the fan is something everyone can relate to.) So plan your research program with flexibility: - Allow new directions. Yes. But cap them. One a year max. - Build in contingencies. What if the data don’t work? What if the method fails? - Keep a “back-pocket project.” Something quick, publishable, and satisfying. For morale. And most important, review the whole thing once a year. Update your CV. Tweak your plan. Get feedback as your research program evolves. My original five-year plan was slightly disrupted when: - A key paper really hit the Zeitgeist and enabled opportunities that I hadn’t really had before - Early key students dropped out of my program - A funding opportunity emerged that wasn’t on my radar - A superstar student joined my team with interests that slightly shifted my focus Rather than seeing these as failures of planning, I learned to treat my research program as a living document. Simply a framework that guides decisions rather than dictates them. **My adaptive approach:** - Review or revise your plan annually - Maintain your core vision while being flexible about pathways - Limit new directions to 1–2 per year - Document changes and their rationale for your tenure narrative #### One secret senior faculty won’t tell you After years of observing successful academics, I’ve noticed something they rarely share. Great research programs often come together like a growing a vegetable garden. You don’t always see the fruits of your labour until you’ve sown many seeds, started many little plants and let it all grow organically. You need the right amount of sun and rain, too. And that’s okay. I used to think I needed a perfect plan from day one. But the best academics I know tell stories about how their work found its direction over time. They kept doing good work and connecting the dots as they went. This doesn’t mean you shouldn’t plan. But make your plan flexible. Leave room for happy accidents and new ideas. Some of the best discoveries in science came from researchers following their curiosity down unexpected paths. Think of Alexander Fleming finding penicillin (a penicillium mold spore likely drifted into his lab from the air and grew under the right temperature conditions while he was on vacation). Some of the greatest discoveries aren’t in original research plans. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. #### How to get started? If you’re feeling overwhelmed after just starting your tenure-track job, start here: 1. **Draft your research vision** in one page 2. **Identify three potential projects** (safe, middle, moonshot) 3. **Map connections** between these projects 4. **Schedule a coffee with a recently tenured colleague** to discuss their research program evolution or [book a coaching call with me](https://learn.lennartnacke.com/coaching-call/?ref=lennartnacke.com). 5. **Block time on your calendar** each week specifically for program development This isn’t all about getting tenure. Not really. It’s about becoming the kind of researcher who doesn’t chase trends but defines them. It’s about mentoring your students with purpose (not randomness). Going from publishing papers to leading a research program starts when you stop asking, “What’s next?” Your research program is about building a body of work that reflects your intellectual contribution to the world. Make it count. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## The 5-10 year research program blueprint ### (Cheat Sheet) _This post is for paying subscribers only._ ### How to Find the Perfect PhD Supervisor URL: https://lennartnacke.com/how-to-find-the-perfect-phd-supervisor/ Last updated: 2025-10-22T01:10:43.000Z Picking your PhD supervisor might be the biggest choice you'll make in grad school. I've seen it play out hundreds of times. Good and bad. This relationship shapes your whole academic journey. Here's what fascinates me. Some smart students bump into major challenges with the wrong advisor, while others absolutely take off with the right mentor by their side. I’ve have several undergraduates, who weren't top of their class, but their future supervisor believed in their ideas and supported their growth. Some of them are now publishing at CHI and absolutely flourishing in grad school. The truth is simple. Find someone who wants you to succeed. It makes all the difference. Today, I'll show you five clear steps to make that happen. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 1\. Find Your Perfect Research Match When I first considered pursuing a PhD, I was starstruck by big names in my field. But I quickly learned that reputation alone isn't enough to get you through. And I certainly didn’t spend enough time on my own choice of PhD supervisor, so I learned this the hard way during my PhD. What matters most is finding someone whose research genuinely excites you and aligns with your goals. Start by reviewing their recent publications, projects, and lab website. Are they currently working on topics that make you want to jump out of bed in the morning? That’s a good sign. A supervisor with deep expertise in your area will provide more valuable guidance than someone only tangentially related to your interests. There is a lot of evidence that students mentored by Nobel prize winner are more likely to become superstars in their field or win Nobel prizes themselves, compared to equally talented students with non-prizewinning advisors. So, checking the track record really helps. High achievers attract and support other high achievers. But here's the crucial part that many prospective students miss. It’s nice if your supervisor is accomplished but it’s even better if they’re also genuinely interested in your project and they have time for you. Some of the most senior superstar professor won't help your career if they're too busy to meet with you or disinterested in your research question. You’ll be fighting for yourself. ## 2\. Pick Someone Who Fits Your Learning Style Every supervisor has a unique approach to mentorship. Some are hands-on, providing detailed guidance and frequent check-ins. Others are more hands-off, expecting students to work independently and seek help when needed. Neither style is better here, but one will likely be better for you. Here's my top advice from years of advising my own students. Have an honest chat with any supervisor you're thinking about. Ask them straight up: "How do you like to work with students?" I always tell my students to find out the basics: When will we meet? Will you set deadlines for me or should I plan my own schedule? How quick are you with email replies? These simple questions can save you years of stress. I learned this myself when I picked my own postdoctoral advisor, just getting clear on their style made everything smoother and the whole experience better than my PhD experience. The most revealing strategy, however, is talking to current or former students. They'll give you the unfiltered truth about what it's really like to work with this professor. Are they supportive during challenges? Do they provide constructive feedback? Do they respect work-life boundaries? I've seen this play out many times in my career. A professor with impressive awards might turn out to be too busy for their students. On the flip side, I know several less famous professors who make time every week to meet with their students and give kind, helpful feedback. When you talk to their students, they all say the same thing: These professors helped them grow and succeed. That makes the choice pretty clear, doesn't it? Think of your PhD supervisor like a coach for a long race. I know from experience that having someone who matches your work style makes training fun and gets you to the finish line faster. But working with a supervisor who isn't right for you? That's like running in shoes that don't fit. It just hurts. And some of those bruises take forever to heal. Take time to find someone who fits you. ## 3\. Build Your Network and Career While You Learn I always tell my students that a good supervisor is more than just a research critic. They become your mentor, your champion, and someone who opens doors for your future. Let me share a quick story. One of my PhD students struggles with public speaking. So, we practice together before conference talks and he’s getting better at presentations with every talk he gives. My goal is always to help him grow beyond just his research. When evaluating potential supervisors, look for evidence that they support students' career development: - **What are their former students doing now?** Have they secured academic positions, industry roles, or notable postdocs? - **Do they introduce students to their professional network?** Will they connect you with collaborators and colleagues in the field? - **Do they encourage conference participation?** Will they help you present your work and meet key players in your field? Let me tell you about two PhD students I met a conference. Both did great research. The first one had trouble finding a postdoc job. The second one got lots of job offers. Why? Their supervisors made all the difference. The second student's supervisor brought them to conferences, wrote scholarship applications with them, and introduced them to other researchers. Those connections opened up amazing opportunities. I've seen this pattern play out many times in my career, too. Having a well-connected mentor who believes in you can turn good work into great opportunities. An effective mentor continues supporting you even after graduation. They provide references, alert you to opportunities, and offer career advice. This long-term relationship can be invaluable as you navigate your career path further. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) ## 4\. Let's Talk About Money & Resources Yes, I know what you’re thinking. Money isn’t everything. We’re not scientists for the money. But forget about your red hot altruistic mantra for a hot second there and trust me that money does matter even in research because you can't eat prestige or pay rent with interesting research questions. Money matters in a PhD. Your funding shapes how much time you can spend on research and whether you'll need a side job to make ends meet. Before committing to a supervisor, have a frank discussion about funding: - Will your position be funded through the supervisor's grants or projects? - For how many years is funding secured? - What contingencies exist if grants end? - Are there expectations for you to secure external funding? Money isn't just about your salary. What you'll need to do great research are tools and tech to test your ideas, software to analyze your data, and a budget for research participants. And don't forget travel costs for conferences or fieldwork. I've seen the difference with some of my underfunded colleagues, students with the right resources get their work done faster and better. When I started my PhD, I wish someone had told me to check if the lab I went to had all the equipment I wanted. Trust me, having access to the right resources makes your research life so much easier. Taking a part-time job during your PhD can slow down your research. Some of the best students I’ve met struggled to balance work and studies. But the good news is that with proper funding, you can focus 100% on your research. My advice here? Look for programs and supervisors who offer full funding. Your PhD journey will be much smoother when you can put all your energy into your work. The right supervisor will be transparent about the funding situation and may even help you with fellowship applications if needed. ## 5\. Choose Your Perfect Lab Fam & Culture This final step might seem less tangible than the others, but it's probably the most important: Do you actually like this person and their crew? You'll spend years working closely with your supervisor. Through the ups and downs of research life, they'll be right there with you. I've learned from my own PhD journey that picking someone you click with makes all the difference. When I finally found a supervisor I could talk to easily, my research just flowed better. But when that connection wasn't there? Even simple meetings felt like a chore. A large part of my PhD was quite painful. During initial meetings, pay attention to how you feel. Do you respect them, and do they respect you? Does their communication style work for you? If something feels “off” now, it will likely intensify under the pressures of a PhD program. Or completely combust and take your dream of doing a PhD right there with it. Beyond the supervisor themselves, consider the lab or group culture they've created: - Are current students positive and collaborative? - Do lab members help each other or compete destructively? - Is there a healthy work-life balance, or is everyone expected to work around the clock? Gosh, I’d really wish we had something like the Sorting Hat from Hogwarts to choose our PhD supervisor? Just imagine: put on an old academic cap, and it announces "Ah yes, you’re clearly a Griffindor." But alas, in the real world, we don’t get the magical certainty of the Sorting Hat's choice. Finding the right supervisor requires good old-fashioned research and relationship building before you start your studies. If you see a lab where students look exhausted and barely spoke to each other, avoid that Slytherin party like a Hufflepuff avoids bragging. Look for places with lively discussions and genuine camaraderie. Pick an environment that produces better research and happier graduates at the same time. If possible, try working with a potential supervisor on a small project before fully committing. This trial run lets you experience their mentoring firsthand and see if the personal fit is right. ## Make a Smart Choice to Find the Right Match The ideal PhD supervisor will be someone who: - Shares your research interests and has the expertise to guide you - Has a mentorship style that matches your needs - Actively supports your career development - Can provide or help secure adequate funding - Creates a positive, respectful lab environment where you can thrive Picking a PhD supervisor is about so much more than just finding someone to grade your work. You're choosing your guide, your mentor, and the whole lab family you'll probably see a little more than your own family for the next few years. Take your time with this choice. It's like picking a new home. You want to feel comfortable, supported, and ready to grow there. I know this process takes time and effort, but the return on investment is enormous. A great supervisor can transform your PhD experience from a gruelling endurance test into a challenging but fulfilling journey of growth. Having the right mentor makes all the difference between dragging yourself to work and jumping out of bed excited to tackle new research challenges. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. I hope this helps you on your grad school journey. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). *Here is a cheat sheet to help you get sorted:* _This post is for paying subscribers only._ ### How to Actually Get People to Fill Out Your Research Questionnaires URL: https://lennartnacke.com/how-to-actually-get-people-to-fill-out-your-research-questionnaires/ Last updated: 2025-05-28T14:08:54.000Z I was sitting at my desk, staring at my inbox with the kind of disappointment I had usually reserved for when someone ate the last kanelbulle from the fika room (I did my PhD in Sweden, for cultural reference). My perfect research questionnaire had been out in the wild for a week, and I had exactly eight responses. Not the hundreds I dreamed of (or wanted). Not eight hundred. Not eighty. Eight. Fuuuu…. My colleague’s words echoed in my head: “You know, you study might fail not because of bad methodology, but because you just might not get enough participants.” The guy was a few fries short of a happy meal, but he was right. What a smart prick, I pondered for a second. If you’re nodding along in painful recognition while I’m telling you this, you’re my people. The academic world doesn’t prepare us for the reality of creating questionnaires. The ones people actually want to complete. And for recruiting participants who don’t immediately delete your email, which really is an art form unto itself. But the good news is that after trying (and failing!) many times to get people to fill out my surveys, I’ve learned what works. And yes, that includes the time I thought about quitting to become a professional ice cream taster instead (still thinking about it sometimes though). Now I’m ready to share my tips with you. Here’s my guide to getting those precious survey responses you need. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Why your questionnaire is secretly terrible (sorry!) I need to tell you something about that questionnaire you’ve been working on for weeks. What I learned the hard way is that even our best-planned surveys can totally miss the mark. Not because we lack skill, but because good questionnaire design just takes solid practice. Trust me, I’ve been there. And I can help you make it better. ### Write clear questions users can understand Remember back in undergrad when your professor assigned that one reading where you understood approximately three words per page? (Was it William Faulkner? 🤣) That’s how participants feel when they encounter showstopping academic jargon in your questionnaire. Instead of asking: “Rate your level of engagement with the user interface’s navigational architecture:” Try something a little easier on the eyes and brain: “How easy was it to find what you needed on the app?” The first makes people’s eyes glaze over faster than a PowerPoint presentation from the leadership team at 4:30 PM on a Friday afternoon. Snorelax, man. The second might actually get answered. ### Avoiding double-barrelled questions One of the most common questionnaire sins is the double-barrelled question. It’s like asking someone “Do you like chocolate and broccoli?” (I’m actually weird and I do.) How do they answer if they love chocolate but hate broccoli with the burning passion of a thousand suns? Well, they won’t, they’d rather [run away to Mars](https://www.youtube.com/watch?v=jMPkCCxkEVI&ref=lennartnacke.com) (love that song, sorry). I once included this batshit beauty in a survey: “How satisfied are you with the speed and reliability of the game controls?” Luckily, I piloted that sucker and found got some feedback along the lines of “yes, it’s fast but crashes every 20 minutes.” Point taken. Right there. Split those conjoined question twins into separate entities. We’re not filming American Horror Story Season 4 here and your question shouldn’t be the Tattler twins. ### Make it run on mobile So, sure, you might run your survey as part of a lab experiment and you’re providing the computers and everything. But there is also the scenario, where you put it out in the wild and you don’t quite know how people answer it. A busy professional might get your survey link, opens it on their phone during their commute, sees it’s a horizontal scrolling nightmare that looks like it was designed for a 1998 desktop computer, and promptly closes it forever. Not fun. Most people (about 70% of young adults) fill out surveys on their phones these days. If your survey looks bad on mobile, you’re saying goodbye to a lot of responses. The good news? Testing is simple. Just open your survey on different devices before you share it. I now always ask a few students to test the surveys on their different phones. It takes 5 minutes and saves me from those “why isn’t anyone responding?” moments. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### More strategy in participant recruitment If “please, please fill out my survey” has become your mantra, it’s time for a strategic overhaul. ### The Multi-Channel Recruitment Framework Don’t fall into the email-only trap like I did! I learned it the hard way that sending mass emails and hoping for responses is about as effective as trying to catch fish in my coffee mug. My whole approach changed when I started thinking like a marketer instead of a desperate researcher. Here’s what I mean: - **Email.** Still effective, but personalize those subject lines. - **Social Media.** Target platforms where your demographic actually hangs out. Reddit is great for certain fields. X is pretty much dead these days and full of ChatGPT bots. - **Research Communities.** Some subreddits on Reddit exist specifically for research recruitment. Some Discord servers have huge communities attached. - **Existing Networks.** Never underestimate the power of asking people you know to share with people they know. ### The timing conspiracy nobody tells you about A secret that took me years to discover was that **“*when”*** you send your recruitment message matters almost as much as **“*what”*** it says. Surveys distributed mid-morning (9–11am) on Tuesdays through Thursdays yield approximately 24% higher response rates than those sent at other times. It’s like the universe has a schedule for when people feel generous with their time. Follow it. One last tip from my own painful experience: Don’t send out surveys during finals week or end-of-semester crunch time. Students and faculty are too busy pulling all-nighters and grading papers to fill out your survey. Trust me, crunch time gets you about as many responses as a salad bar at 3 AM on a Monday morning. ### What to do for better response rates Despite your best efforts, sometimes response rates still resemble a ghost town. Here’s where the rescue operation begins. ### Follow-ups The art of the reminder is subtle. Too aggressive, and you’re annoying; too passive, and you’re forgotten. The sweet spot? A structured approach with multiple touchpoints: 1. **Initial invitation.** Your first, enthusiastic outreach 2. **First reminder.** 3–5 days later, gentle nudge 3. **Final notice.** 7–10 days after initial invitation, create urgency Each message should have a distinct tone. A successful personalized final reminder could open with something like: “Last chance to contribute to research that might actually change how we design mobile applications (and help me avoid having an existential crisis about my PhD).” That touch of vulnerability and humour should increase your reply rate. ### Think about incentives Let’s talk money and motivation for a bit. So, should you pay people to fill out your survey? A quick web search provides a clear answer: smaller guaranteed incentives ($5 for everyone) typically outperform larger lottery-based incentives (chance to win $500). When budget constraints make even small incentives impossible, consider non-monetary options: - Early access to research findings - Professional networking opportunities - Contributing to meaningful research (surprisingly effective) ### Survey fixer upper Combat the dreaded survey fatigue by designing an engaging experience: - **Visual appeal.** Make your design clean and professional to match your target audience - **Progress indicators.** Show a progress bar to keep participants motivated - **Question variation.** Keep participants engaged by using different types of questions - **Storytelling.** Guide participants through a natural flow instead of random questions Turn that cold questionnaire into a friendly chat, but make sure to keep your questions valid. Best way to get some flavour text in, is in the instructions before and after. But it all has to go through ethics clearance first, so make sure you start drafting this early. ### Always ethics first While chasing responses, never compromise on ethics. The pressure to gather sufficient data can sometimes conflict with ethical practices. In general ethics boards will have to approve your survey when you’re working at a university. But in general, you maintain your integrity by: 1. **Avoiding undue influence.** Separate recruitment from power relationships 2. **Ensuring voluntary participation.** Create genuine opportunities to decline 3. **Providing transparent information.** Give potential participants sufficient details Ethical recruitment not only protects participants but typically yields higher-quality data through authentic engagement. If you have a deception study, you’ll have to work with your ethics board to guarantee proper disclosure at the end. ### Some parting words for the data-hungry Getting questionnaire responses takes both planning and people skills. I learned that good planning means thinking through all the details. But what really matters is connecting with people in a way that makes them want to help. From my experience [teaching research methods](https://newsletter.nacke.ca/products/mini-research-course?promo=NEWSLETTER&ref=lennartnacke.com), students who build those connections get way more responses than those who just send out cold emails. Remember that behind every response (or lack thereof) is a human making decisions based on competing priorities. Your job is to make participating in your research feel worthwhile, accessible, and maybe even enjoyable. And when all else fails, remember the academic researcher’s mantra: adjust expectations, extend deadlines, and know that every single response is a small victory against the void of non-response. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Cheat Sheets _This post is for paying subscribers only._ ### Why Most PhDs Misuse AI Writing Tools URL: https://lennartnacke.com/why-most-phds-misuse-ai-writing-tools/ Last updated: 2025-05-28T14:12:56.000Z When ChatGPT first emerged, I spent three weeks in my university office pretty much with the door locked for most of the days, testing what it could actually do for academic writing. As a PhD who was deeply curious how experts integrate AI into their workflow, I needed to know: Was this the academic equivalent of discovering fire, or just another shiny distraction? And I went to work on it hard. Until then, I was mostly known for my games and gamification research in HCI, but over the last year, I’ve been obsessed with understanding generative AI writing. So, what’s my current verdict? Most AI-generated academic text feels like fast food (sorry, my intellectual mavens): Convenient, predictable, and utterly forgettable. You could call it a faster way to mediocrity. It averages out any intellectual stimulus that you possess. That’s fine if you’re just drafting routine emails. Not so great if you’re trying to contribute meaningful research to your field. But, hey, it’s become highly popular since. Here’s what most academics miss though: AI doesn’t have to dilute your writing but it can actually elevate it when used strategically. I’ve been analyzing hundreds of research papers and I keep discovering fascinating patterns in how successful scholars can use these tools correctly. So, of course, I had to compile a list. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Why most AI-generated academic writing falls short Let’s be honest about what happens when most academics throw a prompt at ChatGPT for the first time (I mean I was clueless once, too): 1. *Prompt*: Write a discussion about cognitive load in educational video games. 2. *Result*: Generic zombie text that sounds vaguely academic but contains no original ideas. 3. *Outcome*: If you were to submit that anywhere, reviewers would likely comment lack of theoretical depth and links to the literature. It’s like asking a parrot who’s memorized a textbook to develop a new scientific theory. Not happening, Captain Flint (wait, you didn’t read Treasure Island yet? Maybe fix that this summer?). The problem isn’t the AI and its capabilities here (although more expensive models generally give much better results) but it’s how we’re using it. And after three years studying this intersection, I’ve identified six strategies that separate mediocre AI strategies from genuine research accelerators. Let me share them with you. ### 1\. Master prompt engineering Remember in *The Matrix* when Neo couldn’t jump the first time because his mind wasn’t ready? That’s most academics using AI prompts. They’re setting themselves up for failure before they even begin. Let me share with you my top three AI prompts that I’ve tested and refined over time. They’re simple but powerful. I use them every week and they’ve made a huge difference in my writing: ### Prompt 1: Developing research questions [*(upgrade to paid subscription for full prompt)*](https://go.lennartnacke.com/signup?ref=lennartnacke.com) This prompt helps you find great research questions. I tested it extensively in [Perplexity AI](https://plex.it/referrals/3HSGWX0S?ref=lennartnacke.com) with the academic feature turned on, which keeps the AI from making things up. It also works well with ChatGPT Pro when you turn on web search. But here’s a tip: skip the free AI models, since they often invent fake sources. What I love about this prompt is how it turns your big ideas into specific questions worth studying. It works so well because you tell the AI exactly what you want: tough questions (not basic stuff), open questions (that need real research to answer), focused topics (not too broad), and (maybe most important) questions that connect to real research that’s already out there. I’ve used this to explore deceptive design of AI bots and received five great research questions that sparked an entire research project. ### Prompt 2: Justifying methodological choices [*(upgrade to paid subscription for full prompt)*](https://go.lennartnacke.com/signup?ref=lennartnacke.com) This prompt is like your methodological defence attorney. We all know that moment when Reviewer 2 attacks your methodology choices like they’re personal insults. This prompt helps you build a bulletproof case for why your chosen method isn’t just acceptable but actually optimal for your work. Works best with reasoning models like o1 Pro or Claude with extended thinking. The secret ingredient of this prompt is forcing a comparison with alternatives. I watched a colleague struggle through three painful revisions because he couldn’t articulate well why ethnography was better than surveys for his specific research question. This prompt would have saved him months of academic purgatory. Save yourself the hassle. ### Prompt 3: Editing tone and style [*(I'm giving you the one below as a freebie and preview for paid)*](https://go.lennartnacke.com/signup?ref=lennartnacke.com) > Act as an academic editor. You prefer active over passive voice. Review the following text: "\[Paste text\]" > Rewrite it to enhance clarity, conciseness, and formality, ensuring an objective and academic tone, but not to make it sound too stiff. It should be formal but easy to read and lively through variation of sentence and paragraph lengths. Replace jargon where appropriate, simplify complex sentences, and ensure consistent terminology. Clarity is your main objective. Avoid passive voice where active voice is stronger. Consider this your personal academic stylist. It transforms your just poured my thoughts onto the page at 2 AM draft into something that sounds like you wrote it after eight hours of sleep and three cups of perfectly brewed morning latte (skinny, of course, because we’re cutting calories for that summer bod). The excellence of this prompt is its balance. Academic writing doesn’t have to be the linguistic equivalent of a beige wall. This prompt preserves formality while injecting readability. To me, that is how you write papers people actually *want* to read, not just drive-by cite. I’ve tested these prompts with clients across multiple fields — from neuroscience to architecture — and they consistently outperform generic requests. The key is specificity. You’re not just asking for output but establishing parameters for quality. ### 2\. Treat AI drafts as just raw draft material The most successful academic AI users I’ve interviewed approach first drafts as exploratory material. They don’t ask: ”Is this good enough to use?” They ask: “What useful elements can I extract and reshape?” Here’s my typical workflow for this: 1. Generate multiple versions of the same section with different prompts 2. Extract useful phrases, transitions, or structural elements 3. Ask the AI to critique its own output: “What critical perspectives are missing from this analysis?” 4. Request iterative improvements: “Rewrite this paragraph to better connect with the theoretical framework I outlined earlier” I recently worked with a professor, who found a creative way to use AI. She asks AI to write three different viewpoints on her research questions. This helps her think through her ideas better. Think of it as a peer study group that’s available 24/7\. She told me that reading these AI perspectives helps her sharpen her thoughts because she sees what’s missing and where she can add something new. ### 3\. Verify every single thing We all remember the times, ChatGPT cites a completely fabricated 2022 meta-analysis about possibly even your own exact research topic. Such nonexistent papers usually have a plausible title, journal name, and even detailed findings. But they’re all conjured from the digital ether. So, unless you’re using a cool tool like [Consensus](https://go.lennartnacke.com/consensus?ref=lennartnacke.com) ([watch my video](https://youtu.be/g1qgFLoCMu4?si=z0jUwXXMJyVWQl2Q&ref=lennartnacke.com) and use code LENNARTNACKE100 for 1 year free premium), [Elicit](https://go.lennartnacke.com/elicit?ref=lennartnacke.com), [SciSpace](https://go.lennartnacke.com/scispace?ref=lennartnacke.com), or [Scite](https://go.lennartnacke.com/get-scite-ai?ref=lennartnacke.com). Forget about it. Use my code LENNARTNACKE100 for 1 year of free premium [Consensus](https://go.lennartnacke.com/consensus?ref=lennartnacke.com). Here’s something wild. Great professors get fooled by AI because they treat it like a walking encyclopedia. But AI is more like a smart echo chamber it strings words together beautifully without truly knowing if they’re true. We’ve all heard about people who cite AI-generated papers in their research, only to discover later that the paper never existed. [Retractionwatch](https://retractionwatch.com/?ref=lennartnacke.com) lives for that drama. Let’s make this simple for you. Check everything. Always. When AI mentions research, look it up. When it gives you numbers, find the real source. When it talks about theories, double-check who came up with them. First. These small steps will save you from big headaches later. Web search and Perplexity AI reduce hallucinations but it never hurts to double-check. I follow a simple protocol. Highlight all factual claims in AI-generated text and verify each one before incorporating them into anything. Tedious? Yes. Necessary? You betcha. ### 4\. Infuse your own identity in the output AI writing has a particular voice. Often somewhat formal, overly balanced, and distinctly lacking in intellectual courage or disciplinary perspective. No, whoom bazzle in those outputs, let me tell ya. It’s the bland elevator music of academic writing. Hey, don’t get me wrong. I know academic writing has to be somewhat dry sometimes, but it really doesn’t have to be boring. I found that smart researchers build their own special style on top of AI drafts. It’s like taking mom’s recipe and adding your own secret ingredients (because you know, she left some out on purpose, too). In fact, when I write this newsletter, I take AI text and add my own stories, ideas, and a little bit of crayfish to it. I feel like this makes my writing more real and readable. Here’s what I recommend: - Rewrite key sections to include methodological perspectives specific to your corner of the field - Add subtle critiques that reflect your research lineage - Incorporate personal research experiences or takes on related work that contextualizes the discussion - Restructure arguments to reflect your intellectual priorities I use AI to write the parts that sound like everyone else so I can focus my energy on writing the parts that could only come from me. I think that’s a good way to go. ### 5\. Expand beyond drafting Researchers fixate on using AI to often generate draft text, but there are far more powerful applications throughout the research process. My most productive colleagues use AI for: - Creating structured outlines for grant proposals (especially useful for breaking through writer’s block) - Use research paper summary GPTs to quickly assess relevance (though they always read the full paper before citing). [I’ve got one of those in today’s issue for paid subscribers](https://go.lennartnacke.com/signup?ref=lennartnacke.com). - Proofreading for clarity, consistency, coherence, and compellingness - Brainstorming research questions from different theoretical angles - Converting dense theoretical concepts into accessible explanations for interdisciplinary audiences I recently used a customGPT to analyze patterns across several literature reviews in my field, quickly identifying methodological gaps that would have taken me twice as long to spot usually. But it’s good to think about what you should and should not automate in literature reviews. We actually published a framework for this called [the INSPIRE framework](https://dspacemainprd01.lib.uwaterloo.ca/server/api/core/bitstreams/8bb544c0-dc8c-4cba-a1ae-a8ecde939045/content?ref=lennartnacke.com) (check it out). ### 6\. Maintain integrity through transparency Some academics hide their AI use like it’s academic steroids. And sometimes with good reason, [because some reviewers simply desk-reject papers that declare AI use](https://www.linkedin.com/posts/dries-faems-0371569%5Fi-got-my-first-desk-rejection-because-of-activity-7314616829291683840-j4zl?ref=lennartnacke.com). It’s the wild west currently. But secrecy not only creates unnecessary anxiety but prevents the development of shared best practices. We wrote about this [in our AI witch hunt article](https://www.sciencedirect.com/science/article/pii/S2949882124000550?ref=lennartnacke.com). Our research indicates that transparent AI use builds rather than diminishes scholarly credibility when: - The researcher’s intellectual contribution remains substantive and original - Institutional AI guidelines are followed - AI assistance is appropriately disclosed - The quality of the final product demonstrates scholarly rigour Every field is developing norms around AI use. Be part of shaping those norms rather than pretending the tools don’t exist. Or experience the wrath of a dying generation of academics. ### Academic writing will not be AI or human but simply both I’ve seen both extremes here. Luddite professors who refuse to use any AI tools, struggling with tasks that could be optimized, and AI enthusiasts who generate entire manuscripts with minimal human input, producing technically correct but intellectually vacant work. Both of these are not the right ways of doing things in my opinion. The sweet spot lies in a grey zone as usual, which is using AI to handle routine aspects of academic writing while preserving the uniquely human elements that make scholarship valuable: theoretical insight, methodological innovation, and intellectual creativity. Here’s my challenge to you then. Experiment with one of these 6 strategies in your next writing task. Don’t outsource your thinking, but don’t waste your cognitive resources on tasks AI can actually handle competently. AI writing assistance is neither academic miracle nor intellectual apocalypse. It’s simply a tool. One of many. And like any tool, its value depends entirely on how skillfully you go about using it. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Prompts and Cheat Sheets (for paid subscribers) _This post is for paying subscribers only._ ### 7 Strategies for Mixed Methods Research Papers URL: https://lennartnacke.com/7-strategies-for-mixed-methods-research-papers/ Last updated: 2025-05-28T14:30:49.000Z I stared at my computer screen in total darkness. The familiar panic was rising again. After six months of collecting both physiological sensor data and in-depth interviews for my mixed methods study, I now faced the daunting task of actually writing the paper. My quantitative results sat neatly in one folder, my qualitative analysis in another. My brain, on the other hand, had absolutely no idea how to merge them into a coherent whole. This wasn't my first research paper, but doing mixed methods research felt like playing two different games simultaneously while following a third set of rules. I was baking a cake and grilling a steak at the same time. And neither was close to being well done. I was supposed to create something better than either alone. If you're feeling similarly overwhelmed by the complexity of writing a mixed methods research paper, I feel you. While there's plenty of standard advice out there (state your rationale, describe your methods thoroughly, yada yada yada), I've found some strategies that I think make the difference between a paper that merely presents two types of data and one that truly capitalizes on the synergistic power of mixed methods. And hey, it’s something I’ve been teaching more than 45 of you already [​in my brand new 7-day email course​](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) among many other deep research tips. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 1\. Start with an integration plan Most researchers begin writing with their introduction or methods section. For mixed methods, this is a mistake. This is the reason: The *integration* of qualitative and quantitative components is the heart of mixed methods research. Without meaningful integration, you don't have true mixed methods. You just have two studies stapled together. How about doing this a bit differently? Before writing any section, sketch out a detailed plan for how you'll integrate your data. Will you merge them in the results? Will one build on the other? Create a diagram showing exactly where and how the different data types will connect. Use this concept map as a guide for your entire paper. I recently tried this approach in [​Miro​](https://miro.cello.so/OCG1dR4PHkS?ref=lennartnacke.com), which I used for early idea sketching for many research projects. I realized I needed a joint display (a table directly comparing qualitative themes with statistical results) for one research question, but for another, I needed to show how the interviews explained unexpected survey findings. Having this clarity before drafting saved me from creating a slightly awkward flow of things in the paper draft. ### 2\. Create a divergence section for conflicting findings One of the most uncomfortable aspects of mixed methods research is when your quantitative and qualitative data tell different stories. Many researchers make the mistake of either: - Downplaying the conflicts - Assuming one data type (usually quantitative) is "more correct" - Getting stuck trying to force a coherent narrative We don’t really want that, because it messed with the overall argument of our paper. So, I’d recommend to embrace the tension by creating a dedicated divergence section in your results or discussion (or Discrepancies or whatever you want to call it). This section specifically discusses where your different data types conflicted and explores possible reasons why. Much more insights for your readers that way. For example, in my research on video games, we often have physiological data that show no correlation between things like audio effects and player engagement levels, yet this theme might dominate in our interviews. Rather than hiding this discrepancy, you could create a section like “the subjective effects of game audio,” where you explore potential explanations for the mismatch: - The possibility that physiological measurements don't capture subtle emotional responses to audio that players can articulate - The cultural tendency for some players to emphasize audio importance in game experiences - The possibility that our interview sampling inadvertently included more audio-sensitive players This section can easily become one of the most insightful parts of your paper, where you find hypotheses for future research and show methodological sophistication. Definitely an opportunity here. ### 3\. Use the point-counterpoint narrative technique The standard format for presenting mixed methods results (quantitative results followed by qualitative results) can feel disjointed and makes integration harder for readers to understand. Take a crack at the following instead. Structure your results using a point-counterpoint technique, where you present a quantitative finding immediately followed by relevant qualitative data that either confirms, contradicts, or adds context to that specific point. This can create an interesting contrast in your research paper that makes it more fun to read for your audience. For example: *Point*: Survey results revealed that 76% of participants reported high satisfaction with the game (M = 4.2, SD = 0.8). *Counterpoint*: Interview data provided some distinction to this satisfaction, with many participants expressing conditional approval: "I'm happy with what they're trying to do, but the execution needs work" (Participant 14). This satisfaction appeared contingent on participants' expectations, as one participant noted: "I came in with low expectations, so I'm pleased with what I got" (Participant 8). This approach creates a natural dialogue between your data types and makes integration visible to readers throughout your results, not just in a separate section. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 4\. Transform your data in both directions Data transformation—converting one data type into another—is a powerful but often underused integration technique. Most researchers only transform in one direction (typically qualitative into quantitative by counting theme frequencies or something similar), missing half the potential. Here is a technique I’ve seen used but not applied yet directly myself. Transform your data bidirectionally: 1. **Qualitative → Quantitative**: Yes, count theme frequencies, but also create typologies from your qualitative data and map your quantitative participants onto these types. 2. **Quantitative → Qualitative**: Create narrative profiles or personas based on statistical clusters or patterns. For example, after identifying three distinct response patterns in my survey data, I created composite narrative descriptions of what a "typical" person from each pattern might experience, incorporating relevant quotes from participants who fit each pattern. This bidirectional transformation deepens your analysis and creates multiple integration points. When you try transforming in both directions, you might find patterns you’ve completely missed when analyzing each data type separately. At least that’s what usually happens when I add an extra lens on my data. ### 5\. Integrate your Hyde and Jekyll Mixed methods papers often suffer from the Jekyll and Hyde problem, a split personality—technical and distant for quantitative sections, rich and interpretive for qualitative ones. This stylistic disconnect makes papers harder to read and integration more difficult to communicate. Here is an experimentation with this that I would suggest. Consciously develop a consistent integration voice in your writing that bridges the gap between quantitative precision and qualitative richness. This voice acknowledges both the patterns in your numbers and the texture in your narratives. For example, instead of writing: *"A significant negative correlation was found between autonomy and burnout (r = -.42, p < .001). In interviews, participants described feeling constrained by regulations."* Try this: *"The significant negative relationship between autonomy and burnout (r = -.42, p < .001) came alive in interviews as participants described how regulatory constraints affected their daily work: 'I feel like a robot following a script rather than a professional making decisions' (Participant 3)."* This integrated voice takes practice (or some deep LLM prompting) but creates a more cohesive paper that embodies the spirit of mixed methods throughout. ### 6\. Take notes about integration While many researchers keep methodological notes during their study, few specifically write down detailed notes about the integration process. This misses a crucial opportunity. What I want to try in my next research project is to start an integration notepad (or [​Obsidian​](https://obsidian.md/?ref=lennartnacke.com) page) from the beginning of my project, specifically focused on connections, contradictions, and insights across methods. What I would record in this integration notepad: - My evolving thinking about how the methods complemented each other - Moments when survey results made me adjust my interview questions - Surprising connections I notice between datasets - Challenges and breakthroughs in mixing the data I feel like these notes will be incredibly valuable when writing my next paper's discussion section, because they’ll have rich material for explaining my integration process and insights. With detailed notes, you’ll also have critical reflexivity about when and why you prioritized certain data types at different points in your analysis. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 7\. Create a visual integration concept map Most mixed methods papers include a procedures diagram in the methods section, but then abandon visual elements when it comes to integration. But sometimes to explain findings, a visual integration concept map would be useful that is referenced at key points throughout your paper to remind readers how current information connects to your overall mixed methods approach. You can create a simple diagram with quantitative elements in blue, qualitative in green, and integration in purple to keep things effective. Then, I would reference it in: - The last paragraph of my introduction (show which method would address which question) - My methods section (show procedural details) - The beginning of my results (preview how results would be integrated) - My discussion (emphasize which insights came from which data source or their integration) Consistently referencing a concept map will make it easier to understand the mixed method approaches and make sense of how you integrate your data across methods. It’ll keep your paper readable and the integration narrative clear throughout. ### The goal is to present integrated research Want to know the real power behind these seven techniques? They help you create research that packs twice the punch! When you blend your methods thoughtfully, you'll discover insights you'd never find using just quantitative or just qualitative methods alone. The best part is moving past that old-school way of just plopping two separate studies next to each other. Instead, you'll write a story where each piece adds something special to the bigger picture⁠⁠. I hope my tips will help you tackle common challenges head-on. You'll have clear frameworks for combining your data now, dealing with findings that don't match up, keeping your writing voice consistent, and showing complex connections between your methods. I'll be honest. Working with mixed methods research can feel like learning to juggle while riding a bicycle. Trust me, I've been there. You need to master different research approaches and find creative ways to blend them together. On the bright side, with the right strategies (like the ones I just shared above), you can do more than just stick two different types of data together. You can create something special. Research that tells a fuller, richer story by combining the best of both worlds. And while it might seem tricky at first, I've seen many researchers (including my own students) grow from feeling overwhelmed to confidently producing amazing mixed methods papers. In the end, you won't just say you did mixed methods, but your paper will show it from start to finish⁠⁠. You've got this. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## **Paid Subscriber Bonus (CustomGPT)** _This post is for paying subscribers only._ ### How to Choose a Research Question that Genuinely Matters URL: https://lennartnacke.com/how-to-choose-a-good-research-question/ Last updated: 2025-05-28T14:11:49.000Z I remember those days vividly. Sitting in my advisor’s office, a whirlwind of half-formed ideas gurgling in my head, feeling the immense pressure to pick *the one*. The perfect topic. The killer research question that would define my graduate work, maybe even my career. No pressure. Does this sound familiar to you? Choosing a research question is arguably the most critical — and often the most paralyzing — step in any research project, let alone in the graduate school journey. It’s like being asked to choose your soulmate from a lineup of strangers. The stakes feel impossibly high. And despite AI tools, there is no Tinder for good research questions. Many otherwise highly talented students stumble here, not for lack of intelligence, but for lack of a clear process. Textbooks often jump straight into methodology, assuming you already know what you want to study. It’s like being handed a map without knowing your destination. Run, Forrest, run. Today’s guide is the conversation I wish I’d had early in my grad school career. I want to break down the process of finding, refining, and evaluating a research questions — minus the jargon and abstract theorizamazations (yes, totally a word). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Research questions are survival beacons Think of your research question as the survival beacon for your project. Without it, you’re just lost at sea, hoping to bump into something interesting. And hopefully it’s not a great white shark or Davy Jones’s kraken heading in your direction. But seriously, so much hinges on a good research question. It guides everything in a research project: - **Literature search.** It tells you what to read and, just as importantly, what to ignore (because let’s face it, you can’t read everything). - **Methodology.** It dictates whether you’ll be running experiments, conducting interviews, or spending years hanging out with maesters at the citadel (get a life, Samwell Tarly). - **Scope.** It defines the boundaries, keeping your project from expanding into a decade-long odyssey. - **Analysis.** It determines what data you need and how you’ll interpret it. - **Contribution.** It frames the unique insight you hope to offer the world. A clear research question guides your study design. A poorly formed question can lead to frustrating dead ends. It can feel like a soul-crushing waste of time. And we don’t want that. ### Where do good research questions come from? Contrary to the myth of the lone genius struck by sudden inspiration, great research questions rarely appear out of thin air. Yeah, sorry. They’re not delivered by owls like Hogwarts acceptance letters. Here’s where to look for that initial spark: #### 1\. Look into what bugs you Start with your personal puzzles. Think about concepts in your coursework that felt incomplete or contradictory. Reflect on lectures that made you think, “But what about…?” These moments of intellectual discomfort often signal fertile ground for research. If you’re not in grad school, consider practical problems from your professional experience. What challenges made you mutter under your breath? What processes seemed like they were designed by a committee of chaos goblins? The frustrations you’ve encountered firsthand can translate into meaningful research questions. Finally, pay attention to the patterns or anomalies you observe every day that genuinely spark your curiosity. Sometimes the most powerful questions start with a simple “Why…?” or “How…?” Don’t neglect how strong your natural curiosity can be for investigation. #### 2\. Going beyond the gaps in the literature Explore the knowledge deficit by reading review articles in your area of interest. Look for the boundaries between what’s known and what remains unexplored. These frontiers between knowledge and ignorance often present rich opportunities for investigation. But don’t stop there, find the problems driving the gap. Pay attention to inconsistencies and debates where different studies contradict each other. These tensions frequently signal productive areas for research, because conflicting findings suggest that something important is not yet fully understood there. Examine the limitations of previous work where authors explicitly state the shortcomings of their studies in the discussion section. Ask yourself if you can design research that addresses those limitations, building upon the foundation others have laid. Finally, consider classic gap spotting— identifying an area that hasn’t been studied yet, that’s usually the lowest hanging fruit (and the impact might be limited). While this is a valid starting point, we’ll explore how to go beyond merely filling gaps to asking transformative questions (and solving real problems). [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) #### 3\. Use the wisdom of crowds Consult with your advisor and mentors who usually have a broader view of the field and can guide you toward relevant and feasible topics. Be open about sharing your rough ideas with them, even the ones that make you cringe — their perspective can help turn even the roughest concepts into something valuable. Connect with peers and colleagues by discussing ideas, even half-baked ones. You get some hot new insights from such discussions and this helps you think in unexpected ways. Attend seminars and conferences with a curious mindset, ready to connect disjointed ideas. Reach out to key people who would be affected by your research. Conversations with practitioners, patients, users, or policymakers can reveal pressing real-world problems and by talking it through with them your work has practical relevance beyond just academia. Such perspectives often ground theoretical work in meaningful contexts. #### 4\. Turn theories into action Maybe apply theory by taking an existing theoretical framework into new contexts or problems. Take that road-tested Subaru into the Rocky Mountains (don’t try this at home, kids). You benefit from established tools while getting new insights. Explore opportunities for testing theory by designing studies that examine the assumptions or predictions of particular theoretical approaches. Verification like this strengthens the foundation of your field or shows you any needed modifications. Don’t hesitate to engage in questioning theory when established frameworks seem inadequate to explain phenomena you’re observing. Some of the most significant advances in knowledge come from researchers who recognized the limitations of existing paradigms and proposed alternatives. Challenge foundational assumptions. We call this “problematization.” And it can lead to particularly innovative and impactful research. ### The anatomy of great research questions Your research question is only as powerful as your selection criteria are real. Most choice frameworks aren’t rigid boxes, but they provide checklists to ensure your question has the key ingredients for success. Here are three popular ones: ### 1\. The FINER criteria have the widest scope - **Feasible**: Can you realistically complete it with the resources you have? Is the scope manageable? - **Interesting**: Is it interesting to you? (Crucial for those 2 AM research sessions!) Is it likely to be interesting to others? - **Novel**: Does it contribute something new? It doesn’t have to revolutionize your field, but it should add a piece to the puzzle. - **Ethical**: Can you conduct this research ethically? Will it get IRB approval? - **Relevant**: Does it matter? Is it relevant to scientific knowledge, practice, policy, or a specific community? ### 2\. PICO(T) focuses on quantitative studies - **Population/Problem**: Who are you studying? - **Intervention/Exposure**: What is the treatment or factor you’re investigating? - **Comparison**: What’s the alternative or control group? - **Outcome**: What are you measuring? - **Timeframe**: Over what period will the study take place? ### 3\. SPIDER framework for qualitative research - **Sample**: Who are you studying? - **Phenomenon of Interest**: What experience or process are you exploring? - **Design**: What research approach will you use? - **Evaluation**: What are the outcome measures? - **Research Type**: Qualitative or mixed methods? Quantitative research shows patterns. Qualitative research shows people. But your framework also determines your blind spots. If you check your questions against these specific, well-defined checklists, you have the best chance of assessing the viability of your question. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Problematize by challenging assumptions While finding gaps is valuable, a better approach is always problematization. Because as I have argued before, knowledge is infinite and therefore, there is always something missing. So, it’s better to question what’s there. It involves identifying and challenging the fundamental assumptions that prop up existing theories or concepts in your field. It’s intellectual rebellion with a purpose. Find those well-established theories or methodologies that everyone accepts without question. Think of yourself as academic mythbuster, but with more rigour and less explosions. Why do this? Because research that challenges core assumptions is often more influential. It’s like the difference between putting a Band-Aid on a paper cut versus discovering that humans can actually regrow limbs (if we just drink that delicious Nuka Cola in the wastelands). ### **So, how do you problematize?** 1. **Identify underlying assumptions**. Look for fundamental beliefs that researchers accept without question. For example, in game user research, many assume that more player engagement always leads to better outcomes. 2. **Challenge these assumptions**. Examine why these accepted truths might not hold up in all contexts. For instance, what if excessive engagement actually leads to negative player experiences or addiction? 3. **Develop an alternative**. Create a new perspective that addresses the limitations you’ve found. Consider that optimal engagement might vary based on player personalities, game genres, or cultural contexts. 4. **Formulate a new question**. Transform your critique into a concrete research question. Example: “How do different levels of engagement affect player wellbeing across various personality types and gaming contexts?” This approach is riskier, as it might challenge established ideas (and reviewers!). But the potential payoff — truly original research — can be immense. ### Final check-ups Before you fully commit, run your refined question through these final filters: **Impact** - Who benefits from this research? - What concrete changes could it create? **Feasibility** - Do you have the necessary time and resources? - Are your skills sufficient for this project? **Ethics** - Is participant protection ensured? - Are data handling procedures appropriate? ### How research questions guide methodological choices Your research question intrinsically links to your methodology: - Questions about experiences often imply qualitative methods. - Questions about effects or relationships usually point towards quantitative methods. - Questions about design might suggest design-based research. - Questions exploring both “what” and “why” may lead to mixed-methods approaches. Your research question and methodology must work together. The methods you choose should be specifically designed to answer your research question effectively. Strong alignment between these elements not only makes your research more coherent but also strengthens the validity of your findings. ### Guidelines for better research questions Choosing a research question is rarely a single “Aha!” moment but an iterative journey of exploration, polishing, and critical self-assessment. 1. **Embrace the mess**. Let your ideas evolve organically through exploration and learn from dead ends. 2. **Talk it through**. Share your thoughts with others to gain new perspectives and refine your ideas. 3. **Be specific**. Focus your research question until it becomes clear and actionable. 4. **Think impact**. Consider how your research will make a meaningful difference in your field. 5. **Be realistic**. Choose a scope that matches your available time and resources. 6. **Stay curious**. Follow your genuine interests to maintain motivation throughout your research journey. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Resource Here is a checklist for paid subscribers to help you work through identifying a research question. _This post is for paying subscribers only._ ### Academic Reviewers Can't Actually Tell When AI Writes Research Papers URL: https://lennartnacke.com/academic-reviewers-cant-tell-when-ai-writes-research-papers/ Last updated: 2025-05-28T14:34:47.000Z Another week in the books, my friend. One of the things I'm wondering more and more about every day is what using generative AI will do to the next generation of my students. Yes, I'm on the train of people who love and embrace it, but I'm also using these AI tools with decades of writing experience. And I know that sometimes, I need to write without it and let my ideas flow to help me think. Because as I often said on social media, *writing is thinking* and helps me percolate ideas in my head. Sometimes that's needed and knowing when you need it, is becoming an increasingly unfair advantage for academics these days. But I'm not one to dismiss AI tools right away and so we studied them in our research group and published a paper about our findings. And that's what I want to talk to you about today. The increasingly relevant question of whether or not we can spot AI writing. Here's the details. ### Academic AI aversion: What it means for researchers Remember when you were in high school and teachers would run your papers through some plagiarism detectors like TurnItIn with the steely glint of Sherlock Holmes, who just knew you copied that paragraph from Wikipedia? Well, academia has a new boogeyman, and its name is ChatGPT. [​One of our recent studies​](https://doi.org/10.1016/j.chbah.2024.100095?ref=lennartnacke.com) shows something that should make us all reconsider our confidence in spotting AI-generated content: academic reviewers —you know, those gatekeepers of scholarly publishing with decades of specialized expertise (who go under the pseudonym of reviewer 2 in several memes)—are terrible at identifying AI-written research. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Like, truly awful at it. As in coin-flip levels of accuracy. Shocker. > Frustrated by academic bias against AI tools? > > If you’re using AI for writing, reviewers might be working against you. > > But most reviewers can't tell if your research paper was written by AI. > > (This changes how we think about academic writing) > > Here's what my research team… [pic.twitter.com/GpsDQpnO53](https://t.co/GpsDQpnO53?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [November 5, 2024](https://twitter.com/acagamic/status/1853867216581939254?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) Our recent journal paper, [​The great AI witch hunt: Reviewers' perception and (Mis)conception of generative AI in research writing, published in the journal Computers in Human Behavior: Artificial Humans​](https://doi.org/10.1016/j.chbah.2024.100095?ref=lennartnacke.com), goes full-on honey badger into this embarrassing reality. As someone who regularly uses AI writing tools in all of my writing (yes, I'm admitting to it publicly), this study was important to do. And my grad students, most of all first author Hilda Hadan, and postdocs did an amazing job with this early study on how AI shapes academia. Time to bake why this matters into our brains. It's not just essential new evidence for researchers worried about being accused of cheating, but for all of us struggling to publish in a brave new world increasingly shaped by generative AI for everything we read (this article included). ### Our experiment was a clever little AI trap We set an elegant trap. We recruited 17 experienced peer reviewers from top human-computer interaction (HCI) conferences (like CHI) and presented them with research snippets in three flavours: 1. Original: Written entirely by human researchers 2. Paraphrased: Human text rewritten by AI (Google Gemini) 3. Generated: Created entirely by AI based on human reference text ![Prompt description with annotations.](https://lennartnacke.com/content/images/2025/03/AI-Writing-Research-Paper.webp) Example prompt used for creating a generated snippet from the paraphrased text, and the manuscript's introduction and conclusion sections. We detail the exact prompts we used for Gemini [​in our paper​](https://doi.org/10.1016/j.chbah.2024.100095?ref=lennartnacke.com), but here is a taster of the prompt we used to generate a snippet: Each reviewer read multiple snippets and had to judge: Was this written by a human or AI? They also rated the snippets on perceived honesty, clarity, accuracy, and other qualities. ![A flowchart showing the sequencing of our study.](https://lennartnacke.com/content/images/2025/03/Editing-Academic-Reviewers.webp) Our experimental study design. The results? The academic reviewers were essentially guessing. They couldn't reliably tell the difference between AI-generated text and human writing. Bet you didn't see that one coming from a mile away. *Specifically, our reviewers:* - Rated original human-written snippets and AI-generated snippets as equally likely to involve AI (median score of 5 out of 10 for both!) - Curiously, rated AI-paraphrased snippets as less likely to be AI-generated (median score of 2) - Showed no significant difference in how they judged the quality of research across the three content types Let that sink in. The very people who we might expect to be most sensitive to AI intrusion into their domain couldn't tell when AI was doing the writing - and when they thought they could tell, they were often wrong. Talk about generative AI going full Solid Snake on research fields. 🙃 [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Like finding a unicorn in your cereal bowl Here’s where it gets even more interesting, I think. Unlike previous studies showing “[​algorithm aversion​](https://onlinelibrary.wiley.com/doi/10.1002/bdm.2155?ref=lennartnacke.com)” (where people distrust AI outputs even when they’re objectively better), our sample of academic reviewers showed neither aversion nor appreciation. Their judgments of research quality remained consistent regardless of perceived AI involvement. In other words, they weren’t downgrading papers just because they thought they detected AI’s fingerprints. Let that little nugget of insight find a cozy spot in your brain. “I do think it was written by a human with good language skills,” commented Reviewer 14 about a snippet that was actually AI-paraphrased. How about that? We found that reviewers who had greater expertise in the relevant field and more familiarity with AI tools consistently rated snippets higher on honesty, clarity, and persuasiveness. And this was regardless of whether humans or AI wrote them. This suggests that rather than being blinded by bias, these reviewers focused on the substance of the research. That’s like discovering your toast landed butter-side up. I certainly was surprised. ### Contradictory clues What made reviewers suspect AI involvement? Their responses were fascinatingly contradictory: #### **For sentence structure** - 27% said AI produces incoherent logic and phrasing - But 7% said AI structures sentences better than humans #### **For sentence length** - 13% claimed AI tends to write convoluted, long sentences - But 3% attributed convoluted, long sentences to human writers #### **For language use** - 21% said AI uses unusual language choices - 10% associated plain, natural language with human authors - But some claimed the exact opposite patterns In essence, reviewers were using completely contradictory criteria to flag “AI writing,” revealing their inability to distinguish it systematically. You might as well play darts blindfolded. One reviewer confidently declared: “AI tends to construct sentences that often have a ‘do-ing’ in the second half.” Another was sure that “The sentences are too well-structured to be human-written.” And while I agree with some of those notions, they are not perfect determinants. I feel we’re all just confused pigeons running a committee meeting when it comes to strategies for spotchecking AI. Sadly, it’s become so good. These pervasive conflicting beliefs created what we call *contradictory perceptions*, where the same writing feature might be attributed to AI by one reviewer and to humans by another. Eenie meenie miney moe, just draw a fish and call it Joe. ### The power of the human touch Despite the inconsistencies, there was one area where reviewers seemed to agree: they valued what they called “the human touch” in research writing. What’s that, you ask? Those subjective expressions of personal perspectives that make academic writing feel more than just a clinical presentation of facts, Dr. Fluffy McThunderpants. “There is a humble and stumble feel to the writing, which makes it feel like human,” noted Reviewer 17\. (I’ve definitely done my fair share of stumbling when I write and especially when I embellish AI drafts like this one with my voice.) This emphasizes something important about AI writing in academia: While AI can improve structure and readability, it often lacks the emotional resonance and subjective insights that human researchers bring to their work. The more AI writing we read, the more we crave that stuff. The AI snippets were described as well-structured but occasionally monotonous and devoid of personality. So, everyone loves it when you sound like a confused penguin trying to order a pizza in Italian, I guess. ### So what does this mean for you and me? The implications of our study are significant for both researchers and reviewers: #### For researchers 1. **Stop stressing about disclosure**. Researchers often fear that disclosing AI use will bias reviewers against their work. This study suggests that fear is largely unfounded—reviewers can’t reliably detect AI writing anyway, and their quality judgments remain consistent regardless. Based on our sample at least. 2. **Use AI to enhance, not replace**. AI tools excel at creating well-structured, readable sentences—precisely what many researchers (especially non-native English speakers) struggle with. Let AI help with the mechanics of writing while you focus on the intellectual substance. Cook that intellectual stew, but do it with your own cranium. 3. **Maintain your voice**. The *human touch* matters. Use AI to polish your writing, but guarantee that your unique insights, passion, and perspectives roll into that final text. As a researcher, you should remain in your role as the primary intellectual driver of your own work. 4. **Disclose transparently**. Given that reviewers can’t reliably detect AI use anyway, there’s little to lose and much credibility to gain by simply acknowledging when you’ve used AI tools to assist your writing. Like I did multiple times so far in this article. #### For reviewers 1. **Focus on research merit**. Stop trying to play the AI detective, Sherlock—you’re probably wrong anyway. Instead, evaluate manuscripts based on their scientific validity, methodological rigour, and contributions to the field. 2. **Check your biases**. Many reviewers held contradictory beliefs about AI writing characteristics, suggesting deep-seated biases rather than actual pattern recognition. Be aware of these biases when reviewing. And—even better—let it go, let it goooo. 3. **Remember that AI helps level the playing field**. Non-native English speakers and early-career researchers benefit tremendously from AI writing assistance. It’s good thing. Don’t penalize work just because it’s well-written or follows conventional academic structures. Most of academic writing is too boring and super structured as it is. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### The future of academic publishing Our study points to a future where the lines between AI and human academic writing continue to blur. Rather than fighting this inevitable shift, academia might better embrace transparency while focusing on what truly matters: the **quality** and **integrity** of the research itself. We advocate for a balanced approach: *Researchers should make use of GenAI as a tool for enhancing readability and reorganizing research knowledge. However, they should remain in their role as the primary intellectual drivers of their work.* So, to drive my point home, in our paper, we strongly recommend that you as researchers: 1. Openly disclose your use of GenAI 2. Carefully review and fact-check AI-augmented content 3. Preserve the *human touch* in your writing 4. Use GenAI to enhance—not replace—your critical thinking Similarly, reviewers just have to stop speculating about AI involvement and focus exclusively on scientific merit, coherence, clarity, and effective communication. Wouldn’t that be nice if we get there soon? (Another study worth doing is how many reviewers have actually farmed out their reviews to AI completely already and the potentials of doing that.) ### The irony of it all There’s a delicious irony in all this: academic researchers who fear being caught using AI tools are worried about reviewers who can’t actually detect AI use reliably. It’s like fearing a polygraph test administered by someone who can’t tell which end of the machine to plug in. As someone who has both submitted to and reviewed for many academic venues, I find our study both validating and concerning. AI writing tools are incredibly helpful for structuring thoughts and enhancing readability—particularly for non-native English speakers who have smart ideas but struggle with linguistic barriers. [​I’ve advocated for this before​](https://dl.acm.org/doi/10.1145/3573382.3616030?ref=lennartnacke.com). Yet the current climate in academia often treats AI use as cheating rather than as an assistive technology. And there is too much hostility about it online. Our study suggests we might be overthinking the whole thing. If expert reviewers can’t tell the difference anyway, perhaps we should focus more on the quality of ideas and research rather than policing the tools used to communicate them. ### Beyond academic implications… While our study focused on academic publishing, its findings have implications far beyond the ivory towers that are catching dust too quickly. As AI writing becomes increasingly frequent across all domains, our collective inability to distinguish it from human writing raises several crucial questions: - If expert reviewers can’t tell the difference, what hope do the rest of us have? - Does it actually matter who (or what) did the writing if the content is valuable? - How might this change how we think about authorship and intellectual contribution? Let that bounce around in your skull for a bit. We also noted that reviewers appreciated well-structured snippets with good readability but were disappointed when content lacked logical progression or supporting evidence. This suggests that AI’s strength lies in presentation while humans remain essential for critical thinking and insight. Take that, o1 and DeepSeek. We know you’re just predicting stuff anyways. 😁 (But I do recommend to keep your finger on the pulse by using the latest AI models, always.) ### Where do we go from here? So, heading into a better future, we suggest these 4 easy steps that different stakeholders can take right now: 1. **Academic institutions** should provide ethical guidelines for GenAI use in research. And like now. Not in a year from now. 2. **Publishing venues** should update submission guidelines and templates to accommodate transparent AI disclosure without penalization. 3. **Reviewers** need better training on AI capabilities and limitations. 4. The **research culture** should shift toward valuing thorough, impactful work over publication quantity. Especially for publishing venues such as journals or conferences, there should be standardized, transparent, and convenient rules on what exactly to disclose. For example, in our paper, we disclosed the AI tools, specific models, the prompt used for language polishing, and even the non-AI tools used for analyzing data and creating graphs (so it’s clear what’s AI enhanced what’s not). Perhaps most importantly, we advocate for a research culture that values thorough, **impactful work** while acknowledging the role of new technology. We know we need to recognize both the benefits and limitations of AI in academic communication. ### One big question So where does this leave us? If expert reviewers with years of experience in specialized fields can’t reliably identify AI-written content, what does that tell us about the nature of AI writing itself? Has it already reached a point of indistinguishability from human writing? Or are humans simply not as unique in their writing patterns as we’d like to believe? Reviewers appreciated the personal insights of human authors in research papers. And I believe that intangible quality that we call the human touch will remain essential. Maybe that’s what we should focus on preserving—not fighting a losing battle against AI assistance, but confirming that the human elements of curiosity, passion, and creative insight remain at the heart of our work. In the meantime, if you’re an academic researcher worrying about being caught using AI tools, our study suggests you can probably relax a little. The witch hunters, it turns out, are having trouble identifying the actual witches. And more importantly, they don’t seem to care all that much anyway once they get past their initial biases. Better times ahead there, Salem Saberhagen. So go ahead and use that AI writing assistant and declare it. Just remember to bring your human expertise, critical thinking, and unique perspective to the table. That’s what really matters in the end. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. *(BTW: This newsletter was drafted with an elaborate Claude 3.7 Sonnet prompt and infused with a lot of human touch before publication. This is a technique I use for all of my newsletters.)* P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Generative AI checklist for authors and reviewers Here is a checklist and a cheatsheet to help you work with generative AI in academic publishing and editing. [You may also want to check out this podcast episode about the paper.](https://creators.spotify.com/pod/show/reviewer-credits/episodes/The-Great-AI-Witchhunt-Reviewers-Perception-e2rqffo/a-ablne17?ref=lennartnacke.com) _This post is for paying subscribers only._ ### The Silent Threat of Citation Hacking to Academic Integrity URL: https://lennartnacke.com/the-silent-threat-of-citation-hacking-to-academic-integrity/ Last updated: 2025-05-28T14:35:23.000Z I still remember the email the email like it was yesterday. “Congratulations, your paper has been accepted pending minor revisions.” After months of hard research and writing, the validation felt incredible—until I scrolled down to the reviewer comments. “Please consider including references to the following papers…” While I don’t mind including a good pointer to relevant related research that I might have missed, something felt off about this request. Only one of the suggested references was directly related to the research in the paper. The rest of them: Not relevant. And all of them had the same author in the author list. The air was thick with the aroma of scientific shenanigans. Alas, I had just encountered citation hacking, and I wasn’t alone. [Existing evidence points to nearly 16% of scientists engaging in reference list manipulation](https://www.biorxiv.org/content/10.1101/2020.08.12.248369v1?ref=lennartnacke.com) to some degree. What began as my personal frustration has become an epidemic threatening the very foundation of scientific discourse. And I’m not having it. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## What exactly is citation hacking? Citation hacking goes beyond the occasional self-citation. (Hey, we’ve all been a lonely postdoc, who knew they were doing good work in an area where not many people are paying attention. It’s ok to give yourself a little lift as long as it’s relevant citations to your more obscure work.) But citation hacking is different. And it’s systematic. It’s the deliberate manipulation of academic citations to artificially inflate impact metrics, which are often highly relevant to career advancement. This manipulation takes several forms: 1. **Self-citation inflation.** Excessively citing your own work beyond what’s relevant. Germans have a word for what you are trying to be: *Platzhirsch*. (Meaning: a dominant stag in its territory, or as the Americans say: Top Dog. Woof.) 2. **Citation cartels.** Groups of researchers who agree to cite each other’s papers regardless of relevance. Sure, they ain’t quite Sicarios, but still pretty f%$cking scary for junior researchers. 3. **Coercive citation.** Reviewers or editors demanding their work be cited as a condition for publication. We’ve all seen the [famous sentence “As strongly requested by the reviewers, here we cite some references \[35–47\] although they are completely irrelevant to the present work.” from a retracted article](https://www.sciencedirect.com/science/article/pii/S0360319924043957?via%3Dihub&ref=lennartnacke.com) (and [I wrote about it on LinkedIn](https://www.linkedin.com/posts/nacke%5Fphd-integrity-research-activity-7305555405546442752-A2an?utm%5Fsource=share&utm%5Fmedium=member%5Fdesktop&rcm=ACoAAAAvEqsBay3l0zqX8vJl-roEx13RonVLLLw)). This is a real threat that is too common. 4. **Metadata manipulation.** The newest threat—[inserting references in metadata](https://theconversation.com/when-scientific-citations-go-rogue-uncovering-sneaked-references-233858?ref=lennartnacke.com) that don’t appear in the actual text. But why does this happen? The academic reward system itself creates perverse incentives. When scientists face evaluation based on simple citation counts rather than quality or impact of their work, [some inevitably game the system](https://www.nature.com/nature-index/news/signs-of-citation-hacking-flagged-in-scientific-papers?ref=lennartnacke.com). ## How to handle citation requests ### Challenge coercive citation requests That day when I received those reviewer comments, I panicked. I mean I was a postdoc. My first instinct was to comply. Just add the citations and get it published. And I did and many people do this every day because it’s frictionless and gets your article published. But that’s exactly what perpetuates the problem. So, the next time I faced such a request, I was a bit more senior and I took a different approach: 1. **Document everything**. Save all communications showing citation demands. I didn’t need it, but you might. 2. **Contact the handling editor (or editor-in-chief) directly**. I wrote a detailed explanation of why the suggested citations weren’t relevant, providing specific reasons rather than general objections. 3. **Propose alternatives**. I also suggested more relevant citations from a few authors that would strengthen the paper. The result? The handling editor overrode the reviewer’s demands (and in my heart, I hope they eventually blacklisted them if this kept happening). My experience taught me that editors often stand with authors who provide well-reasoned explanations against citation coercion. So, you don’t have to give in to such requests. ### Maintain your citation integrity When writing your own papers, apply what I call the “stranger test” to every citation: **“Would I cite this paper if it was written by someone I didn’t know?”** This simple question cuts through potential bias, especially for self-citations. Self-citation itself isn’t inherently problematic, I believe, because your current research likely builds on your previous work. That’s normal and nothing to be afraid of. The key distinction lies in relevance and proportion. You want to keep things balanced. I’ve found that keeping self-citations under 20% of total references provides a good rule of thumb. When I go beyond that threshold, I reconsider each citation critically. Is it really necessary or am I just making myself feel good here? ## Systemic safeguards for journal editors Your citation metrics may look great until your realize they are artificially inflated. Don’t get a wake-up call about this too late and then face serious integrity challenges for your entire journal. Here is my suggestions of some safeguards you can implement right away if you’re an editor-in-chief for a scientific journal: ### Effective and explicit policies Create explicit policies prohibiting citation manipulation. The most effective policies do this: 1. **Define manipulation clearly**. Specify that it includes artificially inflating citations for benefits other than situating the work in the literature. For example, a sentence like [“practices aimed at artificially boosting citation numbers for individual benefit.”](https://brieflands.com/knowledgebase/citation%5Fmanipulation?ref=lennartnacke.com) 2. **Differentiate legitimate from illegitimate practices**. Acknowledge that self-citation may be necessary to avoid self-plagiarism or provide research context, but establish clear boundaries. 3. **Implement zero-tolerance for coercion**. When the editor-in-chief discovers a reviewer demanding citation of their papers, they should immediately blacklist the reviewer. You want to send a powerful message with a decisive action to establish the reputation of your journal. (Realistically, I don’t see this happening often, as we face a crisis of qualified reviewers as it is.) ### Practical monitoring techniques [Excellent guidelines for preventing coercive citations exist.](https://pmc.ncbi.nlm.nih.gov/articles/PMC6748764/?ref=lennartnacke.com) I liked the idea of a checkbox that says “I have requested citation(s) to my own research” to quickly identify reviewers, who request citations to their work and check whether they are legitimate. Another idea is to create a simple algorithm that flags any paper receiving more than three citations from a single reviewer. I think AI could easily help with this, too. Other effective checks for editor include: 1. **Screen for citation anomalies between submission versions**. Look for sudden increases in citations to particular authors or journals after a review. 2. **Cross-check reviewer-suggested citations against the reviewer’s publication history**. This simple step shows you potentially self-serving citation requests. 3. **Implement citation analysis tools** that detect unusual citation patterns. These tools should flag statistically improbable citation distributions that warrant further investigation. (Given how much money publishers make, these tools should already exist.) ### Verification that can’t be gamed Require justification for suggested citations during review. When reviewers recommend additional citations, ask them to explain specifically how each citation strengthens the manuscript. This single practice dramatically reduces frivolous citation requests. Verify that citations added during revisions actually support the claims they’re attached to. Random spot-checking of citations catches many instances of inappropriate addition. ## What’s really at stake The real measure of research impact isn’t citation count. It’s how your work advances knowledge. Citation hacking distorts not only metrics but the very nature of scientific progress. It’s clear that they are a result of the career system we are locked into, but once you stop chasing citations, you’ll realize that you’ll have all this new headspace to ask the questions that really matter to your field. Not having this space to find more creative solutions is the deeper cost of our citation obsession. And, I’m sure, finding those answers why you became a scientist in the first place. We need systemic change in how we evaluate research quality. The current overemphasis on citation metrics creates conditions where citation hacking thrives. Journal editors and tenure committees can lead this change by: 1. Publishing editorials that publicly condemn citation manipulation 2. Highlighting exemplary papers with focused, relevant citation practices 3. Implementing alternative impact metrics beyond raw citation counts ### Break the cycle When I now mentor young researchers, I discuss with them that citation integrity is essential to our research practice. Education is our most powerful preventative tool. [Research from Indonesia](https://www.semanticscholar.org/paper/EFL-Student-Teachers-and-Lecturers%E2%80%99-Challenges-and-Prasetyarini-Safitri/79da1997368243a8365a9db5fd3b2af2d3d681e8?ref=lennartnacke.com) found that training in citation practices significantly reduced manipulation among students. The same principles apply to established researchers. Citation hacking threatens the integrity of academic discourse. But we can combat it through vigilance and systemic change. As researchers, we must stand firm against coercive practices. Journal editors need to implement robust policies and verification mechanisms. And honestly, academic publishers in their often infinite greed, should be throwing money at this. Beyond these specific strategies, the academic community must reconsider how it measures and rewards research impact. When tenure committees stop obsessing over citation counts and focus on genuine knowledge advancement, we remove the incentives that drive citation manipulation in the first place. But this is hard work, because there are no easy metrics for assessment and I know senior academics are overworked as it is. What citation practices have you witnessed in your field? Have you encountered citation hacking personally? Let me know. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Video and Cheat Sheet Here is the video of my keynote talk “Who is even a researcher in the post-AI world?” at the Litmaps conference where I talk about the challenges of doing ethical research work in the post-AI world. I am also attaching a PDF cheat sheet as a defense guide for researchers facing citation hacking. _This post is for paying subscribers only._ ### How to Make Your Papers Pop With Good Framing URL: https://lennartnacke.com/how-to-make-your-papers-pop-with-good-framing/ Last updated: 2025-05-28T14:36:07.000Z Remember that awkward moment at a dinner party when someone asks what your research is about, and you launch into a 15-minute monologue that leaves everyone checking their phones? Been there, done that. We’ve ALL totally been in that boat. Let’s face it: even the smartest ideas fall flat if you can’t package them right. I discovered this the hard way when my meticulously researched paper on physiological measures of fun in video games got a pass from the top conference of my field. I was clueless about framing back then, totally in the dark. And I failed to tell them why they should care about my work in the first place. What followed was my crash course in the hidden art of academic framing — the strategic positioning that transforms technical research into meaningful contributions. You might think this is just some advice about better writing, but let me tell you that this is so much more than that. It’s basically the make-or-break strategy that decides if people actually pay attention to your work or if it just sits there gathering virtual cobwebs. ### Why smart research gets ignored Think of academic framing like the difference between handing someone all the ingredients for a yummy taco versus serving them a perfectly assembled one ready to eat. Having all the right stuff is one thing, but putting it together in a way that makes people’s mouths water is a completely different experience. Most early career researchers make the same critical mistake: They assume their research speaks for itself. It doesn’t. When I reject papers for the journals and conferences I’m reviewing for, I most often and most immediately notice framing failures of the work: - Unclear how this extends existing theory - Contribution not sufficiently articulated - Fails to engage with relevant literature Let’s not sugarcoat it: In academia, what you study often matters less than how you frame what you studied. Acceptance into a top venue often comes down to how you package the science. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Framing theory basics Framing isn’t as complicated as it sounds. It all started with the sociologist Goffman and his work on how we make sense of the world through mental shortcuts shaped by our culture. In the academic world, framing is basically both how you think about your research and how you sell it to others. Snow and Benford broke it down into three simple parts that are useful when you’re trying to structure your argument: 1. **Diagnostic framing.** Pointing out what’s missing or messed up in our current knowledge 2. **Prognostic framing.** Coming up with some creative ways to fix these problems 3. **Motivational framing.** Articulating why your work matters and what others should do about it This framework is basically like the hourglass shape you see in scientific papers. You know, where introductions start with all the big-picture stuff before zooming in on your specific research questions, and then you zoom back out to talk about why it all matters. Getting good at this structure shows people you’ve got your academic game together. *Copy and paste these ChatGPT prompts with your title and abstract text to try develop different framing for your article:* _This post is for paying subscribers only._ ### How AI is Changing What it Means to be a Researcher URL: https://lennartnacke.com/how-ai-is-changing-what-it-means-to-be-a-researcher/ Last updated: 2025-05-28T14:33:06.000Z Last Wednesday, as I slowly sipped what was probably my third Latté of the day (because f%$k you liquid calories), Google casually shattered my professional identity by [releasing their co-scientist](https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/?ref=lennartnacke.com) multi-agent AI system. I’ve demonstrated how a well-prompted AI system can generate a comprehensive literature review in my field in about 20 minutes before. But with the release of Google’s Co-Scientist and other multi-agent AI research tools, my very job is facing the instant urge to evolve or mutate significantly in the near future. David Cronenberg would have loved to write the script to this reality I’m facing. With the battle scars of a reduced sabbatical, delayed tenure review, some research papers that nearly broke me, and enough rejection letters to wallpaper my entire office, I sat there wondering if I'd become the academic equivalent of a horse-drawn carriage in the age of Teslas. Real museum stuff. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. This wasn't just some technological anxiety. I am facing an existential crisis packaged in a sleek user interface. And, honestly, I don’t think I’m the only one. Caffeine clearly isn’t the answer to this one, because about a day later another notification popped up about yet another AI breakthrough. As if to underscore this technological anxiety, Elon’s xAI is pushing us further into uncharted territory with [Grok 3](https://x.com/i/grok?ref=lennartnacke.com), making the first public venture into generation 3 AI. Their strategy of “bigger is better,” backed by what they claim is the world's largest computer cluster, has yielded the highest benchmark scores we’ve seen from any base model. Not to be outdone, [Claude 3.7 Sonnet’s release](https://www.anthropic.com/news/claude-3-7-sonnet?ref=lennartnacke.com) a couple of days ago shows even more remarkable improvements (and [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview?ref=lennartnacke.com)), matching Grok 3’s capabilities while offering different strengths. Meanwhile, OpenAI’s unreleased o3 lurks on the horizon, promising to be another third generation powerhouse. I’m honestly feeling a little exhausted this week by this new. Academia will experience a tectonic shift because more companies will launch models at this unprecedented scale (and multi-agent scientist systems will likely do the bulk of academic writing in less than a year from now). This wasn't just some technological anxiety. I am facing an existential crisis packaged in a sleek user interface. And, honestly, I don’t think I’m the only one. Caffeine clearly isn’t the answer to this one, because about a day later another notification popped up about yet another AI breakthrough. As if to underscore this technological anxiety, Elon’s xAI is pushing us further into uncharted territory with [Grok 3](https://x.com/i/grok?ref=lennartnacke.com), making the first public venture into generation 3 AI. Their strategy of “bigger is better,” backed by what they claim is the world's largest computer cluster, has yielded the highest benchmark scores we’ve seen from any base model. Not to be outdone, [Claude 3.7 Sonnet’s release](https://www.anthropic.com/news/claude-3-7-sonnet?ref=lennartnacke.com) a couple of days ago shows even more remarkable improvements (and [Claude Code](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview?ref=lennartnacke.com)), matching Grok 3’s capabilities while offering different strengths. Meanwhile, OpenAI’s unreleased o3 lurks on the horizon, promising to be another third generation powerhouse. I’m honestly feeling a little exhausted this week by this new. Academia will experience a tectonic shift because more companies will launch models at this unprecedented scale (and multi-agent scientist systems will likely do the bulk of academic writing in less than a year from now). ## An academic flex that no longer impresses anyone Remember when knowing obscure citations was our academic superpower? When students would look at us in awe as we casually referenced that hard-to-find but crucial paper from 1976? Yeah, those days are disappearing faster than free wine and cheese at conference receptions. For decades, our worth as academics was measured by our ability to find rare sources, memorize key passages, and weave disparate ideas together through careful analysis. The knowledge we cultivated took years of dedication and countless hours in libraries that smelled like dust and academic desperation. Today, the threshold for producing credible academic content has dropped so low that my neighbor’s teenager could use AI to write a surprisingly decent analysis of power dynamics in medieval French literature. What is even happening? The quality of AI-generated academic content is improving at a pace that’s frankly a little terrifying. Most papers and reviews I read these days are at least 80% AI-supported (it’s a gut feel, but I feel I can judge this well enough). A well-designed prompt together with semantic search systems can produce a literature review that would earn a solid B+ in most graduate seminars. And that B+ is rapidly trending toward an A-. ## Intellectual opposition is my favourite way to use AI though Here's the surprising turn in this academic nightmare though: While many AI companies market AI tools as stuff that makes us better, faster, and more efficient academics, that’s whole efficiency and automation angle is quickly becoming outdated. I think AI’s most valuable function might not be speeding up or replacing our writing (although that’s a given these days), but challenging our own thinking. I discovered this accidentally when, in a fit of petty revenge against the machines, I asked an ChatGPT o1 to critique my latest research idea. I was secretly hoping for lame-ass criticism I could smugly dismiss. But what do you know, I got intellectual confrontation that no colleague would dare to offer me. And I liked it. It really is like summoning a fearless colleague who doesn’t worry about research politics or hurting my feelings to just get me in the discomfort zone, where I do better thinking. Kudos to you, ChattieG. And that’s when I realized: the real power of AI isn’t replacing academics, it’s disagreeing with us in ways that make our work stronger. It’s being a servant and a challenger that never gets tired of our nonsense. Kind of cool to have this readily available. ## Three ways to use intellectual confrontation with AI Here are 3 simple strategies (and AI prompts) how we can use AI’s power to challenge and strengthen our academic thinking. The following methods have emerged from countless hours of intellectual sparring with various AI models, each offering unique perspectives on research problems.Take AI from a mere writing assistant to a valuable intellectual opponent. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 1\. Force AI to argue against your hypothesis Let the LLM play devil’s advocate with your cherished research assumptions. Get that intellectual stimulation on demand. **Prompt:** `Take my following research hypothesis and make the strongest possible counterargument, citing potential evidence and theoretical frameworks that challenge my position.` The counterarguments generated might point our your blind spots you’ve developed over years of researching the same topic. *(More for paid subscribers below.)* P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). _This post is for paying subscribers only._ ### How Asking Better Questions Changes Everything URL: https://lennartnacke.com/get-supervisor-answers-guidance-phd-gradstudents/ Last updated: 2025-05-28T14:40:43.000Z Another email notification hits your professor’s inbox. It’s you. Again. With another vague request for “feedback” on your 75-page draft that you sent at 11:58 PM. The one where you asked, "Thoughts?” We've all been that student. I certainly was at one point. But after spending too many hours waiting for feedback or in limbo about some specific research issues, I realized something had to change. Here are the strategies that improved my academic relationships and might just preserve your supervisor’s sanity, too. *(CustomGPT for paid subscribers at the end.)* ## Be specific about what you need Have you ever observed the difference between asking your partner ”Where should we eat?” versus “Italian or Thai: which sounds better to you tonight, honey?” One question paralyzes with options; the other invites actual decision-making. Instead, try asking your supervisor: “Could you review pages 12-15 where I discuss my methodology? I'm particularly concerned about whether my sample size justification is strong enough.” The contrast is striking. As a PhD supervisor myself now for several years now, I freeze when students just ask for general feedback about a draft. I have a lot of students and very little time. Don’t text someone “wyd” and expect to get back anything but “nm.” Technically that’s a response, functionally it’s useless. I urge my students to maintain a mental checklist before sending anything: - Am I asking about a particular aspect (methodology, theoretical framework)? - Have I identified the specific section/argument I need feedback on? - Have I explained why I’m uncertain about this specific element? When you ask specific questions, you: - Make it possible for them to give focused, useful feedback - Demonstrate critical thinking about your own work - Show respect for your supervisor’s limited time Instead of a vague hour-long meeting filled with generalizations, you'll have focused 15-minute discussions. Your supervisor will respond within hours instead of days. Best of all, you'll receive three paragraphs of targeted feedback that actually moves your research forward. Not just vague comments. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Prepare before meetings The difference between prepared and unprepared grad students is like the difference between a carefully curated playlist and letting your Spotify on shuffle during a first date. One shows thought and consideration; the other is just hoping for to get to third base without putting any effort into getting to first. The same principle applies to advisor meetings. Coming unprepared wastes everyone’s time and signals a lack of investment in your own research journey. But showing up with a clear agenda and thoughtful questions? That's how you turn a routine check-in into a powerful trigger for your research progress. Before your next meeting: 1. Write down your questions in order of priority 2. Review your notes from previous meetings 3. Prepare visual aids if discussing any data 4. Bring a calendar to schedule follow-ups I started creating a one-page “meeting prep” document with three sections: - Specific questions (numbered and prioritized) - Progress since last meeting - Current challenges This structured approach doesn’t just save you time, it positively alters the dynamic of your supervisory relationship. When you consistently come prepared with clear objectives and organized thoughts, you transform from just another graduate student into a respected colleague-in-training. Your advisor will notice and appreciate this level of professionalism. ## Frame open-ended questions There's an art to asking questions that invite meaningful conversation rather than yes/no answers that kill dialogue faster than mentioning your dissertation at a frat party. This is where many graduate students stumble. They ask questions that can be dismissed with a simple checkbox answer rather than sparking intellectual discourse. The key is to frame questions that demonstrate both your engagement with the material and your desire to mine for more. Think of it as the difference between asking someone if they like coffee versus exploring their thoughts on how different roasting techniques affect flavour profiles. Instead of: “Is my literature review okay?” Try: “Which parts of my literature review could benefit from more critical analysis of competing theories?“ The first question can be dismissed with a grunt and a glance at the calendar, while the second invites thoughtful expertise and meaningful discussion. Some effective open-ended questions you can ask right now: - How might I strengthen the connection between my theoretical framework and methodology? - Which researchers in this area might challenge my approach, and how? - What alternative interpretations of these results should I consider? This shift in how you approach questions does more than improve the quality of your supervisor discussions, it will grow and show your intellectual maturity. I noticed this myself when my supervisors began treating me more like a peer researcher rather than just another student, who needed guidance. And it all came from learning to ask better questions. ## Build a relationship that works for you The way you frame questions shapes your academic relationship at its core. Thoughtful, well-phrased questions demonstrate respect for your supervisor’s time and expertise. You set the stage for a more collaborative relationship, where both parties feel valued. When you consistently show up prepared and engaged, your supervisor is more likely to reciprocate with increased investment in your success. I’ve found that as my questions improved, so did: - The frequency with which my supervisor initiated regular check-ins - My supervisor’s willingness to connect me with their network - My confidence in facing academic challenges - The quality of feedback I received Remember that your supervisor has likely supervised dozens of students before you. They can tell the difference between someone who’s putting in the effort and someone who’s expecting them to do the heavy lifting. Writing as an advisor now, it makes a world of a difference. So before you hit send on that next email or walk into that next meeting, ask yourself: “Is this question showing that I’ve done my part of the work?” If you’re consistently getting slow or no responses, the problem might not be your advisor. It might be how you’re asking your questions. Take my advice and transform your academic relationships to set yourself up for success. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Custom GPT and Prompt Bonus _This post is for paying subscribers only._ ### How I Mastered Literature Reviews (And You Can Too) URL: https://lennartnacke.com/how-i-mastered-literature-reviews-and-you-can-too/ Last updated: 2025-05-28T14:43:14.000Z > Today's issue is sponsored by [​Litmaps​](https://go.lennartnacke.com/litmaps?ref=lennartnacke.com) (get 30% off Litmaps Pro until the end of the month, with code **LENNART30**): Want to get a crash-course in the most essential and cutting-edge tools & methods for research? I'm teaming up with them for an exciting 2-day FREE event, [​The Future Ready Scholar Conference​](https://lu.ma/30oequm9?ref=lennartnacke.com). Learn how to produce high-quality research faster, while maintaining your academic integrity. [​Register here now!​](https://lu.ma/30oequm9?ref=lennartnacke.com) Bonus: you get to see my funky mug in a video livestream. > I'm also excited to invite you to an exclusive webinar hosted by my friend Ilya Shabanov at [Effortless Academic](https://effortlessacademic.com/ai-writing/?ref=lennartnacke.com). He has developed a fun, new method that fuses your academic notes with AI to create well-structured manuscripts. [Use my link to get a 20% discount on the webinar.](https://effortlessacademi.kit.com/products/effortless-writing-with-ai-fast-track-?promo=54E95F91DG&ref=lennartnacke.com) Every researcher knows the feeling: staring at a blank document, wondering how to find those perfect papers that will shape their work. After years of wrestling with databases and drowning in PDFs, I've finally discovered the method for conducting efficient literature reviews. Here's what I teach my students: ### Always use a multi-database strategy Think of academic databases like specialty stores. Sure, you could get everything at Costco, but sometimes you need that artisanal cheese (Kaltbach cave or bust) or that specific spice market (Safran any day). The same goes for research papers. **Here's my tried-and-tested database search engine combination:** - [**Google Scholar**](https://scholar.google.com/?ref=lennartnacke.com)**.** The OG academic search engine that catalogs academic articles from all fields (and also a bunch of non-peer reviewed junk that ‘looks’ academic). It serves as an excellent starting point though for research and includes a useful *"Cited by"* feature to track article citations. - [**PubMed**](https://pubmed.ncbi.nlm.nih.gov/?ref=lennartnacke.com). Excellent for health and medical research, particularly mental health topics (e.g., generalized anxiety disorder). It finds papers that might be missed in tech-focused databases and uses Medical Subject Headings (MeSH) for precise searching. - [IEEE Xplore](https://ieeexplore.ieee.org/Xplore/home.jsp?ref=lennartnacke.com) & [ACM Digital Library](https://dl.acm.org/?ref=lennartnacke.com). Essential for technology and VR research. These databases help you find papers on VR applications, game-based assessments, and technical methods. IEEE Xplore contains *IEEE VR* conference papers and related journals, while ACM includes major conferences like CHI (which is the major conference in my field) and VRST. - [Scopus](https://www.scopus.com/?ref=lennartnacke.com) or [Web of Science](https://www.webofscience.com/?ref=lennartnacke.com). These comprehensive databases catalog a broad spectrum of journals and conferences. They offer both a broad *overview of the field* and powerful tools for filtering and analyzing citations. They are also expensive AF and if your university is not shelling out the money for them, you are sh\*t out of luck. Always search multiple databases for your topic. Because no single database contains everything, using different sources helps you find the most relevant literature. For example, combining searches from Google Scholar and IEEE Xplore gives you complementary results. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### It's not what you search, it's how you search After countless hours of trial and error, I've developed a search strategy that actually works. Here's the framework: 1. **Break your topic into is components and synonyms.** Unpack your subject matter ("VR mental health assessment for GAD") into core concepts: *virtual reality*, *mental health*, *assessment*, *generalized anxiety disorder*. Then brainstorm synonyms and related terms (like *immersive*, *serious games*, *anxiety measurement*) to use in different search combinations. 2. **Master boolean search logic.** Create effective search strings by combining keywords with `AND`, `OR`, and `NOT`. For example: `("virtual reality" OR VR) AND (anxiety OR "mental health") AND assessment`. Using `AND` narrows your search by requiring all terms, while `OR` broadens it by including synonyms. This approach guarantees that every result includes all your key concepts. 3. **Use smart quotation marks strategically.** Use quotation marks to find papers containing specific phrases. For example, search `"game-based mental health assessment"` to find articles with that exact phrase. This filters out irrelevant results and focuses your search on precisely what you need. 4. **Use tactical filters.** Most databases offer powerful filtering options by publication year, study type, and subject area. For rapidly evolving fields, you may want to limit your search to the past 5–10 years to capture recent developments. You can usually polish up results by language or document type—such as review articles or conference papers. ### **Use advanced search parameters** ![Screenshot of the advanced search UI of Google Scholar.](https://lennartnacke.com/content/images/2025/02/Google-Scholar-Advanced-Search.webp) Advanced search parameters in Google Scholar. In Google Scholar’s [Advanced Search](https://guides.library.ucsc.edu/c.php?g=745384&p=5361954&ref=lennartnacke.com), you can specify words that *must* appear or *exclude* words. In PubMed, use field tags like `[TI]` for title or `[MAJR]` for major topic to refine results (e.g., `anxiety[MAJR] AND virtual reality[TI]`). Create 2-3 focused search strings with different keyword combinations for best results in any search engine. For example, try searching for "*VR exposure therapy for anxiety assessment"* or *"game-based assessment for GAD in VR"*. Then use these results to find the most influential and relevant papers. ### Start a snowball fight to get deep into your literature This is where it gets exciting. Finding one solid paper opens a gateway to dozens more. When you discover a strong paper in your area, just turn this into your starting point. Study its literature review and reference list to find papers it cites (*backward snowballing*). Then use tools like Google Scholar or Scopus to discover newer papers that cite your paper (*forward snowballing*). This “snowball” method reveals an entire chain of connected research. - **Backward snowballing**: Check the paper's references to find foundations. - **Forward snowballing**: Track citations to see who built on this paper. The beauty of this method? It creates a natural chain of knowledge that helps you understand how ideas evolved over time. [Litmaps](https://go.lennartnacke.com/litmaps?ref=lennartnacke.com), [Research Rabbit](https://researchrabbitapp.com/home?ref=lennartnacke.com), and [Inciteful](https://inciteful.xyz/?ref=lennartnacke.com) are apps that help you understand these citation chains visually. ![Screenshot of a citation network in Research Rabbit.](https://lennartnacke.com/content/images/2025/02/researchrabbit.webp) Citation networks in Research Rabbit. Pick one highly relevant paper and start a citation chase: identify 2–3 papers from its references and 2–3 papers that cite it. Then repeat this process with your new findings. This method will rapidly build your collection of relevant literature. ### Stay organized or cry me a river later This might be the most important lesson I learned (the hard way). Here's my current system: 1. **Use reference management software.** Tools like [Zotero](zotero.org/), [Mendeley](mendeley.com/), [Paperpile](https://paperpile.com/?ref=lennartnacke.com), [Readcube Papers](https://www.papersapp.com/?ref=lennartnacke.com) or [EndNote](https://endnote.com/?ref=lennartnacke.com) are essential for keeping track of papers. Immediately save PDFs and citation info for each paper you find. Organize them into folders or with tags (e.g., *“VR Therapy”*, *“Assessment Tools”*, *“GAD”*). It just makes it easy to retrieve sources later and insert citations while you’re writing a paper. 2. **Create a literature matrix.** Create a structured table (in Word, Excel, or Notion) to summarize each paper. Include essential columns: Author, Year, Title, **Key findings**, **Methods**, and **Relevance/Gaps**. Write a clear 2-3 sentence summary for each paper in your own words, and note any limitations or research gaps mentioned by the authors. Viewing your literature in an organized matrix makes it easy to compare studies and becomes indispensable when writing your literature review. 3. **Take smart notes.** For each article, jot down key points: What problem does it address? What methodology? What were the results? Crucially, note how it relates to *your* research question (Does it support the need for your work? Does it leave an open question?). These detailed notes will save you from re-reading entire papers later, as you can quickly refresh your memory from your summary. 4. **Use tags, groups, and collections.** As you read, you'll start recognizing natural groupings in your literature (e.g., studies on *VR exposure therapy for anxiety*, studies on *game-based mental health assessments*, theoretical papers on *GAD symptoms in virtual environments*). Sort your references into these categories in your reference manager (with tags or collections) or notes. This organization helps you structure your literature review by themes instead of presenting a simple list of studies. 5. **Implement a consistent naming scheme.** When storing PDFs, use a consistent naming format (e.g., `2021-Smith-VR-anxiety.pdf` rather than arbitrary numbers). It’s a way for you to still locate papers by year and author even if your citation manager fails. But it’s less essential if you have cloud storage from your reference manager (and just keep all your PDFs in your reference manager). Set up a Zotero library with collections for each theme of your research. For each valuable paper you decide to keep, immediately add it to Zotero with tags and a concise a one-paragraph summary in the notes field. This simple habit will save you countless hours later when writing the literature review chapter. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### One final truth about literature reviews After years of doing this, I've realized something crucial: A good literature review is about more than finding the right papers. It also pieces together a story of your research in your head. Each paper you find weaves together a larger puzzle, showing how your field evolved and where it might go next. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Systematic Review Tutorial Bonus Video _This post is for paying subscribers only._ ### How to Manage your Time as a Professor URL: https://lennartnacke.com/how-to-manage-your-time-as-a-professor/ Last updated: 2025-05-28T14:46:09.000Z > Today’s newsletter is kindly sponsored by [​Chemy Lane​](https://chemylane.ai/?ref=lennartnacke.com). Chemistry lit reviews eat your time and budget! But Chemy Lane's AI slashes subscription spend and cuts search time in half. It references 120M chemistry publications, and recommends you the most relevant papers for your research. Transform your chemistry research from guesswork into groundbreaking. Use code **NACKE20** to get 20% off their Essentials plan at [​chemylane.ai](https://chemylane.ai/?ref=lennartnacke.com) *"Out of clutter, find simplicity. From discord, find harmony. In the middle of difficulty lies opportunity."* This Einstein quote hits differently when you're staring at an overflowing calendar in 2025's academia. With AI transforming education, rising admin demands, and pressure to publish, time management is vital. It's not just a nice-to-have skill—it's academic survival. Remember that scene in Inception? They're building dream layers within dream layers, each with its own set of rules and challenges. That's basically what being a faculty member feels like. Except, instead of dream levels, we're navigating parallel universes of research deadlines and student emails. And that creature called work-life balance. Because genius-level expertise in your field doesn't automatically translate to genius-level calendar management. Let's not kid ourselves: succeeding in academia requires more than being the top dog in your field (though that helps, woof). It's about mastering the meta-skill that determines whether you'll thrive or merely survive in academia: Time management. Without it, even the best research ideas and teaching plans crash like a computer without the latest hotfix. I want to chat about battle-tested strategies for mastering academic time management today. Because contrary to what your impostor syndrome might whisper in your little ear. You can have both an impactful career and time to watch that new Netflix series that everyone is talking about. Or take an ice bath. Whatever floats your boat, bro. ### Avoiding procrastination Let's talk about academia's dirty little secret. Procrastination. Turns out 80-95% of [college students are chronic procrastinators](https://psycnet.apa.org/doiLanding?doi=10.1037/0033-2909.133.1.65&ref=lennartnacke.com), and spoiler alert: getting a PhD doesn't cure this habit. It's like levelling up in a game only to find the final boss is still your own tendency to say "I'll do it tomorrow." What to do? ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Time Blocking ![Visualization of time-blocking in your calendar.](https://lennartnacke.com/content/images/2025/02/Time-Blocking-8896197c18adcf00.webp) How time blocking works. Time blocking is a great solution. It creates a force fields around your most precious resource: Your focused attention. Instead of letting distractions fragment your day, you're building boundaries around your mind. Our brains aren't wired for constant task-switching. So, checking email while writing that grant proposal is hard. It's like trying to pat your head and rub your stomach at the same time. The solution? Dedicate specific time blocks for deep work. Those sacred hours when your research actually moves forward. For me, this means protecting my writing hours (8-11 AM) with the same ferocity Vhagar defended Aegon the Conqueror (Any House of the Dragon fans here?). Teaching prep gets its own block in the evening when my creative energy rises again. The key isn't perfect adherence to your schedule. It's creating a rhythm that respects your peak performance times and natural energy cycles. ### **Use the 80/20 rule** ![Pareto principle overview.](https://lennartnacke.com/content/images/2025/02/Pareto-Principle-53f1660d462782ca.webp) The Pareto Principle in Academia. Check out my impact score calculator in the paid section below. Many academics find that not all tasks are equal—often, **20% of your activities produce 80% of your results** (known as the [Pareto Principle](https://en.wikipedia.org/wiki/Pareto%5Fprinciple?ref=lennartnacke.com)). Identify which activities (e.g., writing, data analysis, mentoring) help your goals. Then, give them enough time. Establish daily or weekly routines to protect those high-value activities. For example, you might reserve the first two hours each morning for writing or lab work, when your mind is freshest. Studies on faculty productivity show that [a consistent, short writing schedule is better than sporadic, long sessions](https://doi.org/10.1177/0193945912451163?ref=lennartnacke.com). Consistency builds momentum and makes large tasks feel more manageable. Break down those mammoth academic projects into bite-sized chunks. A research paper becomes a series of achievable milestones, each with its own deadline. These techniques help you focus on high-priority work. They let you avoid the less important busywork. So, you make progress on what matters for your career. ### SMART Goals ![SMART goals explained.](https://lennartnacke.com/content/images/2025/02/SMART-Goals-57d25a3564c6bffc.webp) SMART goals framework. SMART goals are more than a funky acronym to memorize. They're your framework for turning vague aspirations ("I should work on that paper") into concrete achievements ("I'll complete the methods section by Friday"). Specificity makes all the difference here. [**SMART goals**](https://en.wikipedia.org/wiki/SMART%5Fcriteria?ref=lennartnacke.com) (Specific, Measurable, Achievable, Relevant, Time-bound) provide clarity and motivation. [Goal-setting theory](https://en.wikipedia.org/wiki/Goal%5Fsetting?ref=lennartnacke.com) shows that specific goals lead to higher performance than vague intentions. ### Eisenhower Matrix ![The four quadrants of the Eisenhower Matrix.](https://lennartnacke.com/content/images/2025/02/Eisenhower-Matrix-ea9cddd1ee473226.webp) The Eisenhower Matrix Here's where the [Eisenhower Matrix](https://en.wikipedia.org/wiki/Time%5Fmanagement?ref=lennartnacke.com#The%5FEisenhower%5FMethod) comes in. It's named after that Eisenhower, who somehow managed to be a president and still have time for hobbies. This four-quadrant approach helps you find what's truly important: your research agenda. It also helps you avoid what just feels urgent, like that email thread about office coffee. Categorizing tasks as "important" or "urgent" helps turn chaos into clarity. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **The 2-2-1 Rule** A time management hack turned my chaotic calendar into a manageable one. It's Marie Kondo meets academia, but for your calendar. The 2-2-1 rule is beautifully simple. 1. Dedicate two days to research alone (think: deep work, no email kryptonite allowed), 2. Two days to teaching prep and delivery (yes, even those 8 AM classes), 3. One flexible day for shit that likes to hit the fan. ![The 2-2-1 rule in practice.](https://lennartnacke.com/content/images/2025/02/221-TMRule--7a5fcb3028d04a34.webp) The 2-2-1 rule in an example week. Days can be moved around. Why does this work? Because academia needs both intense focus periods and strategic recovery times. That flex day isn't just a buffer. It's how you can handle the surprises of university life, like sneaky committee meetings and pesky grant deadlines. Its beauty is in its flexibility. Some weeks, research commands the spotlight. Others, teaching takes centre stage. But, you always have a framework to move forward. Think of it as your academic operating system. Stable enough to be reliable, flexible enough to handle unexpected crashes. ### Boss battles and breaks **Boss Battle Method** Deep work sprints with recovery periods **Deep Work Sprint** 90:00 90-minute focus session Start **Deep Work** 90 minutes **Recovery** 20 minutes Managing time doesn’t mean working nonstop. Research on productivity highlights the value of **short breaks** to recharge your focus. I like the *boss battle method* (i.e., intense 90-minute deep work sprints followed by 20-minute recovery periods). This maintains my peak cognitive performance. In video games, game designers create rest periods between boss battles to keep players engaged. Regular breaks prevent mental fatigue and improve productivity when you return to work. This rhythm takes inspiration from that and matches your brain's natural ultradian cycle of focus and recovery. The key is treating each work sprint like a boss fight. Complete focus. No distractions. A clear goal to hit before your power bar runs out. ### The 52-17 method is a shorter alternative ![Overview of the two time blocks in the 52-17 method.](https://lennartnacke.com/content/images/2025/02/5217method-e51f71011b15502d.webp) The 52-17 Method This method relies on productivity research when tracking high performers. It maintains concentration. This approach alternates 52 minutes of focused work with 17 minutes of complete disconnection. This gives your prefrontal cortex time to recover and consolidate learning. It maintains the momentum of your work session. Think of it as academic interval training for your brain. I'm more of a boss battle kind of guy, but my colleagues have had success with this method. Overextending yourself without rest can lead to diminishing returns and burnout. Instead, working in focused sprints with planned downtime keeps you efficient and energized. In academia, where the to-do list is endless, smart time management is as much about **saying no** to low-value commitments as it is about scheduling the important ones. Strong time management habits—goal setting, prioritization, and regular routines—prevent procrastination from derailing you and [help reduce stress](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245066&ref=lennartnacke.com). You'll get more done in the long run and feel less stressed if you pace yourself and set boundaries with your time. Being organized with your time means you can be more present and prepared in the classroom. In turn, this invigorating teaching experience can then spark new research ideas. Establish clear boundaries between work and home life when possible. Practice self-care (exercise, hobbies, or time with family) to recharge. In academia, taking care of your own well-being will help you sustain your passion and avoid burnout in the long run. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Cheatsheet PDF & Productivity Calculator _This post is for paying subscribers only._ ### How to Thrive in Academia: The 5F Framework URL: https://lennartnacke.com/how-to-thrive-in-academia-the-5f-framework/ Last updated: 2025-05-28T14:48:01.000Z > Today’s newsletter is kindly sponsored by [​Chemy Lane​](https://chemylane.ai/?ref=lennartnacke.com). Chemistry lit reviews eat your time and budget! But Chemy Lane's AI slashes subscription spend and cuts search time in half. It references 120M chemistry publications, and recommends you the most relevant papers for your research. Transform your chemistry research from guesswork into groundbreaking. Use code **NACKE20** to get 20% off their Essentials plan at [​chemylane.ai](https://chemylane.ai/?ref=lennartnacke.com) Let’s cut to the chase: the traditional academic path of “publish or perish” isn’t working for everyone. In fact, it’s stressing many people right out. Many professors and senior researchers that I speak with online feel trapped, struggling to secure funding, produce impactful research, and build a fulfilling career. We all juggle teaching, mentoring, and service obligations. And that’s just the tip of the professional iceberg. It’s easy to feel like you’re running on a treadmill, going nowhere fast, with all the enjoyment sucked out of your career. Well, ain’t that a kick in the head. But what if, in a shocking twist of fate, there was a less painful option? A framework that goes beyond the narrow focus on publications and empowers you to take control of your career trajectory? Let me introduce you to the **5F Faculty Success Framework** — my model designed to help you break free from crude conventions, push your field forward, and create the academic life you envision. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### The 5F Framework The 5F framework addresses five crucial areas for academic success: **Funds, Findings, Footprint, Fellowship, and Fame.** Let’s break down each one: #### **1\. Funds** Money fuels research. Securing funding is essential for conducting meaningful projects, supporting graduate students, and building a strong lab or research program. Success often hinges on identifying opportunities, writing those persuasive proposals, and managing budgets effectively. One major tip I have here is to diversify your funding sources. Relying on one single type of funding can limit your research potential. Look beyond traditional grants to include industry partnerships, philanthropic foundations, and internal university funding. #### **2\. Findings** Publishing remains vital, but quality always outweighs quantity. I would aim to produce work that addresses significant questions, advances my field, and even something that inspires meaningful dialogue. The best way to start is by setting ambitious research goals. Don’t focus solely on adding publications to your CV, but think about how your research can address the critical challenges in your field. Impact should guide your research from the beginning. #### 3\. **Footprint** Visibility matters more than ever in today’s interconnected world. A strong professional identity gets your work beyond the boundaries of your institution and reaches the audiences that need it most. I would do this via social media. Platforms like X, LinkedIn, Instagram, and YouTube allow you to share your research, start discussions with peers, and amplify your findings to a broader audience. Social media is a powerful tool for increasing both your visibility and influence. #### 4\. Fellowship Academia can be isolating, but it doesn’t have to be. Building a strong network of mentors, peers, and trainees is essential for both your professional success and your personal well-being. It’s just great to hang with the right kind of hobbits, Gandalf. Start by seeking mentorship, but also develop a peer network for collaborations on research articles (often with people you meet at conferences). Generosity is vital in academic communities. Mentoring graduate students and junior colleagues not only strengthens your own network but also supports the next generation of scholars. Invest in these relationships through genuine support. #### 5\. Fame Yes, fame sounds kind of silly, but I needed another F here, you feel me? Without the right impact, your work just goes to waste unseen. Increasing your recognition and influence lets you shape the direction of your field and mentor others. Serving in professional organizations, editorial boards, or review panels gets your visibility up. I also recommend to apply for awards, fellowships, and honorary appointments that recognize your contributions to the field. The 5F Faculty Success Approach offers a fresh perspective on academic achievement. Instead of being trapped in the publish-or-perish cycle, my framework prioritizes thinking big about your impact. It’s about doing work that matters — research that sparks real conversations, teaching that transforms students’ understanding, and scholarship that extends beyond journal pages into meaningful change. Instead of publishing papers no one reads, share one fresh and radical idea every month with your network, build a devoted following in your niche, speak at conferences with impact. Papers don’t change minds. But people do. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Additional Materials and Bonus Video Here's a PDF cheat sheet, a bonus video, and the audio for this: _This post is for paying subscribers only._ ### How I Train My Brain to be a Smarter Academic Every Day URL: https://lennartnacke.com/how-i-train-my-brain-to-be-a-smarter-academic-every-day/ Last updated: 2025-09-04T19:26:47.000Z I used to treat “intellectual growth” like it was an occasional burst of insight. If I read the right journal article or attended the perfect conference keynote, I hoped I’d wake up some day with sharper research instincts. That approach brought me scattered epiphanies (like marbles spilled within a bouncy castle; madness) but no consistent progress. “So, what changed, professor?,” you may ask. I stopped relying on random breakthroughs. Instead, I built daily rituals that reinforce my thinking patterns, sharpen my arguments, and capture promising leads before they disappear. Below, I’ll share five foundational steps I use in my academic routine—developed over years of trial and error. You can adapt them easily to your schedule, regardless of whether you’re a dean juggling endless committees or a busy assistant professor finalizing multiple manuscripts. Every step stands on its own, but their power grows when combined into a single daily system. (And yes, paid subscribers get a worksheet for it this week.) ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 1\. Sharpen observational skills ![A scientist under the magnifying glass.](https://lennartnacke.com/content/images/2025/01/observationskills.jpg) Sometimes we have to look a little closer. I once read a creative study on remote collaboration technologies. As I slogged through the paper like a crude oil worker, I noticed three references missing from their [literature review](https://lennartnacke.com/3-essential-skills-for-every-academic-literature-review/)—seminal works any seasoned researcher in that subfield would expect. Yes, there was no arguing the novelty of the data (that’s what got the paper accepted in the first place, I assume), but I spotted those holes instantly because I’d trained myself to watch for omissions. **Observational skills aren’t about piling on critique but asking pointed questions:** - Where does existing work overlook a key variable, population, or method? - Which theoretical assumptions might be off-target? These questions emerge naturally if you make a habit of scanning for anaemic sections whenever you read a paper or attend a talk. Over time, you’ll see your research domain through a more discerning lens, and that often sparks your next big idea. At the very least, it will make you a discerned skeptic there, Neil deGrasse Tyson. ### 2\. Capture emerging ideas in real time ![Smart woman taking notes in big notebook as ideas happen.](https://lennartnacke.com/content/images/2025/01/ideasarrivingnow.jpg) I wish I would write my ideas down like this lady. The best ideas never arrive when it’s convenient. They mess you up like Gremlins when your hands are elbow-deep in dishwater. For me, lightning tends to strike after I close my laptop—driving home, vacuuming, or lying in bed trying to get my brain to shut up at 2 AM in the morning. It’s like my brain has a “Do Not Disturb” sign for when I’m actively trying to think, but throws a rave when I’m trying to remember where I put my keys. Unfortunately, brilliant thoughts also slip away if they’re not logged instantly. In my early days, I told myself, “I’ll remember this later.” I never did. It never returned. Like Donnie Darko (Not sure if you got the ending, Jake, but I was livid). By the next morning, the concept would be gone. Now, I have a quick-capture system. I dictate voice memos, text myself, or scribble keywords on any available surface (“Hello there, forehead”—kidding, there are usually grocery receipts around, if my phone isn’t handy). Then, every morning or at least every Friday, I review these fragments to see which ones might spark new grants, collaborations, or teaching modules. Often things are safe to toss, but sometimes you break open one of those fortune cookies to find a singing hamster on a bicycle. Suffice to say, it’s worth checking. Senior academics know that the difference between a flurry of half-baked thoughts and actual research progress can come down to a graceful idea-capture strategy. Each recorded note is a small Lego piece you can refine into a well-rounded argument later. ### 3\. Read with intent, not just volume ![A young man reading the one book that contains all the information.](https://lennartnacke.com/content/images/2025/01/readwithintent.jpg) Intentional reading can make you feel awesome inside. Plenty of researchers read incessantly but don’t absorb much. They’re like a hummingbird that drinks nectar all day but doesn’t gain any weight. They consume the information but don’t metabolize it into knowledge. I once burned through entire journal issues on autopilot, highlighting segments without analyzing them. When I tried to recall important theories or methods, my mind was a blur. Cabbage soup. At some point, I realized that reading fewer articles but dissecting them in depth produced stronger connections to my own projects. Being selective pays off. So, I began to question every claim, check references, and outline any conflicting evidence. If a piece contradicted something I’d published, I wrote a short internal memo on how to resolve the tension. Making the process more interactive saved me time because each reading session contributed directly to future writing or teaching somehow. What I know now and teach my students: Senior scholars value synthesis skills. So, not just what you’ve read, but how you interpret its *relevance* and *limitations*. This approach spurs your credibility and cements your status as an expert in your domain. ### 4\. Conduct a daily self-review ![Contemplative woman writing a self review in bed.](https://lennartnacke.com/content/images/2025/01/daily-self-review.jpg) Daily self reviews can go a long way when they happen before bed. Picture this: It’s 10 PM. Your experiments ran longer than planned. Your departmental emails went unanswered. And your upcoming committee meeting slides remain half-finished. You could go to bed and hope tomorrow magically fixes itself. But a simple 10-minute self-review can transform your mood. I am to this day struggling to reserve those 10 minutes right before bedtime to ask myself: - *What moved forward today?* Maybe I refined a methods section or connected with a colleague on a shared project. - *What major distraction derailed me?* Perhaps I got stuck in back-and-forth emails that weren’t urgent. - *Which small change can I commit to tomorrow?* Whether it’s batching emails, delegating tasks, or blocking out a writing window. Days when I actually achieve this feel much better than days when I don’t. It replaced my old habit of ruminating on what went wrong. These small, trackable improvements let me structure my time more effectively. Senior academics juggling multiple roles—from editorial boards to funding panels—can benefit immensely from short, focused reflection to set their priorities straight. ### 5\. Write every day, even if briefly ![Three different moments of the day showing a person taking short notes.](https://lennartnacke.com/content/images/2025/01/writing-habit.jpg) A person building a writing habit. This one is big for me. I write shenanigans on social media every day. Because publishing in top journals or guiding doctoral students to completion demands a regular writing habit. Yet many academics, especially those at the senior level, grapple with large administrative loads that push writing to the margins of their day. And answering emails doesn’t count as intentional writing. I faced the same challenge, so I set a strict policy: write something every day, even if it’s just 10 to 20 minutes. Even if it’s “just” stuff for social media. And guess what, I learned to love it. Why does this help? Writing clarifies your thinking. And yes, these days, you can get your favourite LLM looped in as your sparring partner while writing, but don’t just hand over your brain. Prompt, reprompt, mix & match, pitch & patch. You might uncover a logical flaw in a an idea or spot a gap in someone’s argumentation. You also maintain momentum, so you’re never starting from scratch when deadlines loom. You keep that writing muscle warmed up. If writing is sporadic, you’ll dread each return to the page. But if you treat it like brushing your teeth—that’s a nonnegotiable right there, Ron Weasley—you’ll create a muscle memory for drafting and polishing. Over time, this can reduce your revision cycles and help you produce better arguments. And things just pop from your brain to your fingers and keyboard. It’s fun. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### But, Professor, I’m a creative little tulip ![A creative little artistic tulip, ready to conquer the world.](https://lennartnacke.com/content/images/2025/01/creative-tulip.jpg) What a cute little, creative tulip you are. Some colleagues argue that creativity can’t be “forced,” that research breakthroughs happen spontaneously. While Eureka! moments do show up unexpectedly, daily habits prepare you to make the most of them. And preparation is everything to leverage opportunity. Others say they’re too busy for reflection or consistent writing. Yet top-producing scholars often have the same or more responsibilities. They don’t rely on free time; they rely on habits that turn small pockets of time into meaningful work. As you know, work expands to fill the time available for its completion. That’s Parkinson’s Law. If you think these suggestions are too rigid, let’s hear it. Hit reply, comment on my website, maybe you’ve found an alternative approach that suits your schedule better. But I suspect you’ll find that committing to even one or two daily changes can sharpen your academic instincts more than any marathon reading session or sporadic late-night scramble (“to the window, to the wall, until…”). That’s all for today. Have a wonderful day. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### Want to see my concrete methods? Download them here One more thing. Below is a separate PDF featuring the tangible tactics I use daily. Feel free to print these out and pin them to your office wall if you're a paid subscriber. They’re straightforward checklists and prompts that keep my routine on track. _This post is for paying subscribers only._ ### 3 Steps to Snag that Research Grant URL: https://lennartnacke.com/3-steps-to-snag-that-research-grant/ Last updated: 2025-05-29T03:38:17.000Z Let’s be honest, research grants can feel a bit like being in an episode of *Survivor.* Full of twists, challenges, and fierce competition that smells of elephant dung. You pour your heart and soul into a proposal, only to receive a generic rejection email that leaves you feeling utterly crushed. Kind of like Cinderella being left at home while everyone else went to the ball. Bad luck this time, Prince Charming. I’ve been there, trust me. It sucks. But over the years, I’ve noticed a clear pattern: winning proposals have a few key things in common. Most researchers get so caught up in the details that they forget they’re writing for humans. They drown bright ideas in technical details. They suffocate insights with complexity. They let brilliance fade in convoluted explanations. All effort wasted. But what if I told you there’s a much simpler way? A way to make your proposal rise like a phoenix from the ashes of rejection and seize the funding you dream of? It boils down to three simple steps: ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 1\. Define the problem (for a zombie horde hungry for brains) _This post is for paying subscribers only._ ### Why Research Gaps Are Bullsh*t URL: https://lennartnacke.com/why-research-gaps-are-bullsh1t/ Last updated: 2025-05-28T16:17:01.000Z I’ve been lied to. As a result, I’ve lied to my people on social media. Sorry. Academia has fed us an impenetrable myth: that research is about [finding gaps in the existing knowledge](https://lennartnacke.com/the-7-research-gaps/). I ate it up. And, boy, that chase felt good. Channeling your inner Indiana Jones to fill those gaps in the literature (although all he did in the last movie was fill an age gap with computer-generated imagery). But, before you call me a dementor because I suck the joy out of everything, know that there is something inherently easy and exciting about using research gaps as motivation for a research paper or [when looking to find a research topic](https://lennartnacke.com/how-to-find-a-research-topic/). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. You've probably heard it a thousand times. [I’ve preached about research gaps myself](https://lennartnacke.com/the-7-research-gaps/) (and used them copiously in my writing), but I’m here to tell you now that just chasing gaps is a recipe for irrelevance. [Larry McEnerney, the director of the University of Chicago's writing program, opened my eyes.](https://youtu.be/3%5FlTELjWqdY?si=O9s5m3cBVGrOyS1K&ref=lennartnacke.com) It's a trap that leads to countless hours spent on research that, frankly, nobody cares about in the long run. Gaps are like redshirts on a Star Trek away mission. Nobody cares if they’re gone. But don't worry, there's a better way. Luckily a way I’ve been teaching myself for a long time in my [CHI writing course](https://chicourse.com/?ref=lennartnacke.com) as well. [Something I learned from Carl Gutwin years ago.](https://lennartnacke.com/chi2016-course-interview-with-carl-gutwin/#about-the-structure-of-a-chi-paper) And it has to do with something much more valuable: problems. Real, meaningful problems that your readers actually care about. Like having to spend the winter with Jack Torrance at the Overlook Hotel. The difference between research that gets cited boatloads, and research that fades and falls to forgotten fragments. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) The gap mentality stems from an outdated, positivistic view of knowledge. It assumes that knowledge is like a giant, finite crossword puzzle, and our job as researchers is to fill in the missing squares. We find an area that hasn't been studied, and—voila—we've justified our research. But here's the ugly truth: knowledge is infinite. If you fill a gap in an infinite field, how many gaps are left? An infinite number. It’s just like Nintendo releasing another Mario or Zelda game, you know there is an infinite number of those left, too. You've achieved precisely nothing. Productivity level: Snorlax. ![Academic snorlax studying books.](https://lennartnacke.com/content/images/2025/01/snorlax-academic.jpg) Don't be a Snorlax... The problem is about who gets to say what is considered knowledge. Historically, this has been a set of people who look very much the same and who use the same toilets, but fortunately, this is changing. However, these people in power (academic power, but still) get to say what is considered knowledge, and what isn't. Arguing that something is new or original isn't enough. You have to convince the crew in peer-review. Think about it: we could generate new knowledge right now by counting the number of people reading this email. Nobody knows that number (except for me, I can look it up and I’m excited it’s growing), but would anyone actually care? Of course not! Because it's utterly useless information (to most people that are not me or advertisers wanting to sponsor this newsletter). New does not equal valuable. ### The Paradigmatic Power of the Problem Instead of chasing gaps, successful researchers focus on **problems**—specifically, problems that their target community of readers cares about solving. This is a fundamental shift in mindset. Your purpose is not self-expression but transformation. First, you write to express your inner thoughts, to understand yourself, to think. The second form of writing, the one that gets you published is about changing your readers. Less personal revelation, more public revolution. To be clear, this isn't about lying, but about understanding that there are different functions of an academic piece. You are writing to change the ideas of an existing community. Your peers. That’s your main audience. Instead of asking "What hasn't been studied?" ask yourself: - **What are the existing inconsistencies, tensions, or contradictions in the current understanding of this field?** There is a difference between arguing that someone is wrong, and reframing the argument to be about contributing to the overall knowledge. - **What are the limitations of current approaches, and why do those limitations matter?** - **What problems are people in my field grappling with, and how can my research help address them?** You will fill conceptual gaps if you focus your research on solving real problems. The value of your work to your academic community will increase. Your work becomes not just an addition to the literature, but a catalyst for advancing your field's understanding. ### How to Spot a Valuable Research Problem Here's how to identify problems that will make your research resonate: 1. **Know your audience.** This is paramount. Who are the key players in your field? What are their current debates and concerns? A problem is only a problem *for someone*. So, a problem doesn’t exist in a vaccum of your subject matter but it’s a problem for one of your readers somewhere. For example, according to Pew Research Center data from 2023, public trust in scientists has declined since the beginning of the COVID-19 pandemic, particularly among certain political groups. Before the pandemic, trust in scientists was high, with 87% of US Americans expressing at least a fair amount of confidence in April 2020\. However, by [October 2023, this figure had dropped to 73%](https://www.eenews.net/articles/public-trust-in-science-tanked-during-covid-its-still-low/?ref=lennartnacke.com), marking a significant decline. Distrust also increased, with [27% of Americans expressing little or no confidence in scientists by late 2023 (which continues to decline)](https://www.nature.com/articles/d41586-024-03723-5?ref=lennartnacke.com), compared to just [12% in early 2020](https://www.pewresearch.org/science/2023/11/14/americans-trust-in-scientists-positive-views-of-science-continue-to-decline/?ref=lennartnacke.com). This presents a problem for researchers seeking to communicate the importance of scientific findings to a skeptical audience. 2. **Look for instability, not just absence.** Instead of simply pointing out that something hasn't been studied, look for areas where existing knowledge is unstable, inconsistent, or contradictory. This is where the real problems often lie. Use words like "however," "but," "inconsistent," or “anomaly" to highlight these instabilities. Spend some time finding those code words in articles from your field and train yourself to spot value-creating language. 3. **Articulate the cost.** Why does this instability or problem *matter*? What are the negative consequences of not addressing it? Frame the problem in terms of its cost to the field, to society, or to a specific group of people. When searching for a good problem, look for its impact on specific readers, not just “the field” but real people working in the field. 4. **Argue instead of explaining**. Your goal isn't to simply explain your ideas; it's to persuade your readers that your perspective is valuable. Remember that you want to move them from a state of knowledge before encountering your text to a state of knowledge after encountering your text. Anticipate their doubts and address them head-on. To create value, you must persuade your readers first. This leaves us with another interesting tidbit. [Rules, however enticing they are](https://lennartnacke.com/how-to-shift-your-writing-from-good-to-great-13-secrets/), are often generic and not aimed at your specific reader in your specific field. If you are thinking about writing rules, you are often not thinking about your reader. Specific readers are not generic. Don’t send a Charmander to fight a Blastoise or your days as Pokémon trainer might be numbered. In the same way, gaps are more generic and research problems are more specific. Stop chasing gaps. They’re a phantom. A void in the ever-expanding universe of knowledge. A distraction from the real work of research. Start focusing on problems, on the inconsistencies and challenges that your research community cares about. This is how you produce research that matters, research that gets read, cited, and makes a genuine impact. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Bonus Video _This post is for paying subscribers only._ ### When “Fast” Beats “Perfect” URL: https://lennartnacke.com/when-fast-beats-perfect/ Last updated: 2025-06-06T16:24:56.000Z *You need to write a literature review, but do you have 6 months or 6 weeks? Here’s how to decide.* I remember staring at my computer screen many nights in a row and struggling to select sources. The deadline for my literature review looming like a storm cloud on the dark horizon. My professor had thrown around terms like “systematic review” and “comprehensive search.” Honestly, I felt completely lost. I had a mountain of research papers to sift through, and the thought of spending the next months of my life chained to my desk reading filled me with dread. But was there another way? Turns out, yes, there is. In academic research we don’t just do painstaking, year-long reviews that emphasize detailed quality assessment (called systematic reviews). There’s a faster, leaner option called a rapid review. But which one is right for you? How do you choose between speed and absolute certainty? That was what I had to find out. There are actually 14 different types of literature reviews, according to [a fascinating paper by Grant and Booth (2009)](https://onlinelibrary.wiley.com/doi/10.1111/j.1471-1842.2009.00848.x?ref=lennartnacke.com). But for most people beginning their academic journey, the decision comes down to two main contenders: ## Systematic Reviews Think of these as the marathon runners of the research world. They’re exhaustive, meticulously planned, and take anywhere from **6 to 24 months** to complete. ### **What they involve** - **Formal protocols.** As in, you have to make a plan and stick to it. - **Comprehensive searching.** This means you need a strategy for selecting, searching, and excluding literature databases. - **Rigorous quality assessment.** Not only do you usually assess the empirical work of the studies in the papers you collect, but you often also evaluate them for their validity and reliability. - **Tabular presentation.** Often, you’ll meticulously organize these comparison results in tables. - **Narrative synthesis.** You usually also provide a detailed summary of the evidence in a structured format that follows a logical flow to tell the story of the research findings. ### **Why choose this?** When you need absolute certainty. When the stakes are high, and you can’t afford to miss anything. If you’re setting up medical guidelines, for example, this is the way to go. ## Rapid Reviews These are the sprinters. They’re designed to provide quick insights in a matter of **1 to 6 months.** ### **What they involve** - **Flexible protocols.** Not a lot is set in stone here and you can usually adjust the process as you go. - **Time-bound searching.** The idea is that you focus on the most relevant sources within a specific timeframe that you set in advance. - **Limited quality checks.** You only do a basic assessment, but not to the same depth as other review types. - **Evidence summary.** You create a concise overview of the key findings. That’s it. ### **Why choose this?** When you need answers fast. If you’re a policymaker responding to an urgent issue, or a business leader making a time-sensitive decision, a rapid review can give you the information you need without the long wait. ## The Bottom Line: Your Timeline Is Your Thing Choosing between a systematic and rapid review isn’t about right or wrong. It’s about what fits your needs and resources. > A systematic review requires exhaustive, comprehensive searching with quality assessment criteria, while a rapid review can be completed with time-limited formal quality assessment. The difference is months of work. > > According to this paper, 14 literature review types exist. > > If… [pic.twitter.com/IEGhR7CCyF](https://t.co/IEGhR7CCyF?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [December 23, 2024](https://twitter.com/acagamic/status/1871198046882762833?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) ### **Pick a systematic review when** - The highest level of quality and accuracy is essential. - You need rock-solid, undeniable evidence. - Time is not the primary constraint. ### **Pick a rapid review when** - Speed is of the essence. - You need actionable insights quickly. - “Good enough” evidence is sufficient for your purpose. Don’t let academic writing bog you down. Your timeline is your guide for these things. Choose the review type that gets you the information you need, when you need it. Sometimes more is required, sometimes less is okay. And your research team and you make that decision. Now, I know some of professors in the audience might be clutching their pearls at the thought of anything less than a fully systematic review. But in the real world, we often have to make decisions with incomplete information. Is a rapid review *always* ideal? No. But is it sometimes the *best* option? Absolutely. ## Comprehensive Resources for Paid Subscribers Here is my comprehensive list of search engines, academic databases, systematic review guidelines and software for meta analysis: _This post is for paying subscribers only._ ### How To Publish And Get Your Research Cited URL: https://lennartnacke.com/how-to-publish-and-get-your-research-cited/ Last updated: 2025-06-06T23:49:24.000Z You don’t need to be at a top-tier university or have decades of experience to produce research that makes a real difference. With the right approach, your next paper could be the one that changes your field forever. But there is a strategy to publishing research that ensures your research makes a difference. It starts with asking the right questions. In today’s issue, I’m sharing ten steps to ensure your work gets the attention and citations it deserves. Let’s head into it. ### 1\. Choose impactful research questions I know you want to do work that matters. I do, too. But sometimes it’s tough finding the issues that really matter in your field. One way to do this is to start with questions that address real gaps in your field. I will probably write a more in-depth tutorial about this in the future and I’ve already written about [research gaps in the past](https://lennartnacke.com/the-7-research-gaps/). But here’s a way to get to research questions that matter quickly: You begin by tracking recent publications in top journals in your field to identify emerging gaps as you scan new articles that are coming out. The best place to look for new gaps is the “future work” section of current papers as well as highly influential papers in your field from the last 5 years (keep in mind that there is no hard metric for influence, but looking at the citation count is a great way to start). In conjunction to skimming these papers, I would also use citation databases (e.g., [Scopus](https://www.scopus.com/?ref=lennartnacke.com), [Web of Science](https://access.clarivate.com/login?app=wos&alternative=true&shibShireURL=https://www.webofknowledge.com/?auth%3DShibboleth&shibReturnURL=https://www.webofknowledge.com/&roaming=true&ref=lennartnacke.com), [Google Scholar](https://scholar.google.com/?ref=lennartnacke.com)) to see what topics in your field are heavily cited and consider adjacent questions to these topics. For example, in my field, there has been a massive research shift to AI in recent years and before that it was VR. Finally, I would try to connect the research questions you’ve drafted with practical problems in your domain: Ask yourself: What societal, technological, or policy issues demand solutions? ### 2\. Follow a structured research process The quality of your research process directly impacts the quality of your findings. It’s that simple. So, what do I recommend to create highly quality research is to establish a high-quality research process. An easy way to do this is to start research with a literature map to synthesize previous research. I always use reference manager tools like [Zotero](https://www.zotero.org/?ref=lennartnacke.com), [Paperpile](https://paperpile.com/?ref=lennartnacke.com), [Mendeley](https://www.mendeley.com/?interaction%5Frequired=true&ref=lennartnacke.com), or concept-mapping software at this stage. Next, I would want to make sure I follow a robust methodology (i.e., quantitative, qualitative, or mixed methods). My methods should always align with leading standards in my field. Of course, it takes a bit of time and reading to understand what those standards in your field are. Good research processes can therefore not be rushed but require some time to develop well. A good process includes collecting and analyzing data well and with rigour. One way to guarantee your data collection is rigorous is to simply document every step so that other researchers can reproduce your work and how you did things is transparent to your readers. Finally, I would want to deliver hard evidence to support my conclusions in my research. Yes, data is great, but you will always need to argue for the insights that you are drawing from it. I like to do this with clear visuals like tables, figures, and charts. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 6,737 researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 3\. Write a compelling manuscript This is why you’re on this newsletter. So, many of you already know this. And, I’ve been preaching it for quite some time. You can have the best research in the world, but if it’s poorly written, no one will read it. High-quality writing makes your findings accessible and engaging to a broad audience. Of course, I’ve talked about this in many a masterclass and webinar before, but here is how to write the best publication possible. I would use a version of the IMRAD structure: Introduction (and Related Work), Methods, Results, and Discussion. In my field specifically, we always have related work in a separate section after the introduction. I would use a version of this structure for both my introduction and my abstract as well. For your abstract, double-check that it contains the following: - Problem statement - Methodology - Key findings - Significance Then, I’d use my abstract to create a clear and concise title that includes keywords, and maybe a colon. I’ve written about this before. For your title as well as your paper, you’d always want clear sentences that flow well, so alternate short and long sentences and find clear transitions. I try to avoid field-specific jargon unless absolutely necessary to get my point across. And finally, I put some extra effort into writing an impactful conclusion that ties findings to broader implications. ### 4\. Publish in the right journals Yes, publishing in general is a great strategy to get your work out there. But, the journal you choose influences your work’s reach and citation potential. It’s a big deal. And you may not land the top journal while doing a PhD, but you certainly set your eyes on the right venues early. I would recommend the following strategy: I’d identify high-impact journals in my field using impact factor or h-index metrics (like the [metrics you can find on Google Scholar](https://scholar.google.com/citations?view%5Fop=top%5Fvenues&hl=en&ref=lennartnacke.com)). Then, going forward, I would align my research scope with the journal’s audience and aims. In addition, I would probably prioritize open-access journals whenever possible because they increase visibility of your work. Now, I know this might be expensive for some, but it pays dividends in the long run. One last option to think about is to explore special issues or thematic calls for papers in high-ranking journals when the call fits your area. You will likely attract a more focused readership in your niche, which will lead to more impact of your work. ### 5\. Use strategic keywords for discoverability Consider that researchers find work through search engines and databases, so you need to make it easy for them to find yours. I know this is usually the least of our worries when trying to get an article published, but all those LinkedIn influencers pushing SEO title strategies for your papers are not completely wrong. You want to know what people are looking for to get discovered. The best way to get started with this is to identify relevant keywords from your literature review. These are the keywords I’d build your paper around and I would want to include them in the title of the paper, the abstract, and often also in subheadings and body text. ### 6\. Promote your research actively Publishing alone isn’t enough unfortunately. Just because you got past peer review, doesn’t mean people will actually read your work. Sure, the right journals do much for visibility, but you do need to share your work far and wide. Trust me, it’s worth the effort. So, what are some promotion strategies that I’ve used in the past for my work? I generally try to share the findings from my research group on social media and my students like academic networks, such as [ResearchGate](https://www.researchgate.net/?ref=lennartnacke.com) and Academia.edu. I find them both quite spammy to be honest (there was a time when they send way too many emails) and it took me a long time to actually appreciate some of their features (I didn’t like the logged-in PDF wall they have). However, they can be effective. Either way, I would definitely post highlights of my research on LinkedIn, X/Twitter, or other relevant platforms, such as BlueSky, Threads, Instagram, and maybe even TikTok or Pinterest. I find for my work that I usually get the best discussions on LinkedIn (don’t hate). When posting there, I usually put in some effort to use plain-language summaries for broader appeal. Example: “Our research shows a 20% improvement in X using Y.” But, I have also collaborated with press offices at my institution for media outreach. They can be hit or miss, depending on how knowledgable you are about this stuff yourself. However, one thing that is quite common in my field and that I would definitely recommend in addition is to present your work at conferences or workshops to engage your peers. And more often than not, we’ll upload a preprint to platforms like [arXiv](https://arxiv.org/?ref=lennartnacke.com) or [bioRxiv](https://www.biorxiv.org/?ref=lennartnacke.com) for early visibility (you might not want to do that if you’re afraid you’ll get scooped). [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 7\. Collaborate strategically We love people. And while you be in hermit mode when researching your stuff, working with other researchers can substantially improve both the quality and visibility of your work. So, it’s something I would consider. (Keep in mind the downside as well: At some point, I had collaborated with most of the mid-to-senior people in my field, so that most people had a conflict of interest reviewing my papers, which led to a tough period of being evaluated by less-relevant peers.) In general, though, I will always co-author with experienced or well-connected academics. It’s fun. And I love participating in international collaborations, which will increase your reach. Find mutually beneficial opportunities and go all in. I also like to contribute to research networks, working groups, or multi-institution projects. All of these increase your visibility and that of your research. ### 8\. Cite and engage with others’ work Reciprocal engagement is key. Yes, citations must be relevant. I don’t want you to form a citation cartel. Those are bad. But, when you engage with other researchers’ work, it increases your own visibility and credibility. And it’s the right thing to do if the work is relevant to yours. Don’t be petty and not cite someone, because you feel they’re infringing on your territory. Academia is a community. Sharing is caring. So, I would recommend to cite recent, relevant studies in your work because all researchers notice citations. I liked to reach out to authors I cited to share my paper directly when I was more junior and it got some discussions started. Boss move: Publish a review paper that synthesizes key findings in your area. That’ll get people’s attention, for sure. ### 9\. Try out citation-boosting tactics You might be surprised how much small actions can impact your citation count. Availability of your research is a big one, so having your papers available on your own website is essential and many publishers allow so-called author versions. You could also share your paper in course syllabi or research group discussions at your institution to get some of that local visibility. One thing that is more common today that it used to be is to register your work with ORCID and then, of course, to keep your profiles up-to-date. In your next grant, you might also want to consider participating in cross-disciplinary research to attract a broader audience that goes beyond your discipline. This is great for introducing your work beyond your usual community. ### 10\. Monitor and improve over time Always keep learning. Track your impact with citations, podcast, visits, newspaper articles about your work, and then adjust your approach for better outcomes in the future as you see what works for you. Not everyone likes to use the same strategy. Introverts prefer different tactics than extroverts and so forth. One way I do this, is track impact with tools like Google Scholar citations, Scopus, or Altmetric to monitor the reach of my research group. You might easily identify which platforms drive the most visibility and engagement. At some point, Academic Twitter was really great for this, but it’s not anymore, and BlueSky is only now gaining critical mass for this. One thing, I definitely recommend is to regularly update your academic profiles (ORCID, ResearchGate, institutional websites) to make sure people find your most recent work. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 6,737 researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Do this now Here are 10 simple steps you can do right now to get a better strategy implemented for your research publications: _This post is for paying subscribers only._ ### How To Write A CHI Paper URL: https://lennartnacke.com/how-to-write-a-chi-paper/ Last updated: 2025-06-06T23:50:35.000Z So, you might be a bit like I used to be. Dreaming of seeing your work presented at CHI, the premier conference in human-computer interaction (HCI)? You envision yourself on stage, sharing your groundbreaking research with the brightest minds in the field. But there's a catch: CHI is so notoriously competitive. And I used to get rejected. Like hard. But then, over the years, I figured out their formula and I want to share some of this knowledge to help you beat the CHI submission process. ## CHI's gates of steel and stone Let’s face the reality: getting a paper accepted at CHI is about as easy as teaching a cat to file taxes. With an acceptance rate hovering around 26%, only the cream of the crop makes it through the rigorous review process. But what exactly makes a CHI paper stand out? The simple secret lies in originality, significance, research quality, handling of prior work, and presentation. Your paper must do more than reiterate existing knowledge; it must push the boundaries of HCI, offer a fresh perspective, and deliver tangible contributions. Think of your paper as the software update that doesn't break everything, prompting a shift in how we think and act within the field. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## One simple question to rule 8,000 words Every compelling CHI paper begins with a problem that leads to a burning research question. This question serves as your direction. It guides your research journey. And it shapes your ultimate contribution. But not every spark ignites discovery. Some questions whisper, others roar. A good research question is clear, focused, and grounded in reality. It's better to chase lightning than to count raindrops. You want a question that begs to be answered, a question that can be tackled through rigorous research and measured for impact. Treat your research question as the load-bearing wall in a house: if it fails, the roof collapses. So, take some time to craft a question that is researchable, arguable, feasible, and relevant to your chosen topic. In an ideal world, you pick your methods after you set your question. They are the tools you use to answer your research question. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ## The secret trinity of any CHI paper Here's my take on writing CHI papers. Think of them as a three-act structure. Act one sets the stage, introducing the problem and its significance. Act two shows off your solution, fleshing out your approach and methodology. Act three brings to light the impact of your work, putting your contributions in scientific context and ruminating on future implications. ### **1\. Context: The Problem** First, define the problem you’re addressing. Why does it matter? Who is affected? What are the current limitations? Authors anchor their work in past findings and theories. They map out how previous efforts approached similar questions and where they fell short. They look at which groups have felt the impact, whether those interventions addressed cultural or organizational considerations, and how well they fit with current practices in HCI. You want to show here that the problem does not stand isolated. Instead, it emerges from a long conversation about user needs, design principles, and technological growth. Such context prepares the reader for a new contribution that can progress beyond familiar territory. This shows them that the upcoming study does not rest on guesswork but on an informed understanding of what came before it. ### **2\. Content: The Solution** You frame your contribution by detailing how it responds to the identified needs and builds on prior knowledge. How does your approach address the problem? What are its key components? What methodologies did you use? Readers see how you structured your chosen methods, whether you worked with prototypes, conducted interviews, or analyzed existing data. Ideally, you are presenting a solution that addresses both technical and social dimensions. Each component of your solution emerges for a reason, from the foundational design choices to the metrics you selected to measure its effectiveness. You are doing more than delivering a solution. You are placing an idea on firm footing. So that other researchers can see precisely how your solution might stand up against competing approaches and find its place in ongoing scholarly discussions. ### **3\. Contribution: The Impact** Finally, you describe how your work helps researchers and practitioners see issues from fresh perspectives. What are the tangible benefits of your solution? How does it advance HCI knowledge? What new opportunities does it create? Perhaps it informs better design principles, guides new tool development, or encourages more inclusive user experiences. Each gain, whether it involves improved usability, broadened access, or strengthened cooperation, signals that your effort here was not just a theoretical exercise. Instead, it creates openings for future investigations and encourages others to build on these steps you have presented. The result of a good contribution is a lasting mark that transforms previously held assumptions and sets a more effective course for HCI research. ## Some practical tips Now that you understand the core principles of writing a CHI paper, let’s dig into some practical tips to help you write better CHI papers: - **Embrace the short revision cycle.** Familiarize yourself with CHI’s unique revision process, which allows for a single round of revisions within a tight timeframe. This means your initial submission must be polished and your revisions strategic and focused. - **Target the right subcommittee.** CHI is like a mosaic of minds with diverse subcommittees, each with its own quirks and expectations. Research the subcommittees carefully to identify the best fit for your work and tailor your submission accordingly. - **Make your figures do the heavy lifting.** A picture is worth a thousand words, especially at CHI. Ensure your figures are not just aesthetically pleasing but also informative and self-explanatory. A reviewer should be able to grasp the essence of your paper simply by looking at the figures and tables. - **Don’t test the reviewers’ patience.** Reviewers are your audience, treat them with respect. Avoid overstating your claims, using insensitive language, or submitting a poorly formatted paper riddled with typos. These seemingly small details can significantly impact a reviewer’s first impression of your work (and it's hard to backpedal after that). - **Structure your paper as a pattern that patterns itself.** Think of your paper as a set of nested Russian dolls. The abstract is a condensed version of the introduction, which in turn is a condensed version of the full paper. Each section should build upon the previous one. They provide increasing levels of detail while maintaining a clear and consistent narrative. I explain this [​in detail in my CHI writing course​](https://www.chicourse.com/?ref=lennartnacke.com). So, keep in mind that redwoods grow longer than roses bloom. Building the muscle to write good CHI papers takes time. It requires dedication, meticulous research, and a passion for pushing the boundaries of HCI. But with the right mindset and my tips, you’ll be well on your way to CHI success. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ### Next steps Ready to explore more of the CHI lore? Here are three resources to fuel your journey: 1. The current [​CHI 2025 conference website​](https://chi2025.acm.org/?ref=lennartnacke.com) and [​previous CHI 2024 program​](https://programs.sigchi.org/chi/2024/program/all?ref=lennartnacke.com). Your resources for all things CHI, including submission guidelines, deadlines, and links to past conference proceedings, and talks. 2. My [​'How to Write CHI Papers' Podcast​](https://go.lennartnacke.com/podcast-chipapers?utm%5Fsource=newsletter&utm%5Fmedium=email&utm%5Fcampaign=icon-link-in-newsletter&utm%5Fterm=newsletter&utm%5Fcontent=newsletter) and [​YouTube playlist​](https://youtube.com/playlist?list=PLXaGnDxciHwjaJZMDfb6j9uwLwXMHjsaq&si=NmaqAQB-4-okITVI&ref=lennartnacke.com): My interviews with CHI researchers, offering inspiration and insights. 3. [​My CHI course website​](https://www.chicourse.com/?ref=lennartnacke.com): My comprehensive online course designed to help you master the art of CHI paper writing, from ideation to submission. ## Download Please copy *The Ultimate CHI Paper Writing Guide* in Notion for my paid subscribers below: _This post is for paying subscribers only._ ### How to Tell the Quality of Your Sources URL: https://lennartnacke.com/how-to-tell-the-quality-of-your-sources/ Last updated: 2025-06-06T23:47:18.000Z That dreaded literature review looms large. Your inbox overflows with journal articles. But, some sparkle with statistical significance, others hide conflicts of interest behind fancy phrasing. And, I remember staring at my table of contents, wondering if my source selection would impress or implode during peer review. So do many authors, who are just getting started. They simply don't know which sources to trust. Therefore, we need a simple system to help us check whether any source we find on Google Scholar or our academic search engine of choice is actually research that we can trust. Let me present to you seven criteria that help you check whether or not a source is worth citing: ## 1\. Published papers prove their worth Authority isn't just a fancy word for "seems legit." Pull up the author's profile. Yes, legit researchers have a website (the uglier, the better, just kidding). - Do they teach at respected institutions? - Have they published other work in this field? - Are they cited by other scholars? Don't be the student, who cites a "Dr. Smith" who turns out to be a wellness blogger with a mail-order degree in crystal healing. Hard Nein. A better approach if you're completely new to this: Google Scholar the author. If they're legitimate, you'll find a trail of academic breadcrumbs—papers, citations, and institutional connections. Follow the publications. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 2\. Know whether journals justify trust Not all journals deserve your trust. Look for peer review processes, impact factors, and institutional backing. Watch for red flags: promises of rapid publication, fees without clear services, or websites that look like they were designed during the dial-up era. There are too many shady players in this game. A good starting point is [Beall's List of Potential Predatory Journals and Publishers](https://beallslist.net/?ref=lennartnacke.com). Fun fact: I've published in Frontiers journals and they are on the list. But that publisher is so large that some parts of their brand are shady and others aren't (long discussion). Either way, tread with caution when a name pops up on the list. Another pro move is to check if the journal appears in your field's major indexing services. If it's missing from Web of Science or Scopus, treat it like that leftover sushi from last week—with extreme caution. ## 3\. Study design divides wheat from chaff Strong academic work backs claims with data, not dramatic declarations. Count the citations. Check the methodology. If a paper makes sweeping statements without support, it belongs in the same category as your uncle's Facebook posts about conspiracy theories. No, the earth is not flat, Theodore. Here's a smart strategy: Flip to the methods section first. No methods? No dice. Empirically speaking that is. Now, there are fields that don't stress empirical studies and still provide valid additions to theoretical literature. Especially in the humanities and other fields, there is plenty of high-impact work that never flirted with empiricism. Then, again rockstars like Michel Foucault conducted empirically grounded historical research, too. His methodology combined theoretical analysis with detailed examinations of concrete practices and local phenomena. What a cool person. ## 4\. From low-quality to literature review Follow the paper trail. That's a sure way to find work that has bubbled to the surface over the years: - Do other respected scholars cite this work? - More importantly, how do they cite it? Sometimes a widely-cited paper is famous for being spectacularly wrong (like those studies on dubious health benefits of chocolate, many of which are [not as effective as we'd like them to be](https://www.mdpi.com/2072-6643/13/9/2909?ref=lennartnacke.com)). My tactical tip here: Use Google Scholar's "cited by" feature to see who's referencing the paper and what they're saying about it. ## 5\. Browse better, research smarter In some fields, a five-year-old paper is ancient history. In others, classical works from decades ago still shape current thinking. Know your field's freshness dating system and a paper's half-life. My archaeology colleagues would joke that anything from this century counts as "breaking news." For this, it really takes getting to know your field a bit. Check your field's major journals. How old are their typical references? That's your freshness benchmark. Work from there. You might also find that there is a major need for updated citations from a related field (like when Bayesian statistics made their rounds in human-computer interaction). ## 6\. Limitations are just honesty Every paper has a perspective, but good academic work acknowledges its limitations. Watch for language that sounds more like a sales pitch than scholarly analysis. This should usually not get through peer review and if it did, then that's a surefire sign that the peer-review quality of the publication venue is lacking. If it promises to revolutionize everything you know about underwater basket weaving, grab your skepticism hat. In the discussion (at the end of it or in a separate section after it), look for a limitations section. Its absence speaks volumes about the quality of the venue. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## 7\. The writing makes the scholar Professional academic writing isn't just about content. The ability to write good manuscript is a critical skill for researchers. Much excellent research can get rejected because of poorly written manuscripts. Our [recent research](https://www.sciencedirect.com/science/article/pii/S2949882124000550?ref=lennartnacke.com) emphasizes that researchers should be the 'primary drivers' of their manuscript writing (and not AI). But that doesn't mean they should invite spelling mistakes everywhere. Spelling mistakes happen, but a paper riddled with errors suggests careless work. If the authors couldn't be bothered to proofread, did they really triple-check their data? And why was there no editorial process that helped put the paper into top shape? Bottom line: Three typos? Maybe. Three typos per paragraph? Problem. ## What to do about it? Start with these three steps today: 1. Create a source evaluation checklist in your reference manager (I've included one below for paid subscribers) 2. Set up Google Scholar alerts for key authors in your field 3. Bookmark the major indexing services for quick verifications Good sources are much more than correct citations. Good sources are credible, current, and connected to the broader academic conversation. Until next week, my friends. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Downloads _This post is for paying subscribers only._ ### How to Prevent AI Hallucinations in Academic Work URL: https://lennartnacke.com/how-to-prevent-ai-hallucinations-in-academic-work/ Last updated: 2025-06-06T23:53:50.000Z I'm excited to bring you some awesome **Black Friday** best deal of the year offers this week before we get into this week's issue. Check out these amazing deals (and please use my affiliate links in this newsletter if you decide to purchase these). All codes are only valid Nov. 26 through Dec. 2. > Today’s newsletter is kindly sponsored by ​​[Paperpal](https://go.lennartnacke.com/paperpal?ref=lennartnacke.com)​​. ​​[Paperpal](https://go.lennartnacke.com/paperpal?ref=lennartnacke.com)​​ provides secure AI writing support, in-depth language correction, and a suite of pre-submission checks. Do your research, write, cite, edit and submit—all in one place. Use the code ​​[LENNART20](https://go.lennartnacke.com/paperpal?ref=lennartnacke.com)​​ to get a 20% discount all year. Or if you buy a license this week, the code ​[SAVEMAX](https://go.lennartnacke.com/paperpal?ref=lennartnacke.com)​ will be [auto-applied to save 40% on your purchase](https://go.lennartnacke.com/paperpal?ref=lennartnacke.com). --- AI is smart. But sometimes it lies. And if you’re using it for academic writing, those lies can sneak into your papers, leaving you with a polished, confident, but totally false argument. Kind of like those TikTok life hacks that just set your kitchen on fire. In this post, I’ll explore the phenomenon of generative AI hallucinations and how to combat them in academic work (note that I use generative AI and AI interchangeably here for reading ease). Some AI is as trustworthy as a phishing email saying you’ve won the lottery without using your name. AI can make misinformation into plausible content. So, let’s get to the root of the problem and learn how to keep your academic work clean, accurate, and trustworthy. ### The AI hallucination problem Every researcher who has experimented with generative AI tools has run into this: the AI confidently presenting false information as fact. Basically, Fox News dressed up as a Wikipedia article. It sounds good; it reads smoothly, but it’s just plain wrong. Why does this happen? Large language models like GPT-4 work by predicting what makes sense statistically—not necessarily what’s true. This leads to issues when they *sound* confident but are, in reality, just making sh\*&t up. The AI hallucination problem is especially tricky in academic writing. Academic writing requires accuracy, precision, and reliability. But AI hallucinations can introduce inaccuracies, imprecision, and unreliability into your work. You want to back away from that, like Gen Z, from a Facebook invite. In academic writing, you can’t afford to have your AI tool make stuff up. It is not insignificant when machines forget to be truthful. You might as well wear a Mentos jumpsuit in a Diet Coke swimming pool. So, how can you fight AI hallucinations in your academic work? Alright, let’s get down to business and look at the major challenges we’re facing with hallucination: 1. Generative AI will sometimes create **non-existent references** to studies, journals, or papers that do not exist. It fills in the blanks when it doesn’t have an answer, leading to completely fabricated but credible-sounding citations. Generative AI combines legitimate information with false content. A reference might start correctly but then dive into made-up details. It’s like putting salt in your coffee—a mix of good and bad ruins the whole thing. So, don’t ask Terrence Howard to do elementary algebra for you. 2. Generative AI can produce logical-sounding **false statements** that don’t stand up to scrutiny. Think of these as academic urban myths—they make sense on the surface but lack the grounding of real evidence. They can be hard to spot, especially if you’re not an expert in the field. 3. Generative AI systems may draw **faulty conclusions** from the training data, leading to inaccurate summaries of complex research. If you’re not careful, these incorrect interpretations could end up in your work. Everyone loves a good research summary, but you do have to check it like the milk that just expired yesterday. Always treat AI outputs as drafts. It is essential to verify the accuracy of the information provided by AI tools. This is especially true when it comes to academic writing. You need to be the fact checker and not think the AI output is the final product. ### Citation integrity One of the biggest challenges when using AI for academic purposes is managing citation integrity. Here’s how AI can trick you: Imagine you’re working on a paper, and the AI spits out a fantastic-looking reference. Except it doesn’t exist. Sometimes it’s even a name you recognize or the title of a paper you recall, but it’s all jumbled together. Careful here, because these citations do look real—they have a journal name, author, even page numbers. But they’re just a figment of the AI’s imagination. And if you include them in your paper, you’re in trouble. You’re basically saying, “I’m a serious academic, but my sources are from Narnia.” So, how can you ensure citation integrity in the age of AI? 1. *You need to be vigilant.* Always verify the references provided by generative AI tools. If the AI cites a study, check if it exists. If it doesn’t, you need to find a reliable source to back up your argument. The best way to do that quickly is [Consensus](https://go.lennartnacke.com/consensus?ref=lennartnacke.com). Consensus lets you quickly explore a research question (or even better, a simple yes/no question that comes to mind with your argument) and get a sense of the current state of the research about this. It’ll give you study snapshots, the full and correct citation, and a quick summary of the paper. Other tools that offer an AI literature review functionality like this are: [SciSpace](https://go.lennartnacke.com/scispace?ref=lennartnacke.com), [Scite AI](https://go.lennartnacke.com/get-scite-ai?ref=lennartnacke.com) and Paperpal. 2. *You need to cite consistently.* You can use reference management tools like [Zotero](https://www.zotero.org/?ref=lennartnacke.com) to keep track of your sources. Citation management tools let you organize your references, generate citations, and ensure that your bibliography is accurate and up-to-date. You need to understand the citation style you are using, like the laundry rules for your partner’s special sweater. Different citation styles have different rules for formatting references. Consensus outputs the most common citation styles like APA, MLA, Chicago, Harvard, and BibTeX. The latter can be quickly imported anywhere (including in your [Zotero](https://www.zotero.org/?ref=lennartnacke.com) library). 3. *You need to understand the context.* This is distinctly necessary when you’re using AI to find references. The AI’s references should be relevant to your argument. You can’t just throw in a study because you like the title. You need to confirm that it supports your argument and that it comes from a reliable source. If you don’t understand the context, you might end up citing a study that contradicts your argument. In other words, you need to be a responsible academic (and count red flags as if you’re on a date with a crypto bro). Never assume a citation is accurate until you find it yourself in the library or academic database. Example search index databases are: [Google Scholar](https://scholar.google.com/?ref=lennartnacke.com), [Scopus](https://www.scopus.com/?ref=lennartnacke.com), [Web of Science](https://www.webofscience.com/?ref=lennartnacke.com), [JSTOR](https://www.jstor.org/?ref=lennartnacke.com), [Semantic Scholar](https://www.semanticscholar.org/?ref=lennartnacke.com), [ACM Digital Library](https://dl.acm.org/?ref=lennartnacke.com), [APA PsycNet](https://psycnet.apa.org/home?ref=lennartnacke.com), [IEEE Xplore](https://ieeexplore.ieee.org/Xplore/home.jsp?ref=lennartnacke.com), and some field-specific others. ### Maintaining content accuracy Content accuracy is vital in academic research. You can’t afford to have your AI tool misinterpret data or misrepresent academic findings. Here’s why some generative AI tools get it wrong: Generative AI often outputs information that *sounds* right but lacks factual support. It’s not that the AI is lying maliciously; it’s that it’s merely following patterns without understanding real-world correctness. The AI can generate answers that seem logical because they align with the statistical relationships it has learned, but without any grounding in verified facts, these outputs can be misleading. This is where AI often falls flat on its face in academic writing, where you need to be able to trust your sources. It’s like relying on a charming stranger who knows how to use the right words but ultimately doesn’t have any genuine expertise on the topic. So, kind of like following a “thought leader,” I guess. AI can produce coherent, persuasive arguments that are, in fact, nonsense. This happens because it assembles pieces that statistically seem to fit without genuinely understanding the concepts. The AI mimics the flow of academic reasoning but lacks the true comprehension needed to form valid conclusions. The result is content that appears credible but crumbles under deeper scrutiny, as honest as a LinkedIn post about “hustle culture” written from a beach in Bali. After generating text with AI, perform your own critical review—check facts, logic, and the reliability of any claims made. Give yourself a reality check, just like when you look at your screen time report. ### Prevention strategies ### Input control The best prevention is precise input. AI performs better with better instructions. Be specific: if you ask a vague question, you get a vague (and likely incorrect) answer. Garbage in. Garbage out. Asking the AI specific questions with detailed instructions helps limit room for hallucinations. Including verified data also makes a difference—if you have legitimate data, provide it to the AI to reduce the chance that it tries to fill in the blanks inaccurately. Additionally, using specialized AI tools is key; general-purpose AI might know a little about a lot of things, but for academic work, you’re better off with AI tools designed specifically for research purposes. Kind of like when you’re using Consensus and an LLM like Claude, ChatGPT, or Gemini together. You could get the correct sources and summaries from Consensus and then tie their content together with elaborate prompting in an LLM. This way, you’re less likely to get a nonsensical answer. The more you provide context and clear directions, the more accurate the AI will be. At least it will feel more dependable than getting fashion advice from a colourblind penguin. ### Verification methods Verifying AI content is where human intervention really shines. Sometimes tech just can’t get its act together without you. You need to cross-check any output by systematically fact-checking each claim using reliable sources such as academic databases, fact-checking websites, or consulting experts. Cross-referencing is essential here—always use multiple sources to verify the validity of information. If an AI tool presents something as true, ensure at least two additional sources corroborate it. Additionally, consider using Retrieval-Augmented Generation (RAG) systems, which improve accuracy by linking AI outputs to specific, retrievable information. These reduce the chances of hallucinations. Treat AI like a first draft assistant. Like your new gym buddy, who wants to beef up but skips leg day. It’s useful, but you must apply the human touch to refine, verify, and validate it. ### Best practices for technical academic use To effectively use AI in academic work, adjust model parameters, cross-verify outputs, and establish a content validation workflow. - You can adjust AI settings to make it less confident in unknown areas. When the AI is set to lower “temperature,” it becomes less likely to make leaps into speculation. - Use different tools to check the output of one AI. If you have access to multiple AI systems, run the same prompt through each. Different answers show weak spots. - Establish clear workflows for content validation. For instance, always have one round dedicated solely to cross-checking sources. Use a tool like Consensus to quickly help you get to real sources. Lean on different tools and settings to find consistency. If two tools agree, you can usually trust the information a bit more. If they disagree, check the sources. This is a beneficial way to guarantee you’re not falling into the AI hallucination trap. ### What can you do next? 1. Run an academic query and then fact-check it. Identify any inaccuracies. Familiarize yourself with the kinds of errors your AI might make. 2. Create a step-by-step process for verifying AI-generated content—include steps like cross-referencing and using specialized databases. 3. If available, try using a Retrieval-Augmented Generation system to improve the quality of AI outputs. It will help make hallucinations far less likely. Generative AI is an incredibly powerful tool for research, but ultimately it’s only as effective as the vigilance you pour into it, so if you’re half-assing it, don’t expect miracles. Double-check facts and trust your expertise to prevent AI hallucinations from compromising your research. Stay sharp, fact-check thoroughly, and let AI be a helpful assistant. You have enough rogue co-authors already. ### Black Friday Deals for Subscribers Here are some Black Friday deals I have secured for this week. All codes are *only valid Nov. 26 through Dec. 2*: ### 40% off the [​How to Use AI Research Tools Webinar​](https://learn.lennartnacke.com/ai-academics-webinar-recording-a/?coupon=BFF2024&ref=lennartnacke.com) [​The webinar recording includes​](https://learn.lennartnacke.com/ai-academics-webinar-recording-a/?coupon=BFF2024&ref=lennartnacke.com): a 3-hour webinar video recording with subtitles in 19 different languages, 46 in-depth webinar slides (Google Slides and PDF), 1-hour ChatGPT Bonus Tutorial Video (+ 39 PDF slides with prompts), 3 Bonus App Tutorial Videos ([​Yomu AI​](https://go.lennartnacke.com/yomu-ai?ref=lennartnacke.com), [​SciSpace​](https://go.lennartnacke.com/scispace?ref=lennartnacke.com), Sourcely), 184-page Mastery Guide for 34 different AI Research & Writing Apps (PDF). 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[​Use code BFF2024 at checkout.](https://learn.lennartnacke.com/phd-thesis-fast-track-recording/?coupon=BFF2024&ref=lennartnacke.com) More deals below.ꜜ _This post is for paying subscribers only._ ### How to Tame Paper Revisions With a Review Matrix URL: https://lennartnacke.com/how-to-tame-paper-revisions-with-a-review-matrix/ Last updated: 2025-06-06T23:58:08.000Z Every researcher knows that sinking feeling when a paper comes back from review. Your heart races as you scroll through an endless list of comments, each one feeling more overwhelming than the last. Alone you may feel, but alone you are not. We've all been there. But let's not panic now. Instead of spending the next 3 hours jumping between 4 different reviewer comments, imagine you address every bit of feedback systematically in 45 minutes. Let me show you how. > Today’s newsletter is kindly sponsored by [​Paperpal​](https://paperpal.com/?linkId=lp%5F726731&sourceId=lennart-nacke&tenantId=paperpal&ref=lennartnacke.com). [​Paperpal​](https://paperpal.com/?linkId=lp%5F726731&sourceId=lennart-nacke&tenantId=paperpal&ref=lennartnacke.com) provides secure AI writing support, in-depth language correction, and a suite of pre-submission checks. Do your research, write, cite, edit and submit—all in one place. Use the code [​**LENNART20**​](https://paperpal.com/?linkId=lp%5F726731&sourceId=lennart-nacke&tenantId=paperpal&ref=lennartnacke.com)to get a 20% discount. ### **Make revisions simpler. Track every change. Tackle every comment.** Revisions can feel like wrestling an octopus. One comment here, another there, and suddenly you’re drowning in a sea of “minor edits” that are anything but minor. Especially when your paper’s just returned from peer review, and you’re staring down this Brokeback Mountain of feedback. What’s the best way to make sense of it all without it feeling like teaching a stubborn AI chatbot to understand sarcasm? Enter the paper review matrix. It's a straightforward system we use in my research group that turns chaos into a structured plan of action. Think of it like meal-prepping your paper revision. ## Join Write Insight Become a smarter researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Review, categorize, conquer Imagine you’ve opened your email and found the reviewer comments. Your first instinct might be to take your rage out on an unsuspecting pumpkin. But before you turn into Karen from accounting, finally snapping at the office printer. Hold back. Don't dive in and start making changes right away. Don’t. Not yet. Start by reading all the comments. If you're publishing at CHI, this means the overarching points from the associate chair (1AC) and the in-depth feedback from individual reviewers (including the 2AC committee member). Once you've taken it all in, it’s time to categorize. I know it's hard pretending to be Marie Kondo trying to spark joy in a Red Pill Reddit thread. So, I use a “paper review matrix,” a simple Google spreadsheet that acts as a tracker for every single piece of feedback. Here are how its columns work: - **Reviewer**: Label each reviewer (e.g., 1AC, Reviewer 1, Reviewer 2). This helps you quickly see which comments came from whom, making it easy to track both agreement and conflicting feedback. - **Issue Category**: For each comment, label it as an “easy fix,” “moderate change,” or “major revision.” This makes your workload visible. Not all comments are equal—some take minutes, others might need a week to address. This gives you a feel for it. - **Completion Status**: Add a “status” column to track what’s “not started,” “in progress,” or “completed.” This visibility means nothing falls through the cracks and your keep track of your revision. - **Section**: Show the section of the paper affected (e.g., introduction, method, discussion). It’s easier to focus when you can address all related comments in one go. - **Reviewer Comment**: Copy-paste the reviewer’s exact comments into a “comment” column. - **Notes:** Jot down some notes on how you plan to respond—whether it’s a rebuttal, a clarification, or a genuine revision. Just anything that comes to mind here is fine. - **Rebuttal or Response:** Pre-draft your actual full rebuttal or response paragraphs here. Organizing your reviews like this doesn’t just make the task manageable. It turns a daunting overhaul into a nice checklist. Instead of drowning in general criticism, you get a plan. And plans are powerful (like eating Hot Cheetos at 3 AM knowing full well you have acid reflux). ### Turning feedback into action Once you've categorized all the comments, it’s time to create an action plan—concrete steps to transform feedback into a polished draft. 1. **Set priorities by effort level**: Start with the easy fixes. There’s a psychological win in crossing off a few items quickly. These are your warm-up. Then, tackle the moderate changes. Save the full Kardashians—the major revisions—for focused writing sessions where you’re ready to dive in with a queer eye for the straight guy. 2. **Track each comment by section**: If your reviewers ask for changes across multiple sections, it’s helpful to address them by grouping. For example, tackle all feedback related to the method section in one focused go. You to maintain a clear picture of how the section evolves as a whole while you revise like this. You're basically plotting the Marvel multiverse but with sticky notes. You get what I mean. 3. **Know when to rebut**: Not every reviewer comment is gospel. Some are just the devil's gossip. And when you disagree—maybe they’ve misunderstood something that you can clarify, or their suggestion doesn’t fit your paper’s argument. That's fine. Let them know. Push the ignorance out. Use the “response” column to draft concise rebuttals, providing data or logic to support your decision to leave something as is. A good pushback is a humble push forward. With a structured review matrix, you turn every comment—even the tough ones—into actionable items. No more feeling overwhelmed, just clear steps from critique to completion. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Building your own review matrix ![Notion paper matrix screenshot.](https://lennartnacke.com/content/images/2024/11/Notion-Paper-Tracker.png) This is how such a paper matrix will look like. (Download for paid members below.) To create your own review matrix, start with a simple spreadsheet (sure, I'll drop my Notion template for my Substack paid subscribers, too). Here’s what it should include, column by column: - **Issue Number:** For organization - **Reviewer ID**: Assign names or IDs to reviewers. - **Issue Type**: “Easy fix,” “Moderate change,” “Major issue.” - **Paper Section**: “Introduction,” “Methods,” “Discussion,” etc. - **Completion Status**: “Not started,” “In progress,” “Completed.” - **Actual Comment**: Copy-paste the feedback here. - **Your Notes**: Are you making the change? Are you rebutting it? - **Rebuttal or Response:** Your actual response - **Ready?** Check off when completed. Filter. If you can, colour-code by issue type—visual cues can make a big difference when you’re trying to get a quick handle on where things stand. A spreadsheet like this might seem basic. But, it's priceless for handling revisions. It stops you from losing track of what still needs to be done. ### How to tackle revision feedback today 1. **Create a review matrix template**: Open Excel or Google Sheets and build a template. Structure it like I outlined above. Label your columns clearly. 2. **Read all feedback first, then categorize**: Before touching the paper, categorize each comment. This prevents knee-jerk reactions and gives you a map to navigate through the work ahead. 3. **Rank effort by issue severity**: Complete easy fixes first, then moderate, then major. Working in chunks keeps motivation high and prevents burnout. 4. **Track completion visually**: Use status markers and colour coding—turn each cell green when it’s done. Progress is easier to see when it’s colourful. 5. **Rebut intelligently**: Push back on unreasonable comments. If it’s a matter of misunderstanding, take the time to explain. Your voice matters as much as the reviewers'. Tackling reviews doesn’t have to feel like trying to match socks in the laundry during a power outage. With a solid system, you can take control of the process. Every comment becomes another line in your review matrix, and each line is another step toward submission. Like your paper finally moving out of R2's basement. Start building your tracker today and turn that revision into a simple, step-by-step checklist. Best of luck with your CHI revisions if you're publishing in my field. ## Download the Matrix Here is the link to the Notion paper matrix ready for you to copy over: _This post is for paying subscribers only._ ### How to Maintain a Consistent Paper Reading Schedule URL: https://lennartnacke.com/how-to-maintain-a-paper-reading-schedule/ Last updated: 2025-06-07T00:41:28.000Z Writing a dissertation, reading 10x your body weight in journal articles, and somehow remembering to feed yourself — grad school can feel like trying to stack Jenga blocks in a ‘System of a Down’ mosh pit when ‘Chop Suey!’ comes on. The results are about as graceful as the “Love is Blind” reunions. After mentoring hundreds of graduate students through their doctoral programs, I’ve noticed a pattern: the ones who do really well aren’t necessarily the smartest — they’re the ones who master their reading strategy early. So, let’s talk strategy today. Today, let’s figure out how stick to a consistent reading and review schedule more than you stick to your New Year’s resolutions in February. Spoiler: It’s simpler than your cat’s plans for world domination. It boils down to your method. No whimsical systems or impossible regimes. Let’s give you some actionable, realistic strategies to keep you on track. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### The planner plans while the unplanned perish Pre-reading organization is like victory visualization before a sports match. Vital to all, invisible to most. Start your semester by mapping out the literary landscape. Grab your research questions, get an idea of your semester workload. Then, sketch out a master reading list that shows the peaks and valleys of your workload. Heavy weeks? Lighter ones? Plot it all out. You don’t need to conquer everything. But. you have to anticipate when things get hairy and act early. Organize readings into meaningful clusters. Got a bunch of theoretical papers, and then empirical ones that apply those theories? Group them. Reading theory before you hit the data helps build a foundation. Just like reading the group chat drama before you have brunch with the girls. Light bulbs will flash instead of fog rolling in. Hey, I finally understood the plot of Tenet, too, after my third coffee. **Tip:** Colour-code your reading list for intensity. Green weeks? Relaxed reading. Red? Brace yourself and maybe buy some chocolate for those nerves. ### Strategic reading When you approach a reading, don’t go linear. A research paper is text. Yes. But it’s not a novel. It’s more like Tetris. And you’re shooting for hero blocks not teewees or smashboys to score a combo. Start with the abstract, introduction, and conclusion. Then, skim through headings, figures, tables, and subheadings like you’re previewing scenes in a movie. These steps help you decide if this is a “deep dive” reading or a “strategic skim.” You can’t read them all, friend. Write four-word summaries in the margins. It doesn’t have to be pretty — just concise. For example, “Hypothesis: productivity impacts,” or “Result: weak correlation exists.” If something seems controversial or begs a question, note it. These are the golden nuggets for discussion sections and grad seminars. **Tip:** Keep a pen and sticky notes nearby to jot down “contribution” questions. Having a few questions ready will make it easier to return to the paper. ### Clever hands do more than tired ones Weekly planning and daily execution are where consistency happens. Start with a weekly plan: Divide 2–3 hour focused study sessions during your peak productivity hours. Block them out as non-negotiable in your calendar. We call this time-blocking. Treat these sessions like dentist appointments. If you don’t go, don’t blame anyone but yourself if your teeth fall out. On a daily level, do a little something for each paper. Even if it’s a 15-minute skim or reviewing notes. Think of it as chipping away at a wall — small dents make a difference in the end. **Tip:** Mark lighter reading days ahead of a tougher schedule. Give your brain time to rest — it retains information better when it’s not overloaded. ### Each word is wisdom, each line is learning, each page is power Note-taking is an active sport. Write down questions as you read. Why did the author choose this method? How could this theory extend to another field? These are mental trampoline parks for your shower thoughts. If you’re a visual learner, try converting key points into diagrams or flowcharts. There’s something about seeing ideas in spaaaace (Anyone remember Wheatley from Portal 2?) that makes them stick better. You’ll also find that connections between different readings start to become clearer. At the end of each week, spend an hour compiling. Make summary sheets for each major reading and review these before you get back into writing. Don’t let all that effort get buried — summaries will save you time and mental strain. **Tip:** Digital tools like Notion or Obsidian work wonders for creating interconnected notes — link similar topics across different readings. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Harmony needs a bit of hustle Balance demands labour, labour yields wisdom, wisdom creates harmony. You’re not going to get through every word of every paper you’ve picked. And that’s okay. Grad school is not about being a reading machine. It’s about picking the cookie dough chunks out of your Ben & Jerry’s ice cream. Learn to skim you must, and when to investigate with care you’ll learn, young padawan. (Thanks, Yoda.) Set boundaries for yourself. Treat your reading schedule like a job. Say what now? Yes, you wouldn’t work without a break. Same here. Schedule at least one reading-free day a week. Dare to close those browser tabs. Let yourself recalibrate. Take a power nap with that mood ring. Your brain processes complex information best when it’s rested, not when it’s maxed out. Reward yourself a little for sticking to your reading goals. Completed an article drier than Deadpool’s humour? Watch that Selling Sunset episode. You’re training for mental endurance here, and sometimes your brain needs cotton candy. Don’t feel guilty. **Tip:** Adjust as you go. If one strategy starts feeling stale, mix it up. The best schedule is one that evolves with you. ### What to do today 1. **Create a semester reading map.** Mark out heavy and light weeks, and group related readings together. 2. **Set a weekly reading ritual.** Block out 2–3 hour slots during your best hours for deep reading. 3. **Start a “discussion questions” sticky note or scrap paper.** Write down interesting points or questions for the papers you will read. Revisit as you read. A reading plan in grad school is about showing up, week after week, and improving how you approach the workload. Build systems that make consistency easier. Let hungry pages feed your mind. Ink and knowledge pave the way to critical thought. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Seven Ways to Keep AI Honest URL: https://lennartnacke.com/7-ways-to-keep-ai-honest/ Last updated: 2025-06-07T00:47:03.000Z Writing used to be simple. You sat down, typed words, and hoped they made sense. Then generative AI waltzed in like Ron Burgundy, pretending to know everything—and often knowing nothing. Some praise it as academic writing’s saviour; others fear it’ll destroy scholarly credibility. Both miss the point. It’s like watching a cat ride a Roomba—it’s definitely going somewhere, but nobody’s quite sure where that is. The real question isn’t whether to use AI, but how to use it without losing your academic soul. Thing is, you have to adapt now as quickly as your AirPods switching between your Apple devices. If you treat AI like a helicopter parent who’s doing all your homework, you’ll feel guilty about it, eventually. There’s a better way to use it. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### **The problem murmurs not inside the tool** Remember typewriters? Researchers once argued they’d ruin handwriting forever. (Caveat: They did and also didn’t.) But we adapted. AI tools are different, yet somehow the same. They’re not destroying academic writing. They’re changing how we think about authenticity. We don’t want our papers to end up like boy bands from the 90s—different names, same dance moves. Think of AI like a junior research assistant: brilliant but sometimes confused, helpful yet occasionally misleading. You wouldn’t let your RA write your paper alone. Don’t let ChatGPT do it, either. ### **1\. Write first; improve later** Your first draft should flow from your brain, not an algorithm. Start with your raw ideas — messy, incomplete, human. For example, I either jumble unfiltered text together and scramble it into on giant mess, or I talk to [​Otter AI​](https://go.lennartnacke.com/otter?ref=lennartnacke.com) to transcribe my thoughts perfectly (it is the most accurate transcription software I know), and then I have something to copy over into the LLM of my choice as part of a my mega prompt or my many GPTs, Gems, or Claude projects. If I don’t have much to say, I just outline what I want to discuss before I get started in headings (or use [​Paperpal​](https://go.lennartnacke.com/paperpal?ref=lennartnacke.com)'s outline function and refine it based on what I want to write about). Then, you can use more AI tools after you’ve mapped your argument. This creates a foundation that’s unmistakably yours. Another easy example: Draft your methodology section details by hand. Then use AI to check for clarity, not to generate content. In a **prompt**: ``` Make this sound cohesive, clear, concise, and compelling. ``` This maintains your authentic voice while using AI’s strengths. Write with your mind, write with your heart, write with your voice. Your ideas guide the structure. Your expertise shapes the content. **Pro Tip:** First, set a timer for 25 minutes. Write or speech-to-text without AI. This creates a pure first zero draft that reflects your genuine thinking. Take it from there. ### **2\. Document every AI interaction** Create an “AI appendix” in your writing process. Record every prompt, every generated response, every edit. This is good practice, but, especially if you’re in undergrad studies, it is also academic survival. Just like citations to previous literature to avoid plagiarism. You need to cite your AI interactions to avoid academic dishonesty. This is your digital paper trail. It shows how you used AI, when you used it, and why. When someone questions your work’s authenticity (and they increasingly will), you’ll have solid evidence. In most courses, where you can use generative AI for your writing, you will have to meticulously document its use. It’s good practice to get into this early. Just your academic insurance policy. And it needs to be more stable than a Jenga tower in an earthquake. **Pro Tip:** Keep a simple spreadsheet handy with columns: Date | Tool Used | Purpose | Outcome (see download below). Like a lab notebook, but for your AI interactions and outputs. If you can include URLs to your chat transcripts, even better. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **3\. Check, double-check, human-check** AI makes mistakes. Big ones. Spectacular ones. It invents citations, mangles statistics, and occasionally writes nonsense with supreme confidence. Don’t trust it. Check every AI-generated sentence. Verify everything. Trust nothing without human oversight. AI is a tool, not a replacement for thinking. Don't put on training wheels when you're heading down a mountain bike trail. It’s your paper’s integrity that's on the line. So, you must read every word, sentence, and paragraph. Use it as your final quality control. You’re the expert. You’re the author. You’re the one responsible for your work. Each AI-touched section needs three levels of verification: 1. **Technical accuracy** (Are the facts right?). You must check the statistics (which you should still do yourself), the methodology (which you might have enhanced), and the results (which are most important). So, you must verify the AI-generated content against your project knowledge. 2. **Source validity** (Do these citations exist?). This means checking the references and the citations. Really, you should have done this before you used AI. But, now you must do it again. You must ensure the AI-generated content matches the source material. Don’t trust AI’s citations. Ever. It mixes real papers with imaginary ones, real quotes with generated text. Treat every AI-suggested reference as suspect until verified. There are specific tools you can use here ([​Consensus​](https://go.lennartnacke.com/consensus?ref=lennartnacke.com), [​Scite AI​](https://go.lennartnacke.com/get-scite-ai?ref=lennartnacke.com), [​SciSpace​](https://go.lennartnacke.com/scispace?ref=lennartnacke.com), Scopus AI, all let you work with the literature and find real references). 3. **Logical coherence** (Does this make sense?). You must read the AI-generated content in context. Are the arguments logical? Do the claims follow from the evidence? Does the writing flow? **Pro Tip:** Build a “verification checklist” into your writing process (see download below). Check one citation per paragraph immediately after writing. This catches problems early before they multiply. ### **4\. AI is the scaffolding while you pour in the soul** AI excels at organizing ideas, spotting structural weaknesses, and suggesting transitions. Use it there. Let it help arrange your thoughts—but not think them for you. Feed it your outline and ask: “What’s missing?” “Where might a reader get lost?” “Which sections need more support?” You can even use it as a sparing partner for your argumentation and discuss the paper's findings with it to help you strengthen your rhetoric. Trade those intellectual jabs like Pokémon cards at recess. Or use AI to outline your paper, then fill in the content yourself. Or use a AI to complete sentences while you write other ones. It's like passing the mic during "Sweet Caroline" at karaoke night; you should be having fun with it. Treat your writing as a conversation. It's not a monologue, Marcus Antonius. But keep it away from your core results and implications for now. Let those come from your expertise, your research, your understanding. AI can help you explain your ideas. It can probably even get a rocket ship to parallel park. And when prompted correctly even find arguments for your results against existing literature. But you will have to judge whether this has legs. It's just like looking at IKEA instructions and deciding if they work for you. **Pro Tip:** Write your main arguments on paper first. Then type them up. This physical-to-digital pipeline ensures your core ideas remain purely yours. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### **5\. Keep editors in the loop** Tell your editors which AI tools you’ve used. How you’ve used them. Why you’ve used them. Actually do it. Like a person who uses their gym membership in January. This transparency builds trust and helps establish best practices. Have a declaration section in your paper. If you’re using AI to generate content, say so. If you’re using AI to check grammar, say so. If you’re using AI to improve readability, say so. If you’re using AI to find references, say so. Share this with editors before they ask. Jump on that responsibility like a Wordle player on a 1/6 streak. Being proactive shows professionalism and establishes new standards as we all figure out this new writing world together. I'm sure soon, it'll just be part of the process. But for now, it prepares you for the inevitable questions about AI’s role in your writing. It’s better to be upfront than defensive. **Pro Tip:** Create different disclosure templates for different types of work (research papers, grant proposals, book chapters). Each might have unique AI guidelines attached to it. ### **6\. Set your boundaries in stone** Decide what you won’t use AI for. Make these rules non-negotiable for yourself. Mine? No AI-generated methods, analysis, or results for now. These are where your expertise must come through. These boundaries protect your academic integrity and ensure your expertise shines through. They also keep AI firmly in its place where it can support you the most. But to be honest, I wouldn’t be surprised if emerging tools and practices would even facilitate those aspects of your research in the future. **Pro Tip:** Write these boundaries down somewhere. In your Apple Notes, Obsidian, Notion, or whatever tool you use to keep track of things like this. Review them before each writing session to stay focused. ### **7\. Stay human-centric** Your academic voice is your fingerprint. It’s unique. Valuable. Irreplaceable. AI should amplify your voice, not replace it. Think of it like autotune for your brain. A backup dancer who is not stealing the spotlight from the main singer but making the whole show unforgettable. Sure, you can copy your style and clone it. But ask yourself: “Could I explain this paper’s key points without using AI?” If not, you’ve maybe drifted too far from your academic core. Test each section: Does it sound like you? Does it reflect your thinking? Does it demonstrate your expertise? Don't let your personal assistant steal your identity. **Pro Tip:** Record yourself explaining your paper’s key points. Transcribe it. Compare this natural explanation to your AI-enhanced written work. The closer your argument flow matches, the more authentic your voice remains. Remove things that sound wrong. ### **What to do right now** 1. Audit your current AI use. List every tool and how you use it. Keep tabs on it like Swifties tracking Taylor's private jet. 2. Create your AI appendix template. Start documenting every interaction. 3. Write your personal AI boundaries. Make them as specific as your Starbucks orders and stick to them. 4. Draft your AI disclosure statement for your next submission. Create templates from it. 5. Set up a verification system for AI-suggested content. Use a checklist. Academic writing isn't like ghosting AI; it's like sending a connection request on AI's LinkedIn profile and sliding into those DMs professionally. These seven rules put AI’s power to work without sacrificing your academic integrity. Start with one rule today. Master it. Then move to the next. Repeat. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Downloads Use my example of this AI interaction tracking spreadsheet below. Also, download my ethical checklist (duplicate to Notion and as PDF) for AI-generated content that will help you assess your article. Here are your downloads: _This post is for paying subscribers only._ ### How to write papers with clearer communication URL: https://lennartnacke.com/how-to-write-papers-with-clearer-communication/ Last updated: 2025-09-04T18:44:12.000Z Want to know why most academic writing falls flat? Spoiler alert: it’s not the research, it’s not the methods, and it’s not even the topic. It’s the words you use. Maybe you’re trying to explain quantum physics to a pumpkin on Halloween night. But the pumpkin isn’t having it. Just like the vampire in this picture. Happy Halloween, my friends. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) Too many junior academics make these critical mistakes: - Hiding behind passive voice like you’re a chameleon blending into the fall leaves. - Writing sentences longer than entire paragraphs. - Writing to sound impressive rather than clear. - Using jargon that no one understands. - Stuffing the text with empty phrases. The result? Readers who give up halfway through. Reviewers who recommend rejection. A wasted effort that ends in frustration. A ghost story that drags on until dawn. So, don’t sport garlic breath when you’re trying to charm a vampire on a date. Here’s the reality: simple words win. The best academic writers? They don’t hide behind complex language. They write deliberately, cut out hollow phrases, and make certain their ideas radiate clearly through their text. Let’s explore why clear language matters in academic writing, and how you can start choosing better words today. ![](https://lennartnacke.com/content/images/2024/10/image-2.png) ### Clear writing is smart communication Imagine this: you open a new journal article. After five minutes, you’re already feeling like a surfer dude at a poetry slam, neck-deep in phrases like “quantitative elucidation of multifactorial dynamics” and “integrative, synergistic framework” — words that sound impressive but ultimately mean Sweet Fanny Adams (that’s Australian for nothing at all, diddly squat). You close the tab. You feel frustrated. You’re not alone. ## Sign up for Write Insight Become a smarter researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Many writers confuse complex writing with intelligent writing. But clarity isn’t the enemy of complexity. No, clarity is like your brain’s VIP entrance to a knowledge party. Einstein didn’t need complex abstract examples to explain relativity. His most famous thought experiment connected a moving train and lightning strikes. Darwin introduced evolution with plain English. Feynman could explain quantum physics to a high schooler. They all used simple language to convey complex ideas — and they did it because they knew that the true measure of mastery is communication. The best academic writing doesn’t try to sound smart like a dictionary doing CrossFit. You might as well cosplay on Halloween as a walking thesaurus. Its goal should be for you to understand it. Clarity. Vary your sentence structure to keep your readers engaged. Paragraph after paragraph of ten-word sentences will lull them to sleep faster than a Christmas dinner. Instead, mix up sentence lengths. Start some sentences with short, punchy subjects. Then follow them with longer, more complex clauses. This creates a natural rhythm that guides the reader through your ideas. The variety of sentence structures keeps the writing dynamic and engaging. Next time you write, imagine explaining your work to a friend who’s smart but outside your field. Put some training wheels on your thought process. Use plain language that doesn’t require a translator, and hold their hand a bit through your logic jungle. If your sentences are getting you funny looks, simplify them. ### Use clear, specific words instead of jargon Here’s what I’ve learned after editing over 200 papers: most of the jargon that fills up academic writing can’t stand up to a closer look. Words like “utilize” can always be replaced with “use” — and they should be. Otherwise, you’re writing elevator music. Phrases like “the extent to which” are just filler. They’re mental bubble wrap that keeps the real ideas from bouncing around. They say nothing. Zilch. Zip. Nada. Academic writing should be crisp and efficient. You want chopsticks, not a catapult here. Sure, some technical terms (i.e., jargon) are necessary at times, but they should be few and far between. When you reach for a fancy word, ask yourself: Is there a simpler word that says exactly the same thing? If so, go with the simple one. The problem isn’t just readability. More often than not, it’s accuracy. Big, vague words leave too much room for interpretation. They make it hard for readers to understand what you’re really saying. Worst of all, they even make it harder to replicate your work. Instead, use words that do the heavy lifting. Replace vague verbs with specific ones. “Investigate” becomes “measure” or “test”. “Shows” becomes “reveals” or “indicates.” Clear words paint a clearer picture, and they make your arguments stronger. For example, instead of writing “The results underscore the importance of effective strategies,” write “The results show that targeted strategies reduced error rates by 25%”. More specific, more powerful. Think less fortune cookie and more Google Maps. Use a thesaurus to brighten it up with sharper, more accurate terminology. Identify the jargon, and replace it with more concrete language. Write words that you would high-five. Put some boots on those words. Talk to the reader as if they’re a squirrel looking for their nuts. If a word doesn’t contribute, delete it. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Abandon the passive voice Avoid passive voice like a date with a vengeful ex-lover. Many writers think the passive voice sounds more “academic” — more objective, more formal. But all it does is put distance between your research and your reader. “A significant effect was found” makes readers wonder: *by whom?* Don’t play hide and seek with your readers. The passive voice hides agency. It muddies meaning. Instead, make your writing more direct. Put the subject first, and let the reader see the action happen. Instead of “The hypothesis was supported by the data,” write “The data supported the hypothesis.” It’s cleaner, more concise, and it keeps the focus on your actual findings. Don’t put your meaning in witness protection. As we all use generative AI to write everywhere, our words are already having an identity crisis. When you use active voice, you take ownership of your research. You make it clear who did what. Instead of “Data were collected over a three-month period,” write “We collected data for three months.” Yeah, you did that. Good on you. It’s sharper. It’s clearer. It makes your writing come alive. Like Mike Myers at the end of each Halloween movie. 👀 There’s a time and place for the passive voice — like when the actor is unknown or unimportant. But in general, it’s a lazy crutch that weakens your writing. Cut back on it. Marie Kondo those sentences. And instead, do some linguistic Kung Fu. Mark up every instance of “was” or “were” in your draft. Rewrite as many as you can to show who did the action. You might be surprised how much flabbier writing hides in those passive constructions. Get your sentences a gym membership. (And lay off the adverb donuts, too.) And then, hit the editor workout. ### Practical steps to improve your writing Ready to make your writing stand out? Here are five steps you can take today to start using better words in your academic writing: 1. **Read your work aloud**: Let text-to-speech software read your draft back to you. If you stumble on a sentence, your reader will, too. Simplify it. 2. **Slash the fillers**: Words like “somewhat,” “very,” and “likely” often don’t add value. Delete them unless they change the meaning. 3. **Use strong verbs**: Avoid vague verbs like “affects,” “deals with,” or “shows.” Use more specific verbs that paint a clear picture. 4. **Shorten sentences**: Long, winding sentences lose readers. Keep each one to a single thought. If it’s getting too long, split it up. 5. **Keep the reader in mind**: Your job is to clarify your work to others. If someone has to reread a sentence three times, you haven’t done your job. Put these five steps into practice for your next paper. Watch how your writing improves, and notice how much more engaging your ideas become. ### Clear writing drives reader engagement Think about the most cited papers in your field. The ones that pop up again and again when you’re doing your literature review. Chances are, they’re the papers that are written clearly. That’s not an accident. Clarity drives engagement. If your audience can understand your work, they’ll engage with it. They’ll cite it, apply it, critique it. If they can’t understand it, they’ll move on to something they can. As a researcher, your job is to push the boundaries of your field — but if you can’t communicate what you’ve done, you’re not making an impact. Your job isn’t to impress with big words. It’s to guarantee your ideas are understood, remembered, and used. Don’t hide behind fancy language. Use words that tell your story simply and powerfully. The world deserves to understand your research — so make it understandable. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### What to do next Download and print out the 3-page PDF of my **Writing Improvement Checklist** with 61 items to remember when writing your paper: _This post is for paying subscribers only._ ### Try these 9 lit review questions URL: https://lennartnacke.com/try-these-9-lit-review-questions/ Last updated: 2025-09-04T19:34:11.000Z Every researcher faces it: the ocean of information. It demands reading, synthesizing, and turning it into a thorough review. You open papers. Read pages and pages of explanations. Before you know it, you’re knuckles-deep in a fifty-page document. And what do you have to show for it? A series of haphazard summaries that do little more than echo what others have said. Think of your literature review as the cluttered garage of Tony Stark. Sure, gadgets are everywhere, but without an organizing system, you can’t find your Mark III suit when the world needs saving. Bummer. Enter the 9-question literature review framework. It’s my trusty sidekick. I used it to take a 50-page mess and make it a 10-page juggernaut worthy of Stark Industries. Take that, Nick Fury. You can ask these questions for each paper you review in your literature corpus. ![](https://lennartnacke.com/content/images/2024/10/image.png) Here’s why it’s different: You must understand the structure rather than getting bogged down in the details. Assessing your literature review is like debugging code. Understanding the system architecture lets you fix individual lines or modules much easier. It’s about asking the right questions to get answers and gets you a map to guide you through the literature. Let me show you how. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **1\. What has been done?** Let’s start with the simplest question—what’s already been done? This is about surveying the field and mapping out what’s covered. The mission? Draw those lines so clearly even a blindfolded raccoon couldn’t miss them. We need to know which studies form the foundation and which are more like passing trends—flash in the pan. You must distinguish between the two to understand where to focus your efforts for lasting impact. Imagine it’s like preparing a massive Chinese lunch buffet—some dishes are timeless staples, like Ramen (saves you budget, not calories, my friend). In contrast, others are just trendy fusion experiments, like that infamous pickle-flavoured ice cream that might not make it to the table next time. Your task is to figure out which ones deserve a permanent spot in your recipe book. Start by scanning abstracts of relevant studies. Zero in on the juicy bits—the key areas researchers have already poked and prodded. Identify foundational studies versus more recent, less impactful trends. Grab your pen and start scribbling. Jot down a summary of each major work, focusing on the broad contribution it has made. Consider it your personal mission to decipher the magic that turns these simple words into cultural earthquakes. ***Ask:*** - Which studies are most frequently cited, and why? - Are there any major debates or disagreements in this area? - How has the understanding of this topic evolved over time? 🌟 ****Pro Tip:** Scan abstracts to identify the scope of what has already been covered before diving deeper. This saves you time and energy. ### **2\. What were the hypotheses?** Every groundbreaking study kicks off with a spark — a problem begging for a solution. So, let’s cut to the chase: what was this study trying to blow out of the water? When you do this, you start to draw connections between different works — you’re basically cracking the Da Vinci Code. Is everyone investigating the same thing from different angles, or are there conflicting ideas at play? Picture it like a group of Hogwarts students brewing potions: most are making a simple Forgetfulness Potion, but one cheeky student throws in a dash of powdered dragon claw. Are they all aiming for the same potion, or is there an unexpected brain boost in the making? Who doesn’t love a good plot twist? Go full detective mode. Read the introduction of each study to identify the stated hypothesis. Scan the literature to understand the big questions driving the research in this area, the hypotheses being tested, and whether there are any conflicting ideas or approaches at play. Summarize each hypothesis in one clear, concise sentence. Compare hypotheses across different studies (from the related literature, either in the paper or in your Zotero library) to identify common themes or conflicts. If a paper doesn’t have hypotheses, jump right to the next point and focus on the research questions. ***Ask:*** - What assumptions underlie each hypothesis? - Are there any surprising or unconventional hypotheses? - How do different hypotheses build on or contradict each other? 🌟 ****Pro Tip:** Write down each hypothesis in a single sentence (or ask your favourite LLM to simplify the hypothesis from the paper in plain language). This simplicity makes it easy to compare different studies and spot where disagreements arise. ### **3\. What were the research questions?** Hypotheses might be the spine holding it all together, but let’s be real — research questions are the brawny muscles flexing the whole operation. Without them, your paper is just a skeleton in a lab coat! These questions shape the study and determine where the researchers will focus. Let’s say you’re building a treehouse. The hypotheses are your vision and design, but the research questions are where you need to roll up your sleeves and get to work — do you need to reinforce the base, check for rotten wood, or figure out the perfect leaf-proof roof? Dive into the introductions and methods sections to uncover the specific research questions driving each study. Catalogue every last one, then play connect-the-dots with the similarities, spot the oddballs, and target those glaring gaps. Where are researchers focusing their efforts, and where are the white spaces waiting to be filled? ***Ask:*** - Are the research questions specific or broad? - How do the research questions align with the stated hypothesis? - Do the questions push the boundaries of current understanding, or are they more confirmatory? 🌟 ****Pro Tip:** Create a table to list out the research questions from each key study, making it easy to spot patterns, gaps, and conflicts. ## Sign up for Write Insight Become a smarter researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### **4\. How was the work done?** Figuring out the methods is fundamental. Without it, you’re just a lost moose wandering around in a Canadian blizzard. Was the study based on a lab experiment, a survey, a meta-analysis? Did they gather primary data or depend on existing datasets? The methods tell you what weight to put on the findings. Don’t bet on a three-legged racehorse. Flimsy methods often lead to unreliable conclusions. And that’s like handing out Monopoly money in a real-world economy. Comb through the methods sections for details on the study design, data collection, and analysis. Get granular—sample size, measurement techniques, statistical tests, everything. Draw connections between the methods and the research questions. Find the studies that ruffle some feathers because innovation doesn’t come to those who sit on the sidelines. ***Ask:*** - Were there any major limitations acknowledged by the authors? - How well does the chosen method fit the research question? - Could different methods have led to different outcomes? 🌟 ****Pro Tip:** Tabulate the methods used across studies to quickly identify commonalities and outliers. ### **5\. When was it done?** When a study was conducted tells you a lot. Even a good study can age faster than a snowball in July. Ensure that the findings you’re considering are more relevant than a flip phone in a 5G smartphone world (yes, I see you there, Samsung Galaxy Z Flip 6, stop being such a clamshell). A 1995 study might as well be vintage clothing (in some cases). Check the publication dates and, if available, the data collection timeline for each study. Look for any major events, discoveries, or technological shifts that may have impacted the research field during that time. Those factors could’ve steered the research like a rogue hockey puck sliding across the ice. Skate to where the puck is going to be, not where it has been, Wayne Gretzky. ***Ask:*** - How has the timing of the study influenced its findings? What were the technological or social contexts at the time? - Were there any significant technological advancements that might have impacted the research? - How do historical or social contexts at the time of the study influence its relevance today? Are the findings still relevant? 🌟 ****Pro Tip:** Chronology helps you see the progress of knowledge. Older studies may provide the roots, but newer studies often adjust or build upon them. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **6\. Who did it?** This question is about uncovering the brains, the institutions, and even the money bags behind the curtain. Investigate who is pulling the strings, writing the cheques, and calling the shots. These often influence how a study is perceived. An industry-funded study might be perceived as a fox guarding the henhouse and needs more scrutiny. Identify the main authors and their affiliations, noting any institutions or funding sources (or hidden agendas). Consider whether the researchers or sponsors have any potential biases (i.e., are they playing for the home team or in someone else’s pocket?). Reflect on how the credibility and background of the authors might impact the interpretation of the study. Trust me, you’ll want this intel before you buy into what they’re selling. ***Ask:*** - What are the backgrounds and qualifications of the researchers? - Are there any affiliations or funding sources that could introduce bias? - How might the authors’ previous work influence their perspective on this study? 🌟 ****Pro Tip:** Reach out to the authors if you have follow-up questions or want to hear their take on the current relevance of the study. ### **7\. What were the main findings?** The meat (or potatos if you’re a vegetarian) of the literature—what did the researchers find? What matters isn’t just the results but how they fit in with other work in the field. Do they back up the old research, flip it on its head, or toss in some spicy new twists? Make those discoveries work for you and see if they corroborate, contradict, or add new insights to the existing body of research. Read the results and discussion sections for the key takeaways. Here’s where you’ll find the juicy details, the gritty truths, and the revelations that’ll make your brain cut a rug with happiness. Summarize these findings in your own words to understand them. To retain them better. Sure, numbers are solid, easy to stack up and compare, but if you’re ignoring qualitative insights, you’re missing half the bloody picture. Think of it like appreciating a painting by only counting brushstrokes. That’s no fun. Synthesize the major patterns, look for outliers, and focus on the most impactful discoveries. Compare these results with those from other studies to identify commonalities and differences. ***Ask:*** - Are the findings consistent with those from other similar studies? - What unique insights or contributions do these findings provide? - Are there any results that seem surprising or counterintuitive? 🌟 ****Pro Tip:** Distill key findings into a concise table to enable quick comparisons across studies. ### **8\. What were the conclusions?** Wrapping up a study is like serving a meal with both the juicy steak (i.e., findings with impact) and the overcooked veggies (i.e., the limitations). Sure, we’ve got the wins, but let’s not pretend there aren’t some cringe-worthy fails too. Be careful not to accept everything at face value—look for caveats or areas where the researchers admit uncertainty. Read the conclusion section carefully to understand the final takeaways. Think of it as the espresso shot of this whole experience: short, potent, and absolutely necessary to grasp the big picture. Wipe off the fairy dust and check whether the conclusions logically follow from the findings presented in the study. Make sure the findings have legs. ***Ask:*** - Do the conclusions logically follow from the findings? - Are there limitations that weaken the conclusions? - How might the authors’ interpretations differ if certain limitations were addressed? 🌟 ****Pro Tip:** Keep an eye out for overreaching conclusions that go beyond what the data can support. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **9\. What should be done next?** The final question is the most exciting (not just for the paper but for yourself, too). You’ve made it through the gauntlet—now it’s time to turn your sights to the future. Picture this: a vast wilderness of research, just begging for a brave soul like you to explore it. Look for ways the current body of research could be expanded, sharpened, or redirected. Scribble down those burning questions that the studies were too chicken to touch, the gaps that need filling, and the unexplored frontiers waiting to be conquered. Ponder what kind of studies would build meaningfully on these findings. Don’t be afraid to get a little creative—the best ideas often lurk just beyond the obvious. Put on your visionary cap and brainstorm other promising paths forward that might rattle some cages. Use these gaps as a basis to formulate your own research questions or ideas for further exploration. ***Ask:*** - What specific areas do the authors suggest for future research? - Are there unexplored questions that align with your own interests? - Are there specific methods, populations, or contexts that warrant further investigation? 🌟 ****Pro Tip:** Consider how your own experiences, perspective, or unique vantage point could contribute a fresh take on the topic. ### **Organizing your review** This 9-question framework isn’t just about reading a bunch of papers—it’s about processing information in a way that adds value to your field. Here’s how you can transform this framework into a structured literature review: 1. **Ask these questions for each relevant study.** Start by reading through the literature and noting down answers to the nine questions for every significant work you find. This will prevent you from getting lost in too much detail. 2. **Organize answers into clusters.** Once you’ve gathered your notes, arrange them by theme instead of by study. You’ll find patterns and contradictions more easily this way. Tag each study in Zotero. 3. **Identify gaps and contradictions.** With the themes laid out, it’s time to see where there are gaps. Contradictory findings are particularly valuable, as they often highlight where further work is most needed. 4. **Spot opportunities.** Use the gaps you’ve found as kickoff points for your own research questions or hypotheses. Confront the chaos others fear. Be the bold one who sets things right. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **What to do now?** The 9-Question Literature Review Framework is simple, yet it can change how you approach your research. The next time you’re staring down a mountain of papers, don’t panic. Use these nine questions to shape a clear, concise, and coherent review that makes a simple contribution. Ready to give it a try? Here are **three simple steps you can take right now**: 1. **Pick a single theme** that interests you in your research. Gather the key studies that relate to it. 2. **Write a one-sentence summary** for each paper focusing on the main finding or insight. 3. **Group similar ideas together** and look for recurring themes or differences. This will help you see patterns more clearly. You know what they say. Good research is 20% cramming new insights into your brain and 80% making chaos look like an aesthetic work of art. Hopefully, this guide helps you do the latter. Until next time. ## Downloads **Here is the full checklist as a PDF download:** _This post is for paying subscribers only._ ### How to develop an efficient writing process URL: https://lennartnacke.com/how-to-develop-an-efficient-writing-process/ Last updated: 2025-10-21T11:38:30.000Z Writing doesn’t have to be like farming XP in a role-playing game — you know, that repetitive and soul-draining activity that you loved so much as a kid. Let’s take a loud-ass leaf blower to the unnecessary complexity of academic writing and make it scatter like those maple leaves clogging up my driveway. Break it down, build it up, and keep your sanity intact. Writing efficiently isn’t about churning out pages like a Nobel-prize-winning machine; it’s about knowing where you’re headed and picking the best route to get there. This means you need to know how you can set up an efficient writing process that saves you time and makes the actual writing work less of a slog. A little structure goes a long way — just like having the right cheat code when you’re stuck on the final boss level of a video game. #### **1\. Define your goal clearly** Every piece of writing begins with a purpose. If you don’t know why you’re writing, it’s like trying to navigate a corn maze with a paper bag over your head — you’ll probably just crash into many crops (and possibly some evil children). Do you need to argue a point? Inform an audience? Inspire change? Before anything else, clarify what you need your writing to achieve. *Example:* Imagine you’re designing an interface for a new educational app. If your goal is to help users learn efficiently, then every part of your interface — layout, interactions, content — needs to align with that. It’s like trying to survive a horror movie without knowing the killer’s rules (any Scream fans in the audience?) — if you just wing it, you’re probably going to end up as the next victim instead of the final survivor. **Pro Tip**: *Write down your purpose in one clear sentence before you start.* #### **2\. Create a solid writing plan** Jumping straight into writing is tempting — like trying to assemble a LEGO Death Star without the instruction booklet — but you’ll end up frustrated and probably with giant holes and extra pieces. Creating an outline might sound as boring as watching a season of The Acolyte, but it’s your best ally to avoid wasted hours and needless rewrites. Think of it as planning your moves before executing them — keeping your focus on the big picture. Your outline doesn’t have to be fancy. Just bullet points noting what each section will cover can do the trick. This is where you identify the main points, examples, and conclusions. *Example:* Picture outlining a research paper on human-computer interaction: 1. **Introduction**: Identify the problem or gap in existing research. Why is this important? 2. **The Issue**: Explain why the problem matters and how it impacts users. 3. **Proposed Solution**: Describe the study or theoretical approach you plan to use to address the issue. 4. **Contribution**: Discuss what your research will add to the field and why it’s significant. **Pro Tip**: *Outlining helps with structure and speeds up writing — you know what comes next, no guesswork.* #### **3\. Draft without worrying about perfection** Perfectionism kills efficiency. The first draft is all about getting your ideas down, not making them pretty. Trying to write a perfect piece from the start? That’s like trying to carve a Tiki God statue with a butter knife while wearing oven mitts. You sure look cute, but nothing will get done. It’s fine if things are clunky, ugly, and missing bits. Clean-up comes later. Your first draft is raw material — you’ll shape, cut, and polish it. The goal is simply to get words on the page. And yes, if you have to vomit those, that’s cool. Get the glibber out. *Example:* Can’t think of the perfect opening line? Just write “Todo: Intro about user experience in generative AI interfaces” and move on. It’s kind of like skipping all the character intros in a horror movie, so when they start dropping like flies, you don’t even care who they are. **Pro Tip**: *Set a timer for 20 minutes and write non-stop. The goal is speed, not beauty.* #### **4\. Edit in focused rounds** Editing is where the charm is created — like using duct tape to patch up a sinking boat — it works, but you need to do it step by step. But doing it all at once will leave you frustrated. A solid strategy is to edit in rounds, each with a specific focus. This breaks the editing into manageable parts and keeps you from trying to fix everything at once. 1. **Structure and content.** Is everything where it should be? Do your arguments flow? 2. **Clarity.** Are your sentences clear? Would your colleagues from a different field understand what you’re trying to say? 3. **Polish.** Fix typos, grammar issues, and awkward phrasing. *Example:* On the first editing pass, you might notice your main argument about user-centred design is lost halfway through. Bring it up front, like giving the mic to the lead singer instead of the backup dancer. **Pro Tip**: *Take a break between rounds. Fresh eyes catch more mistakes — like rewatching Gone Girl to catch all the red flags you missed the first time.* #### **5\. Use writing tools effectively** Technology can save you time as long as you use it properly. Writing tools like grammar checkers, voice notes transcription apps and AI co-writers are here to help. And, man, they do make our lives easier. Generative AI assistants like ChatGPT, Claude, and Gemini can generate ideas, rephrase sentences, or even draft sections of your work. You can also use voice notes transcriptions with tools like Apple Notes, Audiopen, or Otter AI to get a first draft — a messy, written version of your thoughts that you can then edit. The new ChatGPT 4o with canvas makes it even easier, letting you query and edit bits of text in place. This newsletter was written with its help, too. Still, bear in mind—these tools can’t replace your own judgment, especially with tricky concepts. Think of them as your techie sidekick — maybe like J.A.R.V.I.S. before he turned into Vision (you’re still Iron Man, RDJ). Use a grammar checker, but don’t blindly trust every suggestion (or would you let autocorrect write your next love letter? You’ll probably end up with ‘I loaf you’ instead). Consider voice-to-text tools if you think faster than you type (the new Apple Notes transcripts are useful for me when doing that), and apps like Notion to track progress while taking advantage of its AI features to simplify content organization. *Example:* For my last project, I used Otter AI to transcribe a long brainstorming session I recorded on my phone. This gave me a messy but valuable starting draft that I could shape. Then, I brought it into ChatGPT to help structure some sections and make the wording coherent. I then did some minor editing within Notion and, later, the Hemingway App. Using these tools together saved me hours and kept me focused on making my content as effective as possible. **Pro Tip**: *Find the tool that solves your biggest issue — if outlining slows you down, try mind mapping software; if editing is overwhelming, get an AI grammar buddy.* #### **6\. Write with purpose, not just in sessions** Writing isn’t just about grinding out words — it’s about using your energy effectively. Instead of rigid writing sessions, try setting small, specific goals for each writing block. These could be finishing a section, drafting an argument, or explaining a concept clearly. Focusing on goals rather than time keeps you motivated and gives you a sense of progress, even if you only have 20 minutes. *Example:* Instead of just writing for 45 minutes, decide, “I’m going to complete the introduction.” This focus helps you stay productive without feeling like you’re running a never-ending marathon. **Pro Tip**: *Breaks are still essential, but make them purposeful. Step away and do something that stimulates your mind in a different way — like doodling, solving a quick puzzle, or walking while listening to your favourite music.* **Try this today:** 1. Clarify your goal by writing down your next project’s purpose in one simple sentence. 2. Outline your sections with bullet points to cover the main areas you want to address. 3. Set a short timer and focus on completing a specific section or idea to make progress. 4. Edit in rounds, starting with structure, then clarity, and finally polishing. 5. Set a specific writing objective for each session and take meaningful breaks to keep your energy up. These steps can make your writing process more efficient and much less stressful. Writing well is about the process and staying sane while doing it (or ending up like young Johnny Depp with Freddy Krueger stuck in his head haunting your thoughts at 3 a.m.). **More resources to help you write better** - [*ChatGPT 4o with canvas:*](https://chatgpt.com/?ref=lennartnacke.com) Go from avfirst draft with a prompt to precise editing of said draft in a few minutes. It’ll help you finetune your text as you edit it. - [*Grammarly*](https://www.grammarly.com/?ref=lennartnacke.com): Grammar and spell checker for a quick polish. Do NOT accept all of its suggestions. - [*Hemingway App*](https://hemingwayapp.com/hemingway-editor-plus?ref=lennartnacke.com): Improve clarity by simplifying sentences. Get writing stats and pay for some kickass AI editing tools. - [*Notion*](http://notion.so/UX-Resources-Database-f41f9080b0a54eb8a8bf927dcf38b1d9?ref=lennartnacke.com): Track writing projects, progress, and edits together. Do some easy AI rewriting of parts of your draft. - [*Apple Notes*](https://www.icloud.com/notes?ref=lennartnacke.com): Voice notes transcription for quick drafts. Find everything with easy searching functionality. - [*Audiopen*](https://audiopen.ai/?ref=lennartnacke.com): Transcribe your voice notes for easy drafting. Free version has some limits but will get you started if you like free flow talking. - [*Otter AI*](http://otter.ai/?ref=lennartnacke.com): Detailed transcription to help shape early drafts. The most accurate transcription tool I’ve used for the last years. Writing efficiently is about working smart, not hard. Get a clear purpose, make a plan, work in bursts, and embrace the mess of a first draft. Writing well takes time, but it can feel like Jamie Lee Curtis in many Halloween movies — exhausted, but glowing in some kickass victory scene. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to write a winning research proposal URL: https://lennartnacke.com/how-to-write-a-winning-research-proposal/ Last updated: 2025-01-12T05:41:33.000Z If you've ever written a research proposal, you might have come across this situation: A professor asks for something, but doesn't clearly explain what they want. This leaves you guessing what they're looking for. Yes, I've been there. It felt like somebody gave me a wooden spoon to hit things, but I'm also wearing a blindfold. You try to hit a target you can't see. It's difficult. You might get lucky and hit it, but most likely you'll miss. Well, crap. Many junior researchers get caught in a web of jargon, endless drafts, and a mockingly blinking cursor on a blank page. But your ideas have the power to change the world. That's why I've put together this simple guide to help you turn a wild idea into a winning research proposal. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 7,262+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Below are the 7 key elements you need: ### 1\. Research Topic: Problem Statement Let's first zero in on that brilliant idea swirling in your noggin. Begin by choosing a clear and concise title for your project that reflects the main focus of your research. Define a distinct problem or issue you plan to address, explaining why it is important and worth investigating. Lay it out like you're explaining it to your non-academic friends over some poutine with extra gravy. Set specific research aims and objectives that outline what you hope to achieve with your study. Formulate research questions or hypotheses that will guide your investigation and provide a strategic outline for your work. Are you aiming to solve world hunger or just figure out why your plants keep dying? (Pro tip: Water helps.) Spend quality time refining your foundational problem statement. A well-defined problem is half the solution—just like finding the TV remote usually solves 50% of your evening dilemmas. ### 2\. Background: Study Context Time to give your research some exciting context. The kind of contextual excitement we got from the opening crawl of Star Wars movies, before everything turned into a turd fest over the last decade. Provide background information that explains how the problem originated and why it persists. How did this issue pop up? Spill the tea. Discuss the practical implications and theoretical significance of your research, highlighting how it can contribute to your just field or even society at large. Mention any assumptions you're making to clarify the scope of your study. Define key terms or concepts to ensure that readers understand your proposal. Don't make readers play Sherlock Holmes. Lay it all out so everyone's on the same page. Acknowledge any limitations or boundaries of your research to set realistic expectations and focus your efforts. Think of it like boundary setting in a relationship—healthy and necessary. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 3\. Literature Review Show your understanding of existing research. Who's doing what, and how does it relate to your work? Summarize the key studies and debates on your topic. Discuss what is already known and identify gaps in the current knowledge that your research aims to fill. Where did previous researchers drop the ball? That's where you come in to save the day. Explain how your study builds upon or differs from previous work, offering new perspectives or solutions. Are you adding a new twist or debunking an old myth? Make it clear. Critically analyze past studies. Point out any outdated info or flaws. Then, describe how your approach will provide a better understanding. Consider the theories that will guide your analysis. If you're introducing a new model or adjusting an old one, explain how it improves understanding or fixes past flaws. Don't just nod along—challenge methodologies, question conclusions. ### 4\. Methodology Align your research aims with a well-defined method. Choose a research approach—qualitative, quantitative, or mixed methods—that best suits your objectives, and explain why it's the most appropriate choice. Justify it like you're defending your choice of pizza toppings (pineapple is valid; fight me). Detail the specific methods you will use. Surveys? Interviews? Drone deliveries? Explain what you're doing and how. Describe how these methods will help you gather the necessary data. Where are you getting your data, and what are you going to do with it? Be specific. Include any tools or instruments you will use for this. Break down the techniques for analyzing and interpreting the data. Your methods must address your research questions. Address any ethical considerations, such as confidentiality or informed consent, and describe how you will manage them responsibly. Lab rats escape? Wi-Fi down? Have backup plans ready. Anticipate potential challenges. Be thorough but concise. Your methodology should leave no room for "But how will they...?" questions. ### 5\. Proposed Timeline Time management isn't just for anxious beavers. Break down your project into digestible chunks—preparation, data collection, analysis, writing, and revisions. Set specific milestones for each phase to check your progress. I'm telling you, Murphy's Law is real. Pad your schedule for those inevitable hiccups (e.g., difficulties in data collection or the need for more research). Allocate some extra time for those delays. A realistic timeline keeps you on track and shows evaluators you're serious. Plus, it helps prevent those 3 a.m. panic attacks. ### 6\. Resources Required Ever tried building IKEA furniture without instructions or tools? Trust me, it's no fun. Identify all the resources you will need to complete your research. That's tools and software necessary for data collection and analysis, personnel who will assist you, and any materials or equipment required. SPSS, NVivo, or maybe just a solid Wi-Fi connection or someone to hold the camera. Know what you need. From lab coats to laser pointers, make sure you've got it covered. Verify that these resources will be available when needed during each phase of your project. Planning for resource availability helps prevent delays and lets you manage an effective budget. Diligent preparation protects you against poor execution. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 7\. Bibliography Time to flaunt your extensive reading list. Compile a comprehensive list of relevant sources that you have referenced in your research proposal. Quality over quantity. Cite works that truly inform your research. Show your familiarity with existing related literature. It shows evaluators that you have grounded your research in established knowledge. APA, ACM, Harvard—whichever style guide is your jam, double-check that you have correctly formatted your citations. This adds massive credibility to your proposal. ### Final Thoughts Defining your research problem with clarity sets the tone for your entire project. If you contextualize your research, you draw attention to its importance and show how your work fills a critical gap. You make a compelling case for your study. This precision improves your methods and earns you credibility. It shows a careful approach that reviewers appreciate. Your timeframe shouldn't suggest you are rushing your project. But, deadlines matter. So, be aware of them. Planning ahead in your proposal helps you avoid detours and stay on track. Attention to detail in every aspect reflects your commitment and strengthens the impact of your proposal. Always use clear language to make your ideas accessible. Stay focused on your main research question, confirming that you have aligned all parts of your proposal with your objectives. Finally, show your passion for the topic, because genuine enthusiasm can make your proposal more compelling. ## Research Proposal Cheat Sheet Get a PDF cheat sheet for writing your research proposal below: [How to write a research proposal (cheat sheet)My cheat sheet for writing a research proposal.Lennart Nacke - How to write a research proposal.pdf403 KBdownload-circle](https://lennartnacke.com/content/files/2024/10/Lennart-Nacke---How-to-write-a-research-proposal.pdf "Download") **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to pick a research topic (and actually love it) URL: https://lennartnacke.com/how-to-pick-a-research-topic-and-actually-love-it/ Last updated: 2025-01-28T13:13:21.000Z *Quick ask:* If you’ve been loving these [newsletters](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) and they’ve sprinkled some unicorn dust onto your academic life, could you do me a solid and drop a quick testimonial? Here are some great ones I've received from you thus far: ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 8,000+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. [![](https://embed.filekitcdn.com/e/2Z57ZoCyqGzFe5hbTRuo8g/kgpvWNYCbAK52ug6xykx15)](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) [![](https://embed.filekitcdn.com/e/2Z57ZoCyqGzFe5hbTRuo8g/95odt4Vs2chgBkoiQxma1i)](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) [![](https://embed.filekitcdn.com/e/2Z57ZoCyqGzFe5hbTRuo8g/u7DsfxVtqocksdtuNU95xS)](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) Alright, back to the main event. Let’s get you that perfect research topic without breaking a sweat—or losing more hair. [How to pick a research topic (cheat sheet)A quick cheat sheet for picking a research topic.Lennart Nacke - How to pick a research topic.pdf402 KBdownload-circle](https://lennartnacke.com/content/files/2024/10/Lennart-Nacke---How-to-pick-a-research-topic.pdf "Download") #### 1\. Follow your interests You wouldn’t date someone just because they look good on paper, right? The same goes for your research topic. It’s gotta be love at first (or maybe second) sight. **Ask yourself:** - What topics am I genuinely curious about, and why do they light my fire? Take a stroll through your mind palace, Sherlock Holmes. Reflect on subjects within your field that make you geek out like Sauerkraut (you’ve got to be a hot dog to get this). Is it quantum computing? Sustainable architecture? The mating habits of clay robber frogs? No judgment here. **How to do this:** - **Jot down your thoughts or create a mind map.** Get those ideas out of your head and onto paper (or screen). Visualize how they connect. - **Pick topics you’re passionate about.** Trust me, this will keep you motivated when the going gets tough—and it will get tough like Tungsten. **Pro Tip:** Don’t just chase trends unless they genuinely excite you. Remember, you’ll be married to this topic for a while. Make sure it’s a match made in heaven, not a shotgun wedding in Vegas. #### 2\. Read current research Time to do some sleuthing. Sink your teeth into the latest publications, Renfield, like you’re binge-watching a Netflix true crime show—minus the guilt. **Ask yourself:** - What unanswered questions or gaps can I discover that align with my interests? Gaps emerge from knowing what’s there and what’s not. So, you have to read, and you have to read a lot to understand this synthesis. But after you read, you must also take some time to reflect on the sources. This will guide you along. **How to do this:** - **Root around recent journals, articles, and conference papers.** Know what’s hot and what’s not. Understand what gets published. - **Identify gaps or unexplored angles.** This is your chance to be the Veronica Mars of your field. Find where things don’t connect. - **Avoid duplicating existing work.** No one wants a remix of a song that wasn’t a hit in the first place. You have to know what’s been done and how. **Pro Tip:** Use academic databases like Google Scholar and Scopus, or specialized ones like PubMed or ACM Digital Library to stay in the loop. #### 3\. Talk to your advisor Alright, time to get some face time with the person who’s going to make or break your PhD journey. Their success is tied to yours, so you want to leverage that connection well. **Ask yourself:** - How can my advisor’s experience help me sharpen my research idea? Not only will your advisor know much about your field, but they might also have a strong interest in exploring areas that bring in funding or support for their entire research group. So, do a bit of digging to find the things that align well with both. **How to do this:** - **Schedule a meeting.** Don’t ambush them in the hallway unless you want to be “that student.” Think about the value of the meeting for them. - **Be open and honest about your thoughts.** Lay your cards on the table. Be clear and keep emotion out of it. - **Be receptive to their suggestions.** They’ve been around the block—probably several times. Take in all ideas first, reflect on them later. **Pro Tip:** Advisors can have hidden agendas (like their own research interests or strategic university funding), so make sure their topic also aligns with your passion. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) #### 4\. Check what’s possible Hey, I like Star Trek as much as anyone. Well, “The Next Generation,” anyway. But as much as I’d love to teleport to Mars, we’ve gotta keep it real here. So, once you know what you want, you have to check if it’s possible. **Ask yourself:** - Do I have the resources and time to complete this research successfully? **How to do this:** - **Assess available tools, data, and equipment.** Do you need a supercomputer or just a calculator? What is your advisor planning to purchase if it’s not there already? - **Consider the timeframe of your program.** Is your topic more of a marathon or a sprint? Are there subtopics involved? Are you working closely with other grad students or postdocs? - **Ensure the topic is practical and achievable.** Don’t bite off more than you can chew. Discuss with experienced lab members to get a feel for the feasibility of this thing. **Pro Tip:** Funding is always a biggie. Check if there are grants or scholarships you can tap into. No money, no honey, bunny. #### 5\. Define your research question Alright, enough with the preamble. Time to get down to business. You’ve done the legwork, so now it’s time to transform those scattered ideas into a clear, focused research question. **Ask yourself:** - Is my research question focused and feasible for in-depth study? The research question is the foundation for your entire study. It guides every step, from the literature review to research methods to analysis. Avoid vague questions. You want a question that is specific, answerable, and meaningful. **How to do this:** - **Come up with a specific, concise research question or hypothesis.** Clarity is king here. - **Make sure it’s answerable through systematic investigation.** No “How long is a piece of string?” questions. - **Connect your question to the gaps you’ve identified.** This is where you bring in that background knowledge from the beginning. **Pro Tip:** Bounce your question off a friend or that one colleague who always plays devil’s advocate. If they can’t poke holes in it, you’re onto something. #### 6\. Ensure originality Nobody wants to be the 100th person studying the effects of caffeine on sleep (spoiler: it’s not great). You want something that is different but also builds on existing work. This is tough. **Ask yourself:** - What unique contribution will my research make to the field? Look, I get it—you want to stand out from the crowd, be the special snowflake of your field. Well, to do that, you gotta put in the legwork, amigo. You already dug through that mountain of published work like a mining mole on a mission. Unearth those gaps, those blind spots, those juicy unanswered questions that’ll let you swoop in and claim your territory. If you can find an area that’s been overlooked or neglected, you’ll be sitting pretty, ready to make a real impact with your research. **How to do this:** - **Verify that your topic offers a new perspective.** Be the Daniel Boone of your research field, not just a random settler. - **Consider how your work could advance knowledge or practice.** Think impact. How could you measure this impact, and what would it mean for society if you did this research? - **Avoid overly saturated topics unless you have a groundbreaking angle.** Playing it safe and doing an n+1 iteration might be a great way to get started with a paper but will rarely suffice for an entire PhD. **Pro Tip:** Originality doesn’t mean you have to reinvent the wheel. Sometimes, putting a new spin on existing research is enough to make waves. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) #### 7\. Narrow it down Let’s trim the fat. You’ve got a solid research question, but now it’s time to whittle that bad boy down to a manageable size. Get some gym time, Jabba. Instead of asking yourself open-ended questions, take a more targeted approach. **Ask yourself:** - Is my topic appropriately narrowed to allow for thorough investigation? Narrowing down your topic means honing in on specific aspects that you can realistically dive into within the confines of your program. This sure isn’t about watering things down to the point of meaninglessness—we’re talking about pruning away the unnecessary branches to get to the heart of what you really want to explore, you feel me? No more beating around the giant bush, let’s get focused. **How to do this:** - **Limit your topic to a specific aspect.** Go deep, not wide. Find one aspect that matters most. - **Be willing to tweak your question based on new insights.** Flexibility is your friend. You might discuss with a lab mate and adjust. - **Keep your objectives clear.** Ambiguity is the enemy of progress. Always remember your research goals from earlier sessions. **Pro Tip:** A focused topic is easier to manage and more likely to produce meaningful results. #### 8\. Use AI Tools I hear you. It’s not everyone’s jam. But, AI tools can seriously help you find and refine parts of your topic. They can support your research and help with brainstorming. But the tech is just your sidekick, not the main event. Use it to enhance your process, not replace your sweet, sweet brain power. **Ask yourself:** - How can I effectively use AI tools to discover and refine my research topic? You can use AI to speed up your topic creation. Fire up those AI-powered search engines and go digging for related studies and then take it from there to a literature database. Or put those language models to work for generating keywords and helping you frame your questions. And you know text analysis is what they’re really good at, so you can use AI to find the patterns, themes, and new angles by working on your draft. But don’t go getting carried away. Balance is key, my friend—the AI assists, but you’re still the boss, El Capitan. **How to do this:** - **Idea Generation:** Use tools like [​Perplexity AI​](https://go.lennartnacke.com/perplexity?ref=lennartnacke.com) to brainstorm topics and dig into web-based insights. It connects to the Internet and brings you recent topics. - **Prompt example:** “What are some under-explored research topics in renewable energy, and why are they important?” - **Literature Search:** Use [​Consensus​](https://go.lennartnacke.com/consensus?ref=lennartnacke.com) or [​SciSpace​](https://go.lennartnacke.com/scispace?ref=lennartnacke.com) to skim the first set of relevant papers. Then, go deeper. - **Trend Analysis:** [​R Discovery​](https://discovery.researcher.life/?ref=lennartnacke.com) can help you spot emerging trends before they’re mainstream. **Pro Tip:** AI is a tool, not a crutch. Use it to enhance your work, not do it for you. And always double-check AI-generated info—errors can sneak in like mini ninjas (or like chocolate-covered coffee beans, seriously, who wants to eat that?) #### How to get that research topic just right Alright, we’ve covered a lot of ground here. But knowledge without action is like a Jedi without a lightsaber—it’s not taking you anywhere (and you might end up being an acolyte that nobody wants to hang with). So here’s your plan for today if you need to find a research topic: 1. **Set aside one day:** Block out a full day to focus solely on choosing your topic. Turn off notifications, hide from roommates, do whatever it takes. 2. **Mindmap your interests:** Spend an hour jotting down everything that excites you in your field. 3. **Dive into the literature:** Allocate a few hours to skim recent research. Take notes on gaps and interesting angles. 4. **Consult your advisor:** Set up that meeting. Come prepared with your ideas and questions. 5. **Reality check:** List out the resources you have and those you might need. Be brutally honest. 6. **Define your research question:** Write and iterate your question. Aim for clarity and feasibility. Doesn’t have to be final. 7. **Verify originality:** Do a quick check to ensure your idea hasn’t been done to death. 8. **Use AI tools throughout each step:** Use them to fine-tune your topic and dig up hidden gems. Choosing a research topic is the foundation of your entire academic journey. You want to pick the right Pokémon starter and set the tone for everything that follows. So why not be the Ash Ketchum of your field? Aim to catch ’em all (ideas, that is, not Pokémon, Satoshi), but choose the one that’ll evolve with you. Or channel your inner Tony Stark—innovate, iterate, and don’t be afraid to blow sh\*t up (figuratively, please, especially if you’re a chemist). But seriously, take the leap. Come up with a topic that not only adds value to your field but also ignites your own passion. You can do it. Stay curious and keep pushing boundaries. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to go from research idea to published paper in 10 (mostly painless) phases URL: https://lennartnacke.com/how-to-go-from-research-idea-to-published-paper-in-10-mostly-painless-phases/ Last updated: 2024-09-17T12:22:39.000Z Today, I’m handing you the ultimate framework to take your brilliant research idea into a published paper in 10 (mostly painless) phases. No more feeling like you’re lost in the Bermuda Triangle of data analysis and literature reviews. Just so you know: Many researchers feel this way, especially when they’re starting out. > Your research is failing because you rush step 1. > > Here's the full 10-step process: > > 1\. Define problem > 2\. Develop questions > 3\. Review literature > 4\. Formulate hypothesis > 5\. Select design > 6\. Get ethical approval > 7\. Collect data > 8\. Clean data > 9\. Analyze findings > 10\. Share results [pic.twitter.com/AcQY1gBbcK](https://t.co/AcQY1gBbcK?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [September 14, 2024](https://twitter.com/acagamic/status/1834928845612630086?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) But here’s the thing: research doesn’t have to be a confusing slog. With the right approach, it can be an exciting journey of discovery. Yes, little Dora, hold those Girl Scout Cookies™, I’m here to be your guide on that journey. So let’s go, we’ll break down the 10 essential steps of the research process. And, no, it won’t feel like a root canal. Big promise, Steve Buscemi. ![A flowchart overview of the 10 steps of the research process](https://lennartnacke.com/content/images/2024/09/Research-Process-1.png) A quick overview of the research process (in 10 painless steps) ### Phase 1: Define the problem (The foundation of your research) You wouldn’t bake a cake without measuring your ingredients first, would you? Likewise, in research, determining your problem is the essential first step of your project. But here’s where many researchers trip up: they rush through this step, eager to get to the “real work” of data collection and analysis. Big mistake. A fuzzy problem is like a cat trying to catch a laser pointer. You’ll end up puzzled, annoyed, and still empty-handed. Here’s how to get this critical step right: - **Spend double (or triple) the time you think you need here.** It’s an investment that pays off. - **Write your problem statement from three different angles.** It’s like trying on different outfits to see which one pops. It forces you to reflect on what you’re investigating. - **Get feedback from a few trusted peers or mentors.** Quality over quantity here—select people who can provide valuable insights into your specific area. Fresh eyes will catch that spinach in your teeth. Go for that Colgate smile. Every time. 🧠 **Example:* Instead of “I want to study user interfaces,” zoom in to “How do touch gesture designs impact user engagement in mobile gaming apps for users aged 18–24?” 💡 ****Tip:** Keep asking **“why”* and **“so what”* about your problem. If you can’t explain its importance to a non-expert without them nodding off, it’s back to the drawing board. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Phase 2: Develop research questions (Your inspirational anchors) With a well-defined problem, you can now create sharp, focused research questions. These are the guiding principles, like a Spotify playlist that sets the vibe for your entire project. Good research questions are: - **Specific** (no room for vague vibes here) - **Answerable** (because chasing unicorns isn’t productive) - **Relevant** (your questions should matter to your field) Lame questions lead to meh research. Awesome questions pave the way to breakthroughs that’ll make you the talk of the next conference (and you won’t even have to join karaoke for it). 🧠 **Example:* Building on our mobile gaming example, a research question could be: “How do different touch gesture designs influence user engagement and retention in mobile games among 18–24-year-olds?” 💡 ****Tip:** Jot down at least 10 potential questions. Then channel your inner fruit ninja and slice them down to the top 2–3 watermelon sugar highs. ### Phase 3: Review the literature (Stand on the shoulders of giants) Now it’s time to see what other researchers have already discovered about your topic. This step prevents you from reinventing the wheel and helps you position your work in the existing body of knowledge. This isn’t the time for casual skimming like you’re browsing memes on Reddit. Go deep. Be thorough. Read critically. Look for gaps and contradictions in the current research. That’s where you’ll find your opportunity to contribute something new. Your goal is to uncover what’s already known, what’s still mysterious, and where your work can take center stage. But don’t get lost down the rabbit hole of endless citations. Stay focused on your problem and research questions. 🧠 **Example:* In reviewing the literature on touch gesture designs, you might find plenty of studies on general usability but a gap in research focusing on user engagement among different age groups in mobile gaming. Follow this direction. 💡 ****Tip:** Use a reference manager like [Zotero](https://www.zotero.org/?ref=lennartnacke.com) or [EndNote](https://web.endnote.com/?ref=lennartnacke.com) from the get-go. Your future self (and your sanity) will thank past you for staying organized. ### Phase 4: Formulate a hypothesis (Make an educated guess) Armed with your lit review superpowers, it’s time to make a prediction, Madame Web (hope you didn’t have to suffer through that movie). What’s your educated guess about the outcome? This is your hypothesis. A solid hypothesis is: - **Testable** (you can actually check if it’s true) - **Specific** (details, people!) - **Based on existing knowledge** (you’re not pulling this out of thin air) Your intention shouldn’t be to prove your hypothesis right. It’s to test it with some rigour and see what the data reveal. Even if this contradicts your initial expectations. 🧠 **Example:* “Implementing swipe-based gestures in mobile games increases user engagement by at least 25% among 18–24-year-olds compared to tap-based controls.” 💡 ****Tip:** Stay committed to your original hypothesis, but be open to what the data reveal. Science is sassy like that. If your findings don’t support it, report that honestly and explore why. Not supporting a hypothesis is just as valuable to scientific progress. ## Sign up for Write Insight Shift into a smarter, more resourceful researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam, just the good stuff. I treat your inbox like my own. ### Phase 5: Choosing your research design (Pick your tools) Now comes the fun part: deciding how you’re going to test your hypothesis. Will you run user studies? A/B testing? Simulations? Your choice here will shape the rest of your research process. Choose a methodology that is the most effective way to answer your specific questions. Consider: - **Alignment with your research questions** (make sure your method fits your goals) - **Resources and time** (do you have a month or a year?) - **Ethical considerations** (don’t be that researcher) - **Feasibility** (can you actually pull this off?) 🧠 **Example:* You might decide to develop two versions of a mini-game—one with swipe gestures and one with tap controls—and measure user engagement metrics. 💡 ****Tip:** Consult a methodological guru or a statistician early on. They’ll help you sidestep slip-ups that could invalidate your results. Yes, they might save you from a catastrophic Picard facepalm later. ### Phase 6: Obtain ethical approval (Do no harm) Ethics permeates this process right from the start. You think about ethics during every step. But you also have to file the records. No one likes paperwork, but ethical approval is non-negotiable. It’s all about ensuring your research doesn’t accidentally turn into an episode of “Black Mirror.” Make sure your work respects human dignity and doesn’t cause unintentional harm. Think about: - **Informed consent** (people should know what they’re signing up for) - **Privacy and data protection** (don’t leak user data — that’s a one-way ticket to infamy) - **Fair treatment** (no bias, no exploitation) Determine if you need ethical approval. If your research involves humans, personal data, or animals, start the approval process early. If not, be certain to follow ethical research standards. Consider data handling and environmental impact. 🧠 **Example:* Because you’re dealing with user data from participants aged 18–24, you must ensure their data are anonymized and securely stored. 💡 ****Tip:** Check your institution’s guidelines. They can provide specific criteria on what requires ethical approval. ### Phase 7: Carry out the research (Time to get down to business) This is where the rubber meets the road. You’re collecting data, running experiments, conducting interviews — whatever your chosen method entails. Get your hands dirty, Pigpen. Remember to: - **Follow your plan but stay flexible** (if new insights emerge, be ready to pivot while keeping your objectives in sight) - **Document everything** (future you will need this when writing stuff up) - **Stay flexible** (because life happens and it always happens when experiments are running) 🧠 **Example:* Run your user tests, collect engagement data, maybe even throw in some user interviews for juicy qualitative insights (thick like a chocolate Frosty from Wendy’s 🥤). 💡 ****Tip:** Expect the unexpected. Schedule extra time for when the universe throws a wrench in your plans. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Phase 8: Prepare and clean data (Garbage in, garbage out) Raw data is like unfiltered content on OnlyFans. It needs some cleanup and processing to reveal its true value. Steps to follow: - **Organize your data logically** (spreadsheets are your friend) - **Check for errors or inconsistencies** (did someone play your game for 100 hours straight? Probably a glitch or Elden Ring) - **Transform data for analysis** (normalize, categorize, do the data boogie) 🧠 **Example:* You might need to filter out participants who didn’t complete the game or had technical issues. 💡 ****Tip:** Learn to love spreadsheets and databases. They’re your best friends in this step. Clean data is the foundation of credible results. ### Phase 9: Analyze and interpret findings (The Aha! moment) Now comes the exciting part: Let’s see what the data says. Crunch those numbers, look for patterns, and see if your hypothesis holds water. Apply statistics to test hypotheses. But it’s not always Captain Crunch and the lucky numbers. Data analysis can be more complex than basic statistical measures. Analysis gives information meaning. Consider: - **Appropriate analysis methods** (e.g., regression analysis, ordinary least squares, thematic coding, machine learning techniques—oh my!) - **Visualizations** (graphs can speak louder than numbers; in fact, they often yell at me) - **Contextual interpretation** (what do these results actually mean?) Depending on your study, you might need advanced statistical methods or qualitative analysis techniques. 🧠 **Example:* You might find that swipe gestures increased engagement by 30%, but only in certain types of games or with specific user demographics. And you might have different effect sizes. 💡 ****Tip:** Don’t dump data on your readers like a golden shower. Tell a story about what it means and why it matters. How does it fit into the broader field of knowledge? ### Phase 10: Disseminate results (Share that bounty, ARRR!) Congrats! You’ve got results. Now it’s time to shout it from the rooftops—or at least publish it. Don’t let Reviewer 2 get in your way. Your research isn’t complete until you’ve shared it with the world. Ways to share: - **Write a paper** (aim for that high-impact journal) - **Present at conferences** (network and savour the prestige) - **Share on social media** (make your work accessible to readers with shorter attention spans, after you’ve published the paper) 🧠 **Example:* Besides publishing in a top HCI journal, you could present your findings at CHI or even write a Medium article or Newsletter issue to reach a broader audience. 💡 ****Tip:** Start drafting your paper while you’re still analyzing data. Writing helps clarify your thoughts and might reveal insights you hadn’t noticed. **Your Action Plan for Research Fame** That’s the deal—the 10-step structure from “Hmm, quirky brainwave there…” to “Check out my published paper!” But knowledge without action is like a smartphone without a signal. Let’s get you some reception: 1. **Dedicate a full day** to defining your next research problem. Full immersion, no distractions. 2. **Brainstorm 10 research questions** and then prune them, Edward Scissorhands. Quality over quantity. 3. **Set up or organize your reference manager.** No more last-minute citation hunts. Keep it all together. 4. **Create a realistic timeline** for your project, padding for those inevitable hiccups. 5. **Practice your elevator pitch** on a non-academic friend. If they get it, you’re golden. The path from idea to published paper isn’t always smooth. It’s an epic quest filled with twists, turns, and the occasional dragon to slay (looking at you there, data analysis). Each project is like levelling up in a game, unlocking new skills and achievements. Treat it as a learning opportunity. So why not be the Geralt of Rivia in your field? Plunge headlong into the abyss, tackle those monstrous problems, and maybe toss a coin to your witcher (wait, that’s you!). Until next time, stay curious. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to master 14 literature review types URL: https://lennartnacke.com/how-to-master-14-literature-review-types/ Last updated: 2024-09-10T15:50:08.000Z Today, I'm going to spill the beans on how to master the 14 types of literature reviews I tweeted about a while back. Stick with me, and you'll learn how to pick the perfect review type for any project. > 14 different literature review types categorized by method used [pic.twitter.com/GRULFuPfzk](https://t.co/GRULFuPfzk?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [August 5, 2024](https://twitter.com/acagamic/status/1820474857878069645?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) When you're done with this newsletter issue, you'll have a killer plan to improve your academic writing, surpass your peers, and excel in any scholarly project like a professional. You'll master the art of selecting the perfect review type, avoiding the rookie mistake of relying on methods that don't fit. Most researchers are stuck in a rut, clinging to the same old review types like a bad habit. It's like having a buffet of gourmet options and choosing plain toast every time. Hello, my friends from the Netherlands (😆 kidding, your toast lunches are primo). This self-inflicted tunnel vision is killing your academic mojo and churning out tired, lacklustre work. Nobody wants that. ### Why are we such scaredy-cats? Let’s face it: academic writing can be daunting. And we academics can be a nervous bunch. Here's why we often play it safe: - We're afraid of the unknown. New methods? Yikes! - We feel like imposters. "Who am I to try something new?" - We're perfectionists. What if we mess up? - We're always racing the clock. Deadlines, am I right? We don't see the point. I get it. I've been there, staring at my computer screen, wondering if I should stick to my trusty systematic review or venture into uncharted territory. It's scary stuff! ### Why branching out feels like climbing Everest Even when we want to try something new, it feels like the odds are stacked against us: - Our training was limited. Thanks a lot, Dr. Wilgendübel. - We're flying blind. When do we use each type? Who knows! 🤷‍♂️ - We're afraid of the academic mean girls: "Oh, you're using THAT review type?" - Our departments are stuck in the Stone Age: "We've always done it this way." - The payoff isn't instant. Patience is a virtue they say... - There's too much info. Information overload, anyone? But don't worry, I've got your back. Let's break this down and make it manageable. Let's turn you into a master of all 14 review types. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Know your tools: The 14 review types First things first: you need to know what each review type is for. Think of these review types as different dance moves. You wouldn't do the Macarena at a wedding slow dance, right? Here’s a rundown: 1. **Critical review:** Show off your smarts! This type lets you flex your analytical muscles and contribute new ideas to your field. 2. **Literature review:** The classic. It’s your chance to provide a broad, sweeping look at what’s happening in your area of study. 3. **Mapping review:** Play connect-the-dots with existing research. It’s great for identifying gaps in research. 4. **Meta-analysis:** Numbers nerds, rejoice! Combine data from multiple studies for some serious statistical firepower. 5. **Mixed studies review:** The best of both worlds, getting quantitative and qualitative research together for a group hug. 6. **Overview:** Give your readers the CliffsNotes version of a topic, summarizing key characteristics of the literature. 7. **Qualitative systematic review:** Go deep into qualitative studies. Like a detective, but with no danger and more reading. 8. **Rapid review:** For when you need answers fast. It’s a quick assessment of what’s known about a topic. 9. **Scoping review:** Get the lay of the land. This type helps you understand the size and scope of available research. 10. **State-of-the-art review:** Be the cool kid who knows all the latest trends in research. Deal with your field's most pressing matters. 11. **Systematic review:** The marathon runner of reviews. Thorough, exhaustive, and a bit of a show-off. Comprehensively search and synthesize evidence on a specific question. 12. **Systematic search and review:** Combine your inner critic with your inner librarian. 13. **Systematized review:** Systematic review's chill cousin. Most of the rigour, half the stress. 14. **Umbrella review:** The review of reviews. Frodo's one ring. Bring multiple reviews together in one epic evidence showdown. Each type has its strengths, and knowing when to use each one is key to becoming a the reviewer your field deserves, but not the one it needs right now. (Kidding, you always want to be the one it needs, because you're not Batman despite what mom told you as a kid.) ## Sign up for Write Insight Shift into a smarter, more resourceful researcher in 9 minutes tops per week. Turn your scientific findings into influential articles effortlessly. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### Match methods to research goals Now that you know your moves, how do you know when to bust them out? If you know what each type does, how do you choose the right one for your project? It’s all about matching the method to your research goals. Here's a cheat sheet: - Wanna spot trends? → Mapping review or systematic map is your jam. - Need to know if something actually works? → Meta-analysis or systematic review will be your BFF. - Trying to cook up a new theory? → Critical review or qualitative evidence synthesis is the secret sauce. - Gotta inform the bigwigs? → Rapid review or scoping review can give you the quick and dirty. - Just need a general overview? → Literature review or overview will do the trick. Your goal is to choose the review type that will best answer your research question and serve your audience. It's all about picking the right tool for the job. You wouldn't use a pressure washer to hang a picture, would you? (I dare you to try it 😁) ### Master the unique methods of each type Each review type has its own set of methods and approaches, each with their own little quirks. Here’s a crash course: 1. **Critical review:** Read widely, think deeply, and don’t be afraid to challenge existing ideas. 2. **Literature review:** Cast a wide net, organize your findings, and tell a compelling story about the state of your field. 3. **Mapping review:** Categorize, categorize, categorize. Then step back and look for patterns and gaps. 4. **Meta-analysis:** Brush up on your stats. You’ll be combining data from multiple studies to draw conclusions. 5. **Mixed studies review:** Learn to integrate different types of data. It’s like being bilingual in the research world. 6. **Overview:** Develop a knack for summarizing complex ideas concisely. 7. **Qualitative systematic review:** Hone your skills in identifying themes across different qualitative studies. 8. **Rapid review:** Speed is key. Learn to quickly assess and synthesize information. 9. **Scoping review:** Think breadth, not depth. You’re mapping the territory, not exploring every nook and cranny. 10. **State-of-the-art review:** Stay current. This type requires you to have your finger on the pulse of your field. 11. **Systematic review:** Develop a rigorous, reproducible method for searching and analyzing literature. 12. **Systematic search and review:** Combine critical thinking with exhaustive searching techniques. 13. **Systematized review:** Learn the basics of systematic review methods without getting bogged down in the full process. 14. **Umbrella review:** Master the art of synthesizing information from multiple reviews. ### Develop a review type decision tree Picking the right review can feel like trying to choose a Netflix true crime show. Too many options! To help you choose the right review type, create a decision tree. Here’s a simple version to get you started: 1. What’s your main research question? 2. How much time have you got? 3. How deep do you wanna go? 4. What kind of data are you dealing with? 5. What’s the primary purpose of your review? Your answers to these questions will guide you toward the most appropriate review type. ### Practice makes perfect (or at least pretty good) The best way to master these review types is through practice. Here’s how: 1. Pick a topic you're into. The more interesting, the better! 2. Choose a review type you've never tried before. Be brave! 3. Do a mini-review over 2-3 days. Think of it as a research sprint. 4. Take a step back and think about what worked and what didn't. 5. Rinse and repeat with a new type. Because the time is limited, you can only do a taster of what you would do with a massive set of papers, so don't beat yourself up if you only do like 5 papers. It's all about practicing the method. By the time you’ve done this with all 14 types, you’ll have a skillset that most researchers can only dream of. ### Ready, set, review! Alright, it's go time! Here's your homework: 1. Read the [​Grant and Booth paper​](https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1471-1842.2009.00848.x?ref=lennartnacke.com) linked below. Study Table 1 in the paper: Spend 30 minutes examining the details of each review type. Note the differences in methods and outputs. 2. Choose one new type: Select an unfamiliar review method that intrigues you. 3. Find an example: Locate and read a published review using your chosen type. Pay attention to its structure and approach. 4. Outline a mini-review: Plan a small-scale review using the new method. Choose a topic you’re passionate about to make it more engaging (as mentioned above). 5. Schedule practice: Block out time in your calendar to conduct your mini-review (following the sequence I just mentioned). Treat it like any other important academic task. ### Reference Grant, M. J., & Booth, A. (2009). [​A typology of reviews: an analysis of 14 review types and associated methodologies.​](https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1471-1842.2009.00848.x?ref=lennartnacke.com) *Health information & libraries journal*, *26*(2), 91-108. Rome wasn't built in a day, and you won't master all 14 review types overnight. It’s a journey, but one that will set you apart in your field. With each new type you master, you’ll gain new perspectives and approaches to research. Before you know it, you'll be the MacGyver of literature reviews! Don’t let fear or habit hold you back. Embrace the full spectrum of literature review types, and watch your understanding of your field transform. Drop your next review like it's the hottest track of the year. [Liked this post? Leave a tip.](https://lennartnacke.com/#/portal/support) **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to master tone and voice in academic writing URL: https://lennartnacke.com/how-to-master-tone-and-voice-in-academic-writing/ Last updated: 2024-11-21T00:21:41.000Z Alright, my academic maestros, it’s high time to dust up the meat and potatoes of maintaining a consistent tone and voice throughout your paper. Because, let’s not kid ourselves, if your paper’s tone jumps around hyper like a caffeinated squirrel, you’re going to lose your readers faster than you can say “peer review.” And rest assured, we don’t want any of that. So, slurp up some brain juice (yes, brain freeze on a Slushee works for me, folks), and step on that gas pedal, because we’re in high pursuit of mastering a consistent tone. Tone sets the vibe of your work, and it hinges on the words you choose and how you stitch them together. The key here is to match your tone to the audience and purpose of your work. Your tone should be polished, professional, and error-free. Word choice and sentence structure are the building blocks of this formality. Stick to factual statements, avoid inserting personal opinions, and maintain a steady, clear voice throughout; save the personal rants for your diary or sh\*t like this newsletter (and now you know why I’m here). Consistency is critical. Our tone doesn’t have to be stuffy, just authoritative and precise, giving your arguments the respect they deserve. ![Big mind map with a list of things that keep your style and voice consistent in academic writing.](https://lennartnacke.com/content/images/2024/08/CleanShot-2024-01-03-at-14.10.50.png) This is one way you could keep your voice and style consistent. ### 1\. Your Voice is Your Academic Soulmate First things first, you have to find your voice. And no, I don’t mean that karaoke singing voice you use in the shower. (Don’t even get me started on smashing Jet’s “Are you gonna be my girl” in the shower, because I will do it every single time.) I’m talking about your writing voice, genius. It’s that unique blend of personality, style, and attitude that makes your writing distinctly yours. Think of it as your academic fingerprint—no two are exactly alike. But it can’t be all fun and casual like Uncle Lenny here in this newsletter; your academic tone must be formal, objective, authoritative, clear, and concise. So, keep it formal but ditch the jargon. We’re not having a casual chat at Tim Hortons about the weather, eh, but that doesn’t mean you need to sound like a robot. And stick to the facts. Don’t let your opinions run the show. Unless you’re specifically asked to share your two cents, keep it objective. That’s the only time the “I” and “we” stuff gets the green light. You must also back up everything you say with solid evidence. Leave the emotional drama and wild exaggerations at the door. Academic writing thrives on hard facts, not hyperbole. Adding multiple perspectives to your voice is generally a good idea. Academic writing is all about being inquisitive and analytical, so put on your detective hat and compare different views. Show off your ability to juggle ideas like a pro. And, always, always, always, remain clear in what you say. Academic language should be explicit and straightforward. Use signposting to guide your reader smoothly from one section to the next or from one idea to another. But do you ever notice how some writers kick off every sentence with the same word? It’s like they’re stuck on repeat. Mix it up, folks! Don’t bore your audience to death with predictable openings. - Choose your words wisely. You’re going for a formal tone, so don’t suddenly throw in “YOLO” in the middle of your methodology section. - Maintain a consistent level of technicality. Don’t go from explaining quantum physics to your grandma to discussing string theory with Stephen Hawking’s ghost. - Vary sentence length at regular intervals to keep things engaging, but don’t make it too long or too short. Papers aren’t a place for haikus or overly dramatic prose. Find the race around the middle ground. Mixing up your sentence structure is like adding a playlist to a workout. If every track is the same tempo, you’ll zone out fast. Your voice isn’t about spouting opinions like a caffeinated parrot. It’s about the unique way you string words together to make your argument. It’s WHO the reader ‘hears’ when they’re up to their neck in your text. Are they hearing you loud and clear, or are they drowning in a sea of quotes and citations? Academic sources are your backup dancers, not the lead singer. They’re there to support YOUR show, not steal the spotlight. Your voice should be the star, belting out authoritative claims like a rock star on stage. Developing your voice takes practice. It’s like training for a marathon—you don’t just wake up one day and run 26.2 miles. You’ve gotta put in the work, build that confidence, and know that what you’re saying is worth hearing. Once you’ve found your voice, hold to it as tight as a koala. Consistency is key here. If you start off sounding like a stuffy Victorian professor and suddenly switch to a cool, hip millennial halfway through, your readers will get whiplash. Academic whiplash is not a good look on anyone. Avoid it. _This post is for subscribers only._ ### How to create compelling CHI paper titles: The secret formulas revealed URL: https://lennartnacke.com/how-to-create-compelling-chi-paper-titles-the-secret-formulas-revealed/ Last updated: 2025-09-04T19:02:20.000Z Ever stared at a blank screen, wondering how to craft the perfect title for your research paper? You’re not alone. As a professor who’s reviewed hundreds of papers, I’ve seen titles that rock like Metallica and titles that… well, let’s just say they could use some work. Today, I’m pulling back the curtain on what makes a title stand out in the competitive world of CHI papers (that’s the BIG Human-Computer Interaction (HCI) conference for the uninitiated). But yes, titles. You know, that tiny chunk of text that can make or break your chances of getting noticed in an ever-expanding sea of run-of-the-mill research. Yeah, to get a better idea of what title patterns we see in award-winning papers, I forked them over to my trusty AI sidekicks (Claude, ChatGPT, and Gemini) to get their take on the secret patterns behind those best paper titles. Let me tell you, the insights are gold. And it was a fun exercise. So, put on those reading glasses, and let’s decode the DNA of top-tier research paper titles. Trust me, by the end of this newsletter, you’ll be crafting titles that make peer reviewers sit up and take notice. Here are the 7 killer formulas that will have the review committee eating out of your hand and begging for more. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## Seven Powerful Title Formulas to Capture Eye Balls After letting my LLMs run wild over the title examples I fed them, I’ve identified seven key patterns that dominate the CHI context. Let’s break them down: ### **1\. Colon Combos** **Pattern:** ℹ️ ****Main Title: Subtitle** **Example:** 📄 ["Computing and the Stigmatized: Trust, Surveillance, and Spatial Politics with Sex Workers in Bangladesh"](https://doi.org/10.1145/3613904.3642005?ref=lennartnacke.com) This is the heavyweight champion of CHI titles. It’s also the most common. The colon combo grants you the flexibility to capture the essence of your work in a concise, attention-grabbing main title, and then use the subtitle to thoroughly explore the specifics of your topic. It’s like a one-two punch: the main title grabs attention, and the subtitle delivers the specifics. It’s popular for a reason — it works. ### **2\. Bold Statements** **Pattern:** ℹ️ ****A declarative sentence that summarizes your findings** **Example:** 📄 ["Constrained Highlighting in a Document Reader can Improve Reading Comprehension"](https://doi.org/10.1145/3613904.3642314?ref=lennartnacke.com) Straight to the point, no fluff. This title pattern is all about conveying the core of your research in a clear, compelling way. Slap your main finding front and centre. Science is, after all, the abstraction of simple principles from complex data. Titles that boldly state the key contribution or finding of your work are often the most impactful. Nobody’s got time for cryptic titles. Be direct. Be unforgettable. This pattern tells readers exactly what you’ve discovered. It’s confident, clear, and compelling. If your study has yielded a strong, definitive conclusion, this is the way to go. ### **3\. Curiosity Sparks** **Pattern:** ℹ️ ****A question that your research answers** **Example:** 📄 ["Apple's Knowledge Navigator: Why Doesn't that Conversational Agent Exist Yet?"](https://doi.org/10.1145/3613904.3642739?ref=lennartnacke.com) Less common, but when done well, it can be incredibly effective. Slap a question mark on that title and watch the magic happen. This pattern is all about harnessing the power of curiosity. It’s instant intrigue. You pose a tantalizing question that your research aims to address, instantly drawing the reader in. Titles like this feel more narrative in nature, inviting the audience to join you on an intellectual journey. And when you deliver a satisfying answer in the paper, it’s like hitting a home run. ### **4\. Gerund Gymnastics** **Pattern:** ℹ️ ****Starts with a gerund (-ing verb) to emphasize process** **Example:** 📄 ["Designing for Harm Reduction: Communication Repair for Multicultural Users' Voice Interactions"](https://doi.org/10.1145/3613904.3642900?ref=lennartnacke.com) This pattern puts your research process front and centre. Titles that kick off with a gerund (a verb ending in -ing) showcase the active, hands-on nature of your work. It’s all about the verbs, baby! These titles feel more dynamic. They point to the methodologies that generated your findings. Crafting titles that emphasize the “doing” rather than the “being” can make your work feel more engaging, innovative, and impactful. It shows that your work is actively contributing to the field. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **5\. Concise Concepts** **Pattern:** ℹ️ ****A short noun phrase that encapsulates your topic** **Example:** 📄 ["Cosmovision Of Data: An Indigenous Approach to Technologies for Self-Determination"](https://doi.org/10.1145/3613904.3642598?ref=lennartnacke.com) Sometimes, less is more. Forget the frills — this pattern oozes pure substance. This pattern trades flashy style to zero in on the core concept or framework of your paper — no muss, no fuss. We’re talking short and concise word groupings that capture the heart of your work. Titles like these are memorable and impactful. They act as yummy little clues that get your curiosity going, hinting at the juicy insights you’ve uncovered. This pattern works well when you have a clear, focused research topic that can be summed up succinctly. ### **6\. Acronym Artifacts** **Pattern:** ℹ️ ****Design artifact or system with an acronym before the colon** **Example:** 📄 ["DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing"](https://doi.org/10.1145/3613904.3642639?ref=lennartnacke.com) This pattern is like giving your research its own superhero name. You start with a catchy acronym or tool name, then use the colon to explain its superpowers. Titles like this feel cutting-edge and tech-savvy. They show off the cool new systems or prototypes you made for your research in a memorable way. Plus, let’s be real — we all love a good acronym. It’s memorable branding that’ll stick in your mind like gum on a hot sidewalk. An acronym you won’t forget even if you tried. These titles tap into the reader’s inner tech nerd and make your work sound totally legit and on-trend. Yes, admittedly, it’s a bit cheesy but perfect for showcasing any nifty new tools or systems you’ve cooked up. It gives your creation a catchy identity while clearly naming its value and contribution to the field. ### **7\. Journey Mappers** **Pattern:** ℹ️ ****“From X to Y” Structure** **Example:** 📄 ["From Disorientation to Harmony: Autoethnographic Insights into Transformative Videogame Experiences"](https://doi.org/10.1145/3613904.3642543?ref=lennartnacke.com) This pattern is like crafting a mini-story in your title. It shows readers the journey your research takes, from point A to point B. It’s perfect for studies that explore transitions, evolutions, or comparisons. The “from-to” structure gives your title a natural flow and narrative arc. The pattern screams transformation. It’s like a straightforward progression with an unmistakable beginning and ending. Think epic personal evolution or cutting-edge tech breakthroughs. It hints at the broader themes and implications of your work. This pattern is ideal for showing the transformative nature of your findings or the pivotal insights you’ve uncovered. It feels almost poetic, guiding readers through the arc of your research like a mini-epic. Use this pattern when your research tracks changes over time or compares two distinct states. It’s particularly effective for studies on user experiences, technological developments, or shifts in methodologies. **Pro tip:** Make sure your “X” and “Y” are distinct and intriguing. The contrast between them should make readers curious about the journey you’re presenting. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ## **Words Matter** Now that we’ve got the structures down, let’s talk about the building blocks — the words themselves. Here’s what makes CHI titles tick: 1. **Clarity.** Avoid jargon and complex terms unless they’re absolutely necessary (e.g., acronyms). Anyone in your field should be able to understand your title. 2. **Keywords.** Include relevant keywords that reflect your research content. This isn’t just for human eyes; it helps with discoverability in databases too. 3. **Action Verbs.** Words like “Exploring,” “Designing,” and “Evaluating” add energy to your title and show the active nature of your research. 4. **Concise.** Every word should earn its place. If you can say it in fewer words without losing meaning, do it. 5. **Hyphenations.** Don’t be afraid to use hyphenated adjectives like “context-aware” or “user-centered” to pack more meaning into less space. 6. **Framework.** Frameworks are like Raymond. Everybody loves them. Terms like “framework,” “taxonomy,” “approach,” and “model” signal to readers that you’re contributing something structured and substantial to the field. And HCI loves those guidelines. ### Action Steps: Upgrade Your Titles Ready to craft your own CHI-worthy title? Here’s a step-by-step guide: 1. **Choose your pattern.** Pick one of the seven patterns that best fits your research. A colon combo is a safe bet if you’re unsure. 2. **Inject Action.** Use strong verbs to describe what your research does. 3. **Sprinkle keywords.** Include 1–2 key terms that are crucial to your research area. 4. **Be specific.** Avoid vague language. Tell readers exactly what you studied or discovered. 5. **Keep it tight.** Aim for 10–15 words total. If it’s longer, see what you can cut. 6. **Test it out.** Read your title aloud. Does it flow? Does it accurately represent your work? It takes some practice to get your title just right. Don’t sweat it if the first draft isn’t flawless; that’s totally normal. But with these tips, you’ll be well on your way to crafting titles that really grab people’s attention and get you noticed in academic circles. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) A great title is your research paper’s first impression. It’s worth taking the time to get it right. Ever noticed how the length, punctuation, word choice, and prepositions in research article titles can make or break how a paper declares its purpose and content? Yeah, it’s that important. While some key title traits are pretty straightforward, others require a more nuanced, field-specific understanding. So, next time you’re staring at that cursor, wondering how to encapsulate months of work in a single line, remember: you’ve got this. Apply these insights, trust your instincts, and watch your research make waves in the CHI community. And let me know if you’d like a more general breakdown of how to do these types of title analyses with your favourite LLMs. Happy title writing and good luck with your submissions. [Liked this post? Leave a tip.](https://lennartnacke.com/#/portal/support) **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to succeed at paper writing URL: https://lennartnacke.com/how-you-succeed-at-paper-writing/ Last updated: 2025-02-12T01:34:02.000Z ![audio-thumbnail](https://lennartnacke.com/content/media/2024/08/How-you-succeed-at-paper-writing_thumb.png) How you succeed at paper writing 0:00 /648.85551 1× Can you believe our new term is only 2 weeks away. So, exciting. And so little summer left. On the bright side, we started a bit early and are running some free [​guest lectures​](https://www.linkedin.com/events/effectiveuseoffigmainthelifeofa7230263493776605185/theater/?ref=lennartnacke.com) on LinkedIn [​this week​](https://www.linkedin.com/events/10bigchangescomingtodigitalexpe7230272080125009920/theater/?ref=lennartnacke.com) and next. If you're on the academic job market currently, I found [​this interesting list of CS undergrad institutions that are hiring​](https://cs-pui.github.io/?ref=lennartnacke.com). ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 8,000+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Alright, my pickle popsicle connoisseurs, let’s talk about the holy trinity of paper writing: theory, research questions, and results. Because let's face it, if you can't connect these three, your journal paper is about as useful as a chocolate teapot in the Sahara desert. And I got asked about this by someone on X, so I figured I'll just write it up. ### How to Not Suck at Paper Writing If you want your research to hit like a freight train, clarity is your hidden asset. You’ve got to smudge your theory, questions, and results together so smoothly that the reader can't help but be hooked. Everyone prefers a nicely blended milkshake over a fruit cocktail, friends. Each section should flow like a well-oiled machine, leading them through your argument without a hitch. So, create an experience that leaves no room for confusion. ### 1\. Build a Theoretical Framework That'll Make Darwin Jealous First things first, you need a theoretical framework that's so solid, it could withstand a nuclear blast. Imagine trying to build a skyscraper on quicksand—disaster, right? Same goes for your research questions. You can’t write them without knowing a little bit about what’s going on. Lay out your conceptual framework and dig into the key literature to validate it. Connect it. Change it. Own it. Yes, you can show off your smarts a little, but this is about proving why your ideas matter in the grand scheme of academic discussion. Paint a vivid picture, draw your reader in, and make them see the brilliance of your thoughts. Trust me, having such a groundwork will make your questions hit harder and resonate longer: - Dive into the literature like it's a pool full of margaritas. Read everything you can get your hands on. Get drunk on knowledge for a hot second. - Handpick the gems from relevant theories and mash them together into your own theoretical delight. Think of your theoretical underpinnings as the glue that holds your research questions and findings together. Without it, your work falls apart like a cheap IKEA bookshelf. - Explain your framework so clearly that even your grandma could understand it (and she still thinks the internet is a series of tubes). A solid theoretical foundation isn't just a fancy way to justify your research questions. It's your interpretive lens that turns dry data into a gripping saga. A good framework turns the narrative of your paper beyond coherent into an engaging page-turner. Your theoretical framework is the backbone of your paper. Without it, you're just a sad, floundering fish gasping on the deck of academic despair. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 2\. Craft Sharp Research Questions Now that you've got your theory on lock, it's time to ask some questions that'll make your fellow researchers say, "Damn, why didn't I think of that?" Your research questions are the beating heart of your paper—they're what drive the entire investigation. To make your questions hit hard and stay relevant, craft them with a brutal honesty that springs straight from the heart of your research. Trust me, these aren't your run-of-the-mill inquiries; they're born from the fiery depths of your theoretical insights, making the reader say, "Ah, I see what you did there." The goal? Make them sweat, make them think, and most importantly, make them see the why behind your questions. Channel your inner philosopher and question everything. Make every word count, ensuring your questions not only emerge organically from your theoretical framework but also push the boundaries of existing knowledge in your field as you strive to address significant gaps or unresolved issues in the literature. This fiery tango between your theoretical framework and research questions isn't just for show. Think of it as the engine driving your study forward. Clear questions? They're your compass, they guide the hunt for insights and give meaning to the insights in your findings. Without them, you're just shooting in the dark. - Make sure your questions are tighter than your jeans after Thanksgiving dinner. No room for ambiguity here, folks. - Address gaps in the literature that are bigger than the plot holes in a Michael Bay movie. - Keep 'em short, sweet, and empirically testable. If you can't answer it with data, it's not a research question, it's a philosophical musing. Rather than regurgitating what's already been done, your research questions should push the boundaries of the field, exploring uncharted territories and building on the current state of knowledge in novel and exciting ways. In short: make them count, make them matter, and make them impossible to ignore. ### 3\. Design a Mighty Methodology Your methodology? That's your magic elixir, Gandalf. Without it, you're just another cook in the Overcooked kitchen getting yelled at for chopping the meat too slow. Methodology sets you apart from all the other academic schmucks out there. If you want your research to actually matter, then you need a methodology that’s not just fancy on paper but packs a real punch. Forget the cookie-cutter stuff that everyone knows—go for methods that are rigorous and custom-built to unearth the juicy insights your theory craves. Trust me, your findings should blow minds and turn heads in your field, not collect dust. Best way to do that is to make sure your methodology is as tight as an overcooked chicken drumstick. Imagine crafting a methodology so simple and useful that it adds something genuinely new to the conversation. It should be more than a simple checklist. You want to sync your data collection and analysis with the core aims of your study, making sure every bit of it screams reliability and validity. And hey, stay nimble—adapt as you go. Research isn't static, so why should your approach be? So make it good: get creative, get strategic, and get down to the nitty-gritty of what you're really trying to achieve. - Create a research design that fits your questions like a glove. No square pegs in round holes here. The real power of your methodology lies in its ability to directly address the specific research questions and objectives you outlined earlier. - Describe your sample, measures, and procedures with the loving detail of a Nicholas Sparks novel. Leave no stone unturned. Carefully select your sampling approach to ensure that the participants or subjects in your study are representative of the target population you wish to draw conclusions about. - Justify your choices like your academic life depends on it (because some of it does anyways). Paint a vivid picture: show exactly how your work kicks some serious academic butt, filling those gaping holes you so brilliantly spotted earlier. Make them fit into the big picture and add to the big narrative you’re presenting in your paper. ### 4\. Present Results That Sing All right, you've got your theory, questions, and methodology locked down—now it's time to bring home the bacon. Doing your results presentation right is non-negotiable. This is where you turn all that sweat and tears into a story so gripping, even your data will start talking! You want each fact and figure to scream your theoretical framework and answer those burning research questions. Make your reader sit up and listen. Don't just rattle off a list of stats and numbers—that's like listening to a middle school band play the national anthem. Show how your findings connect to the earlier theories. Your results should answer your research questions. When showing off your results, cut the fluff and get straight to the point. Throw in some killer visuals—graphs, tables, you name it—to make your data pop. Make your audience see why your work matters. - Keep it clean, clear, and organized. If your results section looks like a Jackson Pollock painting, you're doing it wrong. - Stick to the facts. Save your brilliant interpretations for later. - Use visuals that'd make Edward Tufte weep with joy. A picture's worth a thousand words, especially when it comes to p-values and data-ink ratios. As you present your findings, be clear and concise. Each data point must help answer your research questions and guide the reader to your study's implications. ### 5\. Interpret Like Your Career Depends On It Okay, you've done the legwork, now let's knock it out of the park. Dive into what your findings really mean, tie them back to your theories and burning research questions, and give a nod (or a sly winkie wink) to the existing literature. Show how you're not just another voice in the crowd, but a paradigm shifter. This is your chance to flex those intellectual muscles and prove why your work matters—big time. Here's your chance to show off those brain gains and prove you’ve got the chops. Dive into how your findings shake up old-school theories or back them up. Paint a picture of the future research playground your study creates. Make sure your insights hit the sweet spot—where your theoretical beef meets the juicy core of your field’s latest buzz. A killer research paper spins a gripping story, seamlessly stitching together theory and real-world impact. Paint a picture of how your research doesn't just answer questions but kicks open doors for future research. All the while, keep your eye on the main questions that started this whole adventure. Connect those theoretical dots: - Explain your findings like you're the Sherlock Holmes of academia. No stone left unturned, no theory left unconnected. - Show how your work is pushing the boundaries of human knowledge. Be humble, but don't be afraid to toot your own horn a little. - Admit your limitations like a recovering alcoholic at an AA meeting. Honesty is the best policy, especially in academia. Your interpretation part shouldn't just navel-gaze at your findings. It needs to punch its way into the broader academic brawl, showing how your work throws some serious weight into ongoing debates. Make it count. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 8,000+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ### 6\. Bookend Your Brilliance Your intro and conclusion are like the buns of a delicious academic burger. Your intro isn't just a stage setter—it's the spotlight. It's where you drop the thesis and those burning research questions that’ll drive your hunt for answers. Think of the conclusion as the grand finale. It's where you bring the homerun why your findings matter. Put emphasis on your theory again. Revisit those intro questions. Your conclusion is your Obama mic drop opportunity in the academic space. Make your academic sandwich tasty: - Start with a hook that'll grab your readers faster than a cat video grabs attention on Instagram. - Give a sneak peek of your theoretical framework, questions, methods, and findings. It's like a movie trailer, but for nerds. - End with a bang! Show why your work matters in the grand scheme of things. Make folks remember you. Writing a killer research paper is all about plying theory, research questions, and results together into a story that gets at your reader like Jack Reacher gets at his enemies. He grabs them by the collar and doesn’t let them go until it’s done. A paper must do more than just spew out facts. It has to present an argument clear as day and explain why your findings matter. Your readers must follow your logic like breadcrumbs, making your conclusions hit hard and ignite curiosity in your subject. Plus, they’ll walk away with a deeper grasp of the theoretical heavyweights backing your work. Win win. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to use writing registers URL: https://lennartnacke.com/4-writing-registers-to-go-eminem/ Last updated: 2025-09-04T18:50:34.000Z Here we go again with another newsletter that will turn any Harry Potter into an instant Gandalf. It’s time to talk about writing registers, and trust me, this isn’t just grammar girl getting comfy on the couch. As an academic writer, you need to be aware of the diverse registers at your disposal — from the informal to the abstract — and how to wield them with precision to captivate your readers. Let’s put on our swimsuits and dive into the wild ocean of language registers that’ll make your writing dance as smooth as a TikTok cultist. As you already know, writing isn’t just about stringing words together and hoping for the best. Nobody throws spaghetti at the wall anymore. It’s about knowing your audience and hitting them with the perfect linguistic cocktail. I’ll have a Mojito, please. Hop on your Nimbus 2000, Neville Longbottom, because we’re about to go full Eminem on those writing styles. A register is essentially the level of formality in your writing, and as an academic, you’ll need to at least know about four distinct registers: *informal*, *popular*, *conventional*, and *abstract*. ![Four different writing registers displayed in a mind map.](https://lennartnacke.com/content/images/2024/08/Screenshot-2024-08-12-11.33.37.png) Mind map of different writing registers. ## 1\. Informal Register First up, we have the informal register, the casual cousin of the bunch. This is where you can let your hair down, crack a few jokes, and speak directly to your audience in a relatable, conversational tone. Think of it as the comfy sweater you wear on a lazy Sunday — it’s all about building a genuine connection with your readers. Picture this: you’re chatting with your bestie over a couple of beers or texting your significant other about the latest episode of Ted Lasso. It’s characterized by a relaxed tone, colloquial language, and maybe even a few emojis 🏖️ for good measure. That’s the informal register, in a nutshell. It’s chatty, it’s emotional, and it’s about as far from an academic paper as you can get. ### Pros - It’s engaging as f\*ck. Your readers will feel like you’re talking directly to them. - It can make even the driest topics feel like a juicy gossip session. ### Cons - It might not fly in academic circles. Sorry, Professor Umbridge. - There’s a fine line between approachable and “Dude, are you even taking this seriously?” **Example:** “You’d be amazed at how AI can whip up a whole article in just seconds!” **When to use it:** Blogs, social media, newsletters like this one, or anywhere you want to sound like a human and not a textbook. ## 2\. Popular Register Next up, we have the popular register, the slightly more formal cousin of the informal register. This is the cool kid of the writing world. It’s clear, it tells a story, and it doesn’t make your brain hurt with technical jargon. This is where you take your academic knowledge and package it in a way that’s digestible for a general audience. Think of it as the “for beginners” version of your topic. You’re still keeping things relatively casual and conversational, but you’re dialling back the slang. (Tone down those pop culture references, Barbenheimer.) Imagine you’re explaining a complex topic to your grandparents or your neighbour who has zero background in your field. ### Pros - Your grandma could read it and get the gist. - It’s perfect for explaining complex ideas without putting people to sleep. ### Cons - Hardcore traditional academics might sniff at its simplicity. (I see you there, Minerva McGonagall.) - You might have to sacrifice some nuance for the sake of clarity. **Example:** “Generative AI tools can quickly create content, saving time and boosting productivity for writers.” **When to use it:** Science magazines, public lectures, or anytime you want to make science just a little more sexy. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## 3\. Conventional Register Now, let’s talk about the conventional register, where you break out the suit and tie, or perhaps your best Hermione Granger impression, and start speaking the language of academia. This is the little black dress of writing styles. It’s formal but not stuffy, clear but not simplistic. It assumes your readers have a few brain cells available to rub together, but it doesn’t require a Ph.D. to understand. Fire up your speech synthesizer, Stephen Hawking, and turn everything into precise, measured, and impeccably intellectual prose. This is the register you’ll use for most of your scholarly writing — think research papers, literature reviews, and technical reports. It’s the linguistic equivalent of a perfectly brewed cup of Earl Grey — refined, complex, and with just a hint of sophistication. Save the Chai Lattes for another day, friends. The conventional register is all about using standard grammar, complex sentence structures (think Sherlock Holmes deductions), and precise, technical vocabulary. You’re essentially putting on your “serious academic” hat and crescendo the formality to a Mr. Darcy level. ### Pros - It’ll make you sound like you know your sh\*t (because you do, right?). - It strikes a balance between being understandable and intellectually respectable. ### Cons - It might go over some people’s heads if they’re not familiar with the topic. - It can sometimes be about as exciting as watching paint dry. **Example:** “The application of generative AI in content creation significantly reduces time spent on drafting and editing.” **When to use it:** Academic papers, professional reports, or anytime you need to impress someone with a fancy title. ## 4\. Abstract Register Finally, we come to the abstract register, the Kafka of the writing world. This is where you take language to the next level, crafting sentences that are so dense, they’d make Nietzsche blush. If the conventional register is Earl Grey, the abstract register is a triple shot of espresso — rich, complex, and not for the faint of heart. Check your heart rate, Messi. This register is all about sounding smart. It’s about using big words, convoluted syntax, and a level of abstraction that would make even the most senior academic scratch their head. Think of it as the linguistic equivalent of a Picasso painting — beautiful, sure, but good luck trying to explain what the hell is going on. It’s complex, jargon-heavy, and about as clear as the ending of Lost. Use this if you want to make sure absolutely no one understands what you’re talking about. Why would you ever use this register? Glad you asked. Well, sometimes, you just need to flex your intellectual muscles and show off your mastery of the language. Maybe you’re writing a philosophical treatise or a highly specialized journal article. Heck, maybe you just feel like adding a bit of Shakespeare to your grocery list. In the abstract register, you’re not just writing about your topic — you’re exploring the very nature of it, diving deep into conceptual frameworks and theoretical underpinnings. You’re the Deep Down Dumbledore of academia, sprinkling your prose with big words, complex syntax, and a healthy dose of intellectual snobbery. Lumos! ### Pros - It might impress other experts in your field. - It’s great for conveying complex ideas… to other complex thinkers. ### Cons - Most people will think you’re speaking in tongues. - It can make even brilliant ideas sound like pretentious word salad. **Example:** “The utilization of generative algorithms in automated content production optimizes workflow efficiency and enhances textual output quality.” **When to use it:** Rarely. Like, almost never. Unless you’re writing for a hyper-specialized academic theory journal and you hate your readers. Ok, I’m outta here. Four writing registers that’ll make you a linguistic chameleon. Great writing is more than what you say, but it’s how you say it. So choose your register wisely, and may the words be ever in your favour. BTW: if anyone gives you crap about your writing style, just tell them you’re fluent in all four registers and they can kiss your funky little thesaurus. Here is the tweet that spawned it all: > Why does your writing fall flat? > > The answer lies in the register you use. > > The more formal your writing, the harder it becomes to understand. > > In much of our writing, registers range from informal to abstract. > > Here’s a breakdown and suggestions: [pic.twitter.com/AmnxI68tu2](https://t.co/AmnxI68tu2?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [August 13, 2024](https://twitter.com/acagamic/status/1823328716786929749?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) On you go, friends. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Your 1000x citation counts don't matter URL: https://lennartnacke.com/your-1000x-citation-counts-dont-matter/ Last updated: 2025-09-04T19:38:16.000Z Big thanks to you and everyone who’s emailed me back about wanting to leave some feedback for my upcoming book, “The Resilient Researcher.” It’s not too late if you want to help me write the early draft. It’s inspired by our successful [​PhD Thesis Fasttrack webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com), which is still available for purchase. Also, on the [Twitter/X sphere](https://go.lennartnacke.com/twitter?ref=lennartnacke.com), I had a new record tweet with 8+ Million views about how to use your emotion words (it’s always the random ones that go viral): > Geoffrey Roberts's emotional word wheel helps describe emotions. [pic.twitter.com/2m5Lx9R3km](https://t.co/2m5Lx9R3km?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [July 23, 2024](https://twitter.com/acagamic/status/1815763936819732654?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) Also, big shoutout to my grant mentees, who have successfully submitted their NSF CAREER proposals and who are writing their NSERC Discovery grants with my help. Working with you is inspiring. If you’re struggling with grant writing (or any other academic writing), [​let’s book a Discovery call to see if I can help you, too.​](https://go.lennartnacke.com/discovery?ref=lennartnacke.com) ### The dirty truth behind citation counts and impact You’ve probably heard it a million times: citation count is the ultimate measure of a paper’s importance. But is it really? Let’s peel back the layers of this pickled little onion and see what’s lurking beneath its surface. Spoiler alert: It’s not Sauerkraut. I get weirded out whenever people boast about their citation count (but I do it myself online and definitely catch flag for it when I do). Because this is how the system works. Citations mean impact. No doubt. But do they measure what’s actually advancing the field? How could a paper be highly cited yet not be particularly valuable? Is that possible (sure hope that’s not the case with my papers)? This thought spawned this newsletter issue. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) After digging into the literature a bit and talking to other seasoned researchers, I’ve come up with some insights that might just change the way you think about citations and research impact. Ready to challenge some assumptions? Let’s crack our heels on these cobblestones. ### Citation Count Issues Submitting your paper and seeing the citation count rise can feel like you’ve conquered a mountain. But wait — citation counts can be tricky. They don’t always indicate the quality or importance of your work. Some papers get cited often because they’re controversial or have mistakes, not because they’re revolutionary. Citation counts are kind of like that shiny sports car you dream about. They look impressive, they’re fun to talk about at parties, but they don’t tell the whole story. In scientific publishing, citations are more like a Swiss Army knife — they’ve got tons of uses, some obvious, some not so much. We’re talking everything from genuine admiration and grant support, to subtle shade-throwing. So, before you get swept away by those flashy numbers, remember, there’s way more to this citation game than meets the eye. Not all citations carry the same weight. Some cite a paper’s core intellectual contribution; others refer to a minor point. Occasionally, citations are even used to critique or challenge the cited work. In academia, it’s essential to remember that “not all citations are equal.” [​A 2014 study found​](https://doi.org/10.1002/asi.23179?ref=lennartnacke.com) that counting raw citation numbers is a lousy measure of a paper’s influence. The researchers tackled this issue by asking authors to identify the key references in their work, creating a dataset of citations ranked by their academic impact. Research on the [​”intellectual lineage” of science​](https://doi.org/10.1162/qss%5Fa%5F00186?ref=lennartnacke.com) shows that new studies often build upon the work of past influential researchers. Using network-based methods, these studies assess the importance of various references within a paper. It’s clear that not all citations hold the same significance. Other factors can mess with citation counts too. Groundbreaking or interdisciplinary research might take a while to get noticed and rack up citations as [​it finds its footing in the field​](https://doi.org/10.1007/978-3-319-10377-8%5F12?ref=lennartnacke.com). Likewise, niche studies or research that goes against the grain might [​get fewer citations, even if they’re incredibly valuable​](https://doi.org/10.1007/s11192-021-04055-1?ref=lennartnacke.com). So, where does that leave us? While citation metrics have their place, they’re just the starting point, not the final say on a paper’s impact. The most cited papers aren’t always the most valuable — the true intellectual giants might be hiding among the less-cited references, just waiting to be discovered. ### The Halo Effect Ok, let me explain this “halo effect” thing, Master Chief. When a paper comes out, published in a high-impact journal (or conference) or written by some big-shot professor from Harvard or Yale. Instant street cred, right? People start throwing citations at it like confetti, even if the actual research is kinda “meh.” It’s like those RayBan sunglasses you buy just because of the brand name, even though they might not actually be the best fit for your face. Turns out, there’s science to back this up. [​Studies show that papers from those big-name journals tend to get cited way more often​](https://doi.org/10.48550/arxiv.2002.10033?ref=lennartnacke.com), regardless of whether they’re actually good or not. It’s like being in the VIP section of a club — you get more attention, even if you’re not the most interesting person in the room, Kanye. So, what’s the takeaway for you here? Don’t be fooled by the glitz and glam! When you’re checking out a research paper, look past the shiny journal title and the impressive author list. They mean nothing, Jon Snow. Put your reading goggles on and see what the research actually says. Then, decide for yourself if it’s worth all the hype. It’s the content that counts, not the packaging! (Cue the people emailing me about buying their books just for the cover. I see you there. I do it, too.) ### Impact Beyond Citations Real impact isn’t just about racking up citations. Consider how your research shapes policy, informs practice, or sparks further studies. The most valuable research often drives significant advancements or tackles real-world problems, even if it doesn’t accumulate citations right away. Quantifying the “intellectual lineage” of science mentioned earlier shows that highly-cited papers [​aren’t always the ones that build upon the most influential past work​](https://doi.org/10.48550/arxiv.2202.07862?ref=lennartnacke.com). Instead, some lesser-cited papers may draw from the most important foundations, quietly advancing the field. Picture a study that completely changes our understanding of the origins of life. Its findings are so transformative they become the new standard, making additional citations redundant. Now consider a paper that introduces a pioneering new technique. Its value isn’t in the number of direct citations but in the future discoveries it sparks, the problems it solves, and its lasting impact on the field. ## Sign up for Write Insight Shift into a smarter, more resourceful researcher in 9 minutes tops per week. Join 4481+ readers Email sent! Check your inbox to complete your signup. No spam. Only the good stuff. Unsubscribe anytime. I treat your inbox like mine. ### A Closer Look at Citation Metrics Citation metrics like the h-index are often used to measure research impact, but they have big flaws. They can easily be skewed by self-citation, where researchers cite their own previous work, or citation circles, where a group of researchers frequently cite each other’s publications. These practices can inflate citation counts without truly reflecting the research’s intellectual significance or broader impact. Moreover, citation metrics don’t capture ***why*** a paper is cited — whether it’s to build on the core ideas, critique the methods, or just provide background information. Relying too much on citation-based metrics can give a skewed and incomplete picture of a scholar’s contributions. ### Quality over Quantity In academia, there’s often a massive push to crank out a ton of papers. But chasing quantity can sacrifice quality. Flooding the field with publications that lack rigour and significance dilutes your research’s overall impact. It’s far better to be picky and intentional, publishing fewer but higher-quality papers that genuinely advance your field. Quality should always trump quantity. The most impactful work comes from meticulous, in-depth research that pushes the boundaries, not from superficial studies aimed at increasing publication counts. When researchers really focus on doing thorough, impactful work, their studies can make a big difference in their field and really advance our understanding. ### Peer Review and Impact Peer review is the backbone of the whole academic publishing game, but let’s face it — not all peer reviews are created equal. Some papers slip through with barely a glance, while others get the full treatment. This inconsistency can mess with how a paper’s value is perceived. In a perfect world, peer review would judge the quality, significance, and originality of research, not just if it’s technically correct. But in reality, the process isn’t always so sharp. Reviewers might have biases, conflicts of interest, or just not enough expertise, causing them to miss big flaws or fail to see a paper’s true worth. Then there’s the anonymity factor. Without accountability, some reviewers may give half-baked or even careless reviews, while others might use their power to block papers that challenge their own work or beliefs. To fix these issues, some folks are pushing for changes in the peer review system. Ideas include more transparency, post-publication peer review, and focusing more on the research’s overall significance and potential impact, rather than just the technical details. ### Collaboration and Diversity Collaborative research often yields higher-quality, more impactful work than solo projects. Teaming up with folks from different backgrounds and areas of expertise can help us uncover cool new ideas, make our findings more solid, and improve the overall impact of our work. The mix of perspectives and approaches in a team setting lets us reach a deeper, richer understanding than we could on our own. On the flip side, single-author research might miss the multifaceted approach and comprehensive scope that define the most groundbreaking papers. Sure, solo work has its place and importance in academic discourse (especially popular in the humanities), but the biggest breakthroughs usually come from teams of researchers pooling their knowledge and skills to tackle complex problems from different angles. This collaborative magic pushes the boundaries of our fields in ways solo efforts often can’t match. Fight me on this. ### Action Steps (or should we call it ‘Homework’?) 1. **Evaluate Research Beyond Citations.** Look beyond citation counts when assessing research. Consider the content and contributions of the paper. Ask: What new knowledge does this paper bring to the field? 2. **Focus on Quality Research.** Aim to produce high-quality research that addresses significant gaps or challenges. Depth and rigor should be your priorities, not just high citation counts. 3. **Be Critical of “Halo Effect” Papers.** Be critical when reading papers, regardless of their source. Evaluate the methodology, data, and conclusions on their own merits. 4. **Highlight Broader Impact.** When presenting or writing about your work, emphasize its broader impact. Explain how your research can be applied or how it advances the field. 5. **Use Multiple Metrics.** Assess research impact with multiple metrics. Consider adding [​altmetrics​](https://www.altmetric.com/?ref=lennartnacke.com), which track mentions in social media, news outlets, and policy documents. 6. **Prioritize Quality Over Quantity.** Focus on producing fewer, high-quality papers that make significant contributions. Avoid diluting your research with high volume. 7. **Choose Rigorous Peer Reviews.** Publish in journals with rigorous peer review processes to enhance the credibility and impact of your work. Focus on highest quality venues. 8. **Seek Collaborative Opportunities.** Collaborate with researchers from different disciplines or institutions. This enriches your research and increases its impact. Tada. That’s my take on this. The next time you’re tempted to judge a paper by its citation count, be more like Homelander and laser that paper right through its head (kidding). Keep questioning, keep exploring, and keep making your mark. Until next time. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to use Dramatica story theory for academic papers URL: https://lennartnacke.com/how-to-use-dramatica-story-theory-for-academic-papers/ Last updated: 2025-09-04T18:59:29.000Z Our [​webinar recording for Thesis Fast Track​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com) received one of the most emotional testimonials I’ve ever received from a customer. I don’t know how to thank you, Camila, but this made me so happy to be truly able to help you with our work. [![](https://embed.filekitcdn.com/e/2Z57ZoCyqGzFe5hbTRuo8g/bRtbU6TraixhE7Q8iaQ4Fn)](https://learn.lennartnacke.com/phd-thesis-fast-track-recording/?ref=lennartnacke.com) Thank you for the amazing review, Camila. You rock. I was so motivated after this that I finished Chapter 1 of my upcoming book, “The Resilient Researcher,” that extends the ideas I talked about in the webinar. Send me an email if you’re interested in being one of the first to read it and give feedback on the book draft. Something weird happened this week on X. I posted a great storytelling graphic/table I found on Pinterest (I had not idea who the author was) and it somehow went viral with almost 3 Million views (Later found out that full credit goes to: [Ian Bousher, please give him full credit and download his newer high-resolution version](https://drive.google.com/file/d/1-3h4%5F%5FKVGRpB55Ujyb-UoaqbtV1d5qMm/view?ref=lennartnacke.com)). I only found the thing and thought it would be cool to share it. I think Ian did an amazing job with this. Sorry, I didn't know it was him before. He deserves all the credit for this. Just for everyone who joined us after this tweet, I had this idea of adapting another storytelling framework that's not in that older table I tweeted to academic writing. Read on. ### Dramatica Story Theory for Academic Papers You’ve poured your blood, sweat, and tears (and probably a few too many coffees) into your research. You’ve got groundbreaking results that could change the world (or at least your little cozy corner of academia). But now comes the real challenge: writing a research paper that doesn’t put your readers to sleep. Easier said than done, with one snooze fest more boring than the next. We often rightfully focus first on rigorous methodology and precise data analysis. This will always be the core of scientific publishing. But your job doesn’t have to stop there. What if I told you that the key to writing truly impactful research papers lies in storytelling? What if there’s a secret weapon you can use to make your research paper a lot more fun to read? Today, I’m showing you a powerful framework that can transform your academic writing from dry, technical prose into a compelling narrative that captivates your readers and increases your research impact. Fun fact: If nobody reads your research, it doesn’t matter how good it is. Enter [​*Dramatica*​](https://dramatica.com/resources/assets/dramatica-theory-book.pdf?ref=lennartnacke.com): a creative story theory typically used by novelists and screenwriters, that I have reimagined for the world of scientific research. ### Why Your Research Paper Needs a Narrative Arc Let’s face it: even groundbreaking research can fall flat if it’s presented in a dull, convoluted manner. The most influential papers don’t just present facts; they tell a story. Of facts. Of science. Of rigour. They guide readers through a journey of discovery, building tension, revealing insights, and delivering a satisfying conclusion. This is never about sacrificing scientific rigour. It’s about making your work more accessible, engaging, and ultimately, more impactful. ### So, what’s Dramatica? Dramatica theory ([​Structure Chart PDF​](https://dramatica.com/resources/assets/dramatica-structure-chart.pdf?ref=lennartnacke.com)) posits that every complete story contains four essential elements: Character, Theme, Plot, and Genre. Here’s how we can map these elements onto the structure of a scientific research paper: 1. **Character** → Research Question/Hypothesis 2. **Theme** → Research Gap 3. **Plot** → Methodology and Results 4. **Genre** → Field of Study *Let’s break this down further:* 🦸‍♀️ ****Character: Your Research Question/Hypothesis.** In Dramatica, the main character drives the story forward. In your research paper, this role is played by your research question or hypothesis. It’s the protagonist of your scientific narrative, the central focus that propels your investigation. 💥 ****Theme: Your Research Gap.** The theme in Dramatica represents the central conflict or tension in the story. In academic writing, this translates to the research gap you’re addressing. It’s the unresolved question in your field, the problem that needs solving, or the debate that needs settling (at least if you follow a positivist paradigm). 🪡 ****Plot: Your Methodology and Results.** In storytelling, the plot is the sequence of events that unfolds as the story progresses. In your research paper, this corresponds to your methodology and results. It’s the journey your research takes, from initial question to final conclusion. The simple tools that make it all work out. 🎭 ****Genre: Your Field of Study.** Just as the genre of a novel sets reader expectations, your field of study establishes the context for your research. It determines the conventions you’ll follow. The language you’ll use. The standards by which your work will be judged. ### How Can I Build a Scientific Narrative? Now that we've mapped Dramatica elements onto academic writing, let's explore how to weave them into a compelling research narrative: 1. **Introduction**: Set the Stage - Hook your reader with a provocative idea related to your research gap that makes it urgent to be addressed (Theme). - Introduce your research question or hypothesis (Character). - Briefly outline the current state of knowledge in your field to frame your contribution (Genre). 2. **Literature Review**: Build the Backstory - Develop the context of your research gap (Theme). - Show how previous work has attempted to address this gap, and where it falls short. 3. **Methodology**: The Quest Begins - Present your research design as a logical approach to answering your question (Plot). - Explain your methods in a logical and clear way that builds moderate anticipation for your results. 4. **Results**: The Journey Unfolds - Present your findings as a series of discoveries. Don't overclaim or exaggerate. Use clear, concise, compelling language, figures, and tables to bring your data to life. 5. **Discussion**: The Climax and Resolution - Interpret your results in the context of your research question (Character meets Plot). - Show how your findings address the research gap (Theme resolution). 6. **Conclusion**: The Denouement and Caveats - End with a bang. Don't let your paper fizzle out. Restate your argument, key findings, and their implications. - Suggest future directions for research, setting the stage for the next scientific story. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Balancing Narrative and Academic Rigour While adopting a narrative approach can make your papers more engaging, it's crucial to maintain academic integrity and rigour. Here are some tips for striking the right balance: 1. **Prioritize Accuracy.** Never sacrifice factual accuracy for narrative appeal. Your story should enhance, not replace, your scientific content. 2. **Maintain Objectivity.** While you're crafting a narrative, remember to present alternative viewpoints and limitations of your study. 3. **Use Appropriate Language.** While your writing can be more engaging, it should still adhere to the conventions of academic language in your field. But, please use active voice. 4. **Support with Evidence.** Every claim in your narrative should be backed by data or citations, just as in traditional academic writing. ### The Future of Academic Writing As academia evolves, so too must our approach to communicating research. The Dramatica framework offers one tool for making our work more accessible and impactful, without compromising on scientific integrity when done right. Framing our research papers as scientific stories lets us: 1. Make complex ideas more accessible to a broader audience 2. Increase reader engagement and retention of key concepts 3. Enhance the memorability and citability of our work 4. Bridge the gap between academic research and practical application The goal here is NOT to turn your research paper into a work of fiction. Rather, it's to use narrative to illuminate the authentic drama of scientific discovery. When you apply the structures of Dramatica to your academic writing, you're not simply reporting results. You're inviting your readers to join you on that journey. ### Action Steps To implement the Dramatica approach in your academic writing: 1. Create a story outline for your next paper using the Dramatica elements (Character, Theme, Plot, Genre) before you start writing. 2. Review the introduction of a paper you're currently working on. Rewrite it to introduce your 'Character' (research question) more compellingly and establish the 'Theme' (research gap) with greater tension. 3. Identify three key transitions in your paper where you can insert 'plot points' to drive your narrative forward. Revise these transitions to create more momentum. 4. Analyze your conclusion. Does it satisfyingly resolve your main 'conflict' (research question)? If not, revise it to provide a clearer resolution and suggest future directions. 5. Ask a colleague to review your paper, specifically looking at how well you've balanced narrative engagement with academic rigour. Iterate with their feedback. Balance it right. So, Dramatica allows you to not only communicate your findings more effectively but also ignite curiosity and inspire further exploration in your field. And isn't that the true essence of scientific progress? Let me know what you think. Bullsh\*t or bombshell? **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to develop a research question (Free canvas) URL: https://lennartnacke.com/how-to-develop-a-research-question-free-canvas-2/ Last updated: 2025-07-28T18:59:42.000Z Lovely. January is almost over. How has your start into 2024 been? Mine has been productive. I’ve had some more excellent chats in the [AI Academics X Space](https://x.com/acagamic/status/1751983131018592766?s=20&ref=lennartnacke.com) the last Mondays, exploring all the recent AI developments with Vugar. > [https://t.co/LAl1FQvon5](https://t.co/LAl1FQvon5?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [January 29, 2024](https://twitter.com/acagamic/status/1751983131018592766?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) We’ve settled on a date for our [AI Research Tools webinar](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com): 29th of February (10 AM ET). And a price ($39.90 USD). I’m currently working on the landing page and will send you the link as soon as it’s ready. I am also thinking of rebranding this newsletter as **Write Insight**, more about that soon. [Thanks to everyone, who has already responded to the survey that is helping me restructure this.](https://tally.so/r/mZaP4a?ref=lennartnacke.com) [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) I’ve also recently developed this method for finding your research questions (reply to this email if you’d love to get a training webinar from me on this): ![](https://cdn-images-1.medium.com/max/800/0*ibFReEntp4_keMtm.jpeg) Figure 1\. The Research Question Canvas (by Lennart Nacke) [The Research Question Canvas by Lennart NackeA high resolution PDF version of the canvas to print out.The Research Question Canvas by Lennart Nacke.pdf261 KBdownload-circle](https://lennartnacke.com/content/files/2024/08/The-Research-Question-Canvas-by-Lennart-Nacke.pdf "Download") ## **The Research Question Canvas** Every journey needs a map. In academia, that map often takes the form of a well-defined research question. But how do you move from a vague curiosity to a question that’s not only insightful but also workable and impactful? That’s what I’ve developed this Research Canvas exercise for. It’s goal is to help you through prompts to come up with one or more useful research questions. It’s a dynamic framework with ten key quadrants. Each of these guides you through a crucial step in crafting your research question. ### **1\. Problem Identification** Here, you plant the seed — define the specific problem or issue you’ll tackle. Ask yourself: “What burning issue am I addressing? Why is it important to understand?” This sets the stage for your entire investigation. You want to target a relevant and worthy area of concern. ### **2\. Literature Gap** Before venturing into uncharted territory, always check your map. This quadrant is about identifying unexplored areas or limitations in existing research. Ask: “What do we not yet understand about this problem? What have previous studies overlooked?” Your research should fill a critical gap and shouldn’t retread old ground. ### **3\. Research Feasibility** Time to assess your resources! This quadrant is about ensuring your research is practical and achievable. Ask: “Do I have access to the necessary data or sources? Is this study workable within my timeframe and constraints?” Don’t let logistical hurdles derail your investigation. Plan your strategy from the outset. ### **4\. Specificity and Scope** Broad ambitions are admirable, but research demands focus. This quadrant is about narrowing down your focus to a manageable scope. Ask: “What specific aspects of the problem will I address? How can I limit the scope to make it more in-depth and impactful?” A laser-sharp question often yields more profound insights than a scattershot approach. ### **5\. Complexity and Depth** Don’t settle for surface-level scratching! This quadrant encourages you to think harder. Ask: “What are the different layers or dimensions of this problem? How can I explore these in depth to uncover hidden nuances?” Embrace the complexity of the issues. Your research will be richer and more valuable if you can figure this out early. ### **6\. Relevance and Impact** Why should anyone care about your research? This quadrant is about evaluating its significance in the field and beyond. Ask: “Why is this research important? What potential impact could it have on theory, practice, or society?” Connect your inquiry to a broader purpose. It must resonate not only within academia but also in the real world. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **7\. Empirical Testability** Research thrives on evidence, not speculation. This quadrant is about securing you can answer your question through empirical methods. Ask: “Can this question be investigated through data collection and analysis? What methods might be appropriate?” Choose a question that leads to observable and measurable results. This will put your research on a foundation of solid evidence. ### **8\. Research Design Compatibility** Every question needs the right approach. This quadrant helps you match your question to a suitable research method. Ask: “What research design (qualitative, quantitative, mixed methods) fits this question best?” Choosing the right approach facilitates a smooth and effective investigation. It guarantees your question and method work in harmony. ### **9\. Innovation and Originality** Don’t echo existing voices. Find the sound of your own voice. This quadrant encourages you to bring new insights or challenge established notions. Ask: “How does my question challenge or add to current understanding? What new perspective am I offering?” Strive for originality. Your unique angle will make your research stand out. ### **10\. Hypothesis Formation** Every good question deserves a bold prediction. This quadrant is where you form a testable hypothesis based on your research question. Ask: “What do I expect to find? What hypothesis or statement can I draw from this question?” Craft a clear and specific hypothesis. This guides your investigation and sets the stage for exciting discoveries. The Research Canvas is not a rigid checklist; it’s a flexible tool for exploration. Use it as a springboard for brainstorming, refining, and crafting a research question. The question you find should ignite your passion and make a meaningful contribution to your field. That’s it for today. If you enjoyed reading this newsletter, please forward this email to a friend or a person who would benefit from these tips. I appreciate your support. I love hearing from you. 🫶 **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to build academic relationships (5 Secrets) URL: https://lennartnacke.com/how-to-build-academic-relationships-5-secrets/ Last updated: 2025-07-28T19:01:51.000Z I’ve missed you. I haven’t been messaging in a while because I’ve been deepening my knowledge of AI tools for academics. I’ve started [X spaces](https://x.com/i/spaces/1yNGaZjWdAgJj?ref=lennartnacke.com) on X/Twitter on [the very topic](https://x.com/i/spaces/1mnGepwobEoKX?ref=lennartnacke.com), and it’s been fun. > We’ll be doing another space with [@Vugar\_Ibrahimov](https://twitter.com/vugar%5Fibrahimov?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) on academic AI use cases. > > Put it in your calendar below.[https://t.co/vOh7NJ2Was](https://t.co/vOh7NJ2Was?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [November 5, 2023](https://twitter.com/acagamic/status/1721241679414362442?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) ## Sign up for Write Insight Turn your scientific findings into influential articles effortlessly. Join 5k+ writers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. We did them every Monday at 10 AM Eastern Time ([check out the latest one we did yesterday](https://twitter.com/i/spaces/1yoJMwmrkeRKQ?ref=lennartnacke.com)). Come and hang out with us if you have the time. Oh, I also created this cool ScholarAI tutorial video for my YouTube channel: Here’s a little something that I had on my mind with all my content creation recently. [**Am I delivering stuff you’re interested in?**](https://tally.so/r/mZaP4a?ref=lennartnacke.com) I know I’ve asked for feedback before, but I’d really love to find this out. So, if you’ve got a hot minute, [pop over to this survey and please tell me what you’d love to read in this newsletter](https://tally.so/r/mZaP4a?ref=lennartnacke.com): My newsletter wouldn’t be the same if I didn’t have a useful piece of writing for you. This week, I want to talk about building relationships: ## 5 Secret Relationship Skills for Academics In the cutthroat academic environment, strong relationships can be your Infinity Gauntlet. They can open doors to collaborations, grant funding, and exciting career opportunities. But building meaningful connections in such a demanding environment isn’t always easy. It’s critical to focus on building genuine connections rather than networking tactics. Unfortunately, plenty of academics find it challenging to prioritize relationship building. They may feel like they just don’t have the bandwidth with all the research, teaching, and administrative work they need to juggle. Some may also be hesitant to step outside their comfort zones or get to know their colleagues on a more personal level. But here’s the good news: You don’t need to be a social butterfly to build strong academic relationships. You can cultivate personal and professional connections like a skilled artisan crafting their finest work. Just focus on a few key skills, and you’ll unlock a treasure trove of opportunities. ![A mindmap of 5 relationship skills for academics.](https://lennartnacke.com/content/images/2024/10/CleanShot-2024-10-25-at-08.20.23@2x.png) 5 simple relationship building skills for academics. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 1\. Active Listening We’ve all zoned out during a colleague’s presentation, but true listening demands more. It’s about giving your full attention. Ask clarifying questions. Find the essence of their thoughts. This helps us understand things better. It helps people appreciate each other and builds trust. Start by being mindful of your attentiveness during casual conversations with coworkers, then intentionally apply these active listening techniques in more formal settings as well, such as during presentations or team meetings. ### 2\. Collaborative Curiosity Shifting your mindset from solo success to shared discovery unlocks a hidden power. Approach conversations with genuine curiosity about your colleagues’ work. Offer your expertise without dominating. Be open to the unexpected sparks of collaboration. Remember, two minds are often better than one. This is especially true when there is mutual respect and intellectual excitement. Seek out opportunities to participate in collaborative projects and interdisciplinary research, as this can foster deeper connections. Cultivating a spirit of collaborative curiosity not only opens the door to new ideas and discoveries, but it also helps build trust and rapport with your colleagues as you actively engage with their work and perspectives. Take an active part in collaborative projects and meetings. ### 3\. Confident Humility Owning your expertise is crucial, yet arrogance destroys relationships. Be confident in your knowledge and acknowledge your limitations. Embrace learning from others. Balancing politeness with confidence builds trust and approachability. Balancing confidence and humility can be tricky for academics. They may overcompensate for insecurities by coming across as arrogant. However, being aware of this tendency and actively working to temper confidence with a dose of humility can lead to more natural and productive discussions. Cultivating genuine connections in academia often requires stepping out of our comfort zones and embracing vulnerability. Through humility, curiosity, and active listening, we can foster an environment of mutual respect and intellectual excitement that ultimately leads to more fruitful collaborations and fulfilling careers. Practice confident humility. Own your strengths, admit your weaknesses, and always remain open to learning. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 4\. Celebrate Success, Share Struggles Academic life is a roller coaster of highs and lows. One minute, you’re soaring like a falcon; the next, you’re crashing and burning like a meteor. Sharing these triumphs and disappointments can be daunting, but it’s what makes us human. When we openly acknowledge and celebrate our colleagues’ accomplishments, even in disparate fields, like a proud parent cheering on their kid’s big win, while also being willing to open up about our own challenges and setbacks, like peeling back the curtain on our vulnerabilities, it creates opportunities for greater empathy, trust, and connection within the academic community. This type of vulnerability can be a powerful tool for building meaningful relationships, as it allows us to connect on a personal level and see each other as whole, multifaceted individuals, not just disembodied intellects. So let’s cheer on our colleagues’ achievements with genuine enthusiasm, like handing out gold stars, and don’t hesitate to share our own struggles, even if it feels like baring our souls. Vulnerability encourages support and strengthens those important bonds, reminding us that we’re all part of the academic journey, like a band of brothers and sisters. Embrace and share the highs and lows of your academic journey—it nurtures a supportive and authentic community, like a warm family gathering. ### 5\. Express Gratitude (It Goes a Long Way) A simple “thank you” for a helpful critique or a sincere compliment on a research paper matters. Expressing gratitude for an insightful discussion can have a profound impact. Acknowledging others’ contributions fosters mutual respect. It reinforces the positive connections essential for academic success. Academics tend to be focused on their own work and goals, but taking the time to show genuine appreciation for our colleagues’ efforts and achievements can go a long way in strengthening relationships. Maintain a regular gratitude practice. This simple act can transform the academic atmosphere. It can become one of mutual respect and collaboration. Academic life often feels like a constant solo race for individual achievement, but the real secret to success may lie in our ability to build meaningful bridges to our colleagues. Cultivating active listening is the gateway to understanding, like an open door to their thoughts. Approaching conversations with collaborative curiosity is a mindset shift from “me” to “we”, unlocking a hidden power like a key to shared discovery. Balancing confidence with a dose of humility, like a skilled tightrope walker, builds trust and approachability. Embracing vulnerability, like peeling back the curtain on our struggles, creates opportunities for empathy and connection, weaving us together like a warm family quilt. Expressing authentic appreciation, like handing out supportive kudos, reinforces the positive bonds essential for academic success, cultivating an atmosphere of mutual respect, like a secret garden in full bloom. Remember, relationship building is a muscle that gets stronger the more you flex it, like a well-toned athlete. Don’t wait for the perfect moment — dive right in. Start casual coffee chats and join vibrant research groups. Offer a helping hand and participate in departmental events like a sociable butterfly. Step outside your comfort zone and fully embrace these relationship-building skills. In doing so, you’ll engage in unexpected collaborations that enrich your academic world, foster a vibrant network of colleagues, and have true allies in your intellectual pursuits. Imagine a harmonious orchestra where each unique instrument contributes to a beautiful symphony. That’s all, folks. Hey, if you liked this issue, why not hit reply and let me know? Always makes my day. You’re the best. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Three skills for every academic literature review URL: https://lennartnacke.com/3-essential-skills-for-every-academic-literature-review/ Last updated: 2025-09-14T21:07:04.000Z ![audio-thumbnail](https://acagamic.mymagic.page/content/media/2024/08/Voiceover-Essential-Skills-Academic-Literature-Review_thumb.png) 3 Skills for Every Academic Literature Review (Article Voiceover) 0:00 /347.328 1× I know, we've been out of touch for a bit. It's been a busy fall term for me. Two new course preps will do that to you. But I am slowly getting my time for the newsletter back and I promise to bring you my writing tips more regularly. ## **1\. Mastering Backward Searching and Citation Chaining** When diving into a literature review, it's like exploring a forest; you need to know where to start and how to trace your steps back. Begin with the newest papers in your field. These are like fresh tracks in the forest. As you examine them, look at their references. This is your path backward. It's a journey through your topic's history, leading you to foundational papers. *Backward citation searches look at the references of a linked article to locate relevant sources. It helps you find relevant research in your field. To search backward citations, take these steps:* 1. Find a recent, topic-related article with a long list of references. Search Scopus, Web of Science, or Google Scholar (alternatively use AI tools like Litmaps, Scite, Elicit, or Semantic Scholar but know that their results are not as vetted and comprehensive) for papers by, for example, keywords, authors, titles. This is your seed paper. 2. Find the most relevant sources in the article's reference list for your topic. Read source titles, abstracts, or entire texts to assess relevance and quality. 3. Repeat for each appropriate source until you exhaust the backward citation chain or reach your desired number of sources. Backward citation searches may reveal a study topic's roots and evolution, as well as gaps and potential for subsequent investigation. However, it may have constraints like: - Depending on the initial article's publication date, it may not include the latest findings. - Author selection and citation may prejudice it, because they may not include all relevant or significant sources in their reference list. - Manually assessing each source is time-consuming. When searching for relevant sources, it's best to combine a backward citation search with a forward citation search. True citation chaining. For forward citation searches, the same databases as above generally include a link or number indicating how many times an article has been referenced by other publications. Find and read those, following the same process. Forward citation searches may identify the latest research and an article's effect and influence but still has some downsides: - Because citing articles may reference a particular article for many reasons, it may not represent their quality or importance. - Because of their many citations from many areas, some papers may be overwhelming and hard to parse. - Some databases may not include all sources that quote an article or may update citation data incorrectly or slowly. Thus, backward and forward citation searches should be balanced and are necessary to get multiple viewpoints on your research questions. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## **2\. How to Skim Academic Papers** Skimming is not just reading fast; it's reading smart. It's like driving; you don't just speed up but also focus on things like road signs and other indicators to maximize your efficiency. - When you skim an academic paper, focus on the title, then abstract first. The abstract generally summarizes the research topic, methodology, findings, and conclusions. Read the abstract to understand the paper's major points. This gives you the gist of the research—its aims, key findings, and overall significance. You may also see whether the publication fits your research interests. - Next, read introduction, and then conclusion. Authors discuss their research's motivation, background, goals, and implications in the introduction and conclusion. These sections explain the paper's background, importance, key arguments, and results. - Then, glance over the headings and subheadings. They're like signposts, guiding you to sections that might need a deeper read. This saves time and helps you quickly determine a paper's relevance to your topic. - If you're lucky and the paper is well-written, next read the first and last sentences of each paragraph in the main body. The first and last sentences of each paragraph usually contain the topic sentences and the transitions that link the paragraphs together ([following the PEEL technique](https://youtu.be/ErmYTPN23YI?si=PUtyhHQ4WCYMBn8j&ref=lennartnacke.com)). These sentences let you follow the logical flow and structure of the paper and you should find the key points and evidence that support the main arguments. - Finally, examine the paper's figures, tables, and graphs. Figures, tables, and graphs show the researchers' data and findings. These sections provide a brief idea of the research methodologies, results, and trends and patterns discovered by the authors. ## **3\. Using Systematic Reviews and Meta-Analyses** Meta-analyses give you a high-level view of existing research and are usually regarded at the top of the hierarchy of evidence: ![A pyramid showing the quality of evidence.](https://lennartnacke.com/content/images/2024/08/image.png) Figure 1\. Quality of evidence taxonomy (from [ResearchSquare](https://www.researchsquare.com/blog/what-is-the-hierarchy-of-evidence?continueFlag=3b41b853df05eb0d1faf5743baddbf7c&ref=lennartnacke.com)). Systematic reviews and meta-analyses allow you to get a summary of key findings, methodologies, and gaps in the literature. Meta-analyses also help you evaluate the quality and consistency of the evidence in the literature. They often estimate the effect size and confidence intervals of the relationship or intervention of interest. You can then explore the sources of heterogeneity and potential moderators or mediators. This makes them full of new insights and implications for your future research, which can be a massive time saver. The purpose of these is to guide and supplement the process of a comprehensive and critical literature review, not to replace it. Your mileage may vary depending on your field. I hope this newsletter issue was helpful to you. If you enjoyed reading it, please forward this email to a friend or a person who would benefit from such tips. If you want to support my work, please recommend it to others and follow me on social platforms. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to find a research topic URL: https://lennartnacke.com/how-to-find-a-research-topic/ Last updated: 2025-09-14T21:08:49.000Z It is estimated that over 5.14 million academic articles are published each year. When you write a research paper, the choice of a topic becomes a significant first step. Selecting a topic for your research paper is a piece of academic writing that sets the course for the entire writing process. The topic you choose will determine the direction of your research, the resources you will need to gather, and the overall success of your paper. It is imperative to choose a topic that is both interesting to you and relevant to your field of study. For example, if you are studying history but are also interested in politics, you could select a topic like the effects of the French Revolution on the development of democracy in Europe. This will allow you to explore both topics in depth while simultaneously providing an original perspective for the reader. You will also be able to draw connections between the two topics that may not have been previously explored. This guide will provide you with a step-by-step process for selecting a research paper topic that will ensure a successful and engaging project. ## **1\. Brainstorming** The first step in selecting a research paper topic is to brainstorm potential ideas. Take some time to think about the subjects or areas of study that interest you the most. Consider your previous coursework, any current events or trends in your field, and any personal experiences or observations that have sparked your curiosity. Jot down any ideas that come to mind, no matter how big or small they seem. This initial brainstorming session will help you generate a wide range of potential topics to explore further. Once you have your list of ideas, narrow it down to two or three that you find most appealing. Do some research on each topic to find out more information about it and decide which one you want to pursue further. Finally, create a plan of action that outlines your research, writing, and other tasks needed to complete your project. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 6,737 researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## **2\. How to Approach Broad vs. Specific Topics** Contrary to common belief, starting with a specific research question may reveal innovative pathways in your field of study. As you conduct research, you can gradually widen your focus, leading to a richer understanding of the research problem. You can gain new insight into the topic by diving deep into a specific research question and uncovering information that may have been overlooked. Through this approach, unique perspectives and methodologies can be explored. Furthermore, narrowing down your research topic will help you stay organized and prevent you from getting overwhelmed by the abundance of available information. Striking the right balance between specificity and breadth is critical, as excessively narrowing down the focus may impede both findings' impact and generalizability across broader contexts. ## **3\. Filling the Gaps for Research with Impact** In academic research, focusing on unanswered questions or overlooked areas from prior research can lead to an academic research paper that brings novel insights into your field of study. You can make a real impact in your field by filling in the gaps in existing research. Identifying these gaps allows you to build upon previous findings, which ultimately contributes to the academic community. Furthermore, addressing overlooked areas can lead to practical applications and real-world solutions, making your research more relevant and valuable. You can solve practical problems as well as advance the theoretical understanding of your field by addressing these overlooked areas. Identifying a neglected aspect of a phenomenon, for example, may reveal new strategies or approaches that can be applied in real-world situations. Furthermore, completing the gaps in existing research can contribute to clarifying or validating previous findings, thereby increasing the credibility and reliability of the body of knowledge in your field. Your efforts to identify and address these gaps will have a lasting effect on the academic community and society overall. ## **4\. Cross-Disciplinary Approaches Beyond Boundaries** If you plan to write a research paper, don't be afraid to explore related disciplines when you are considering a topic for your paper. In addition to following a research guide, consulting your university's writing centre or lab, including the writing lab, may provide you with unexpected perspectives on the topic. Cross-disciplinary approaches will not only enhance the credibility and reliability of your research but also create a more comprehensive and holistic understanding of your topic. In your own field, it is possible to ignore the unique insights that can be gained from exploring related disciplines. Interdisciplinary research can lead to breakthrough discoveries and innovative solutions that have profound effects on your research area. Don't be afraid to step outside your comfort zone and embrace cross-disciplinary collaboration. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) The development of novel perspectives and methodologies for one's research endeavours is enhanced through interdisciplinary collaboration. A study that considers a wide range of factors and variables is likely to be more comprehensive and inclusive. Furthermore, interdisciplinary research promotes the exchange of ideas and the integration of diverse methodologies, cultivating a climate of innovation and creativity. Research that is interdisciplinary in nature can also facilitate unforeseen connections and revelations that would not have been possible if restricted to a single field of study. ## **5\. Anticipate Future Trends To Elevate Your Research** Rather than following current trends, why not look ahead? Your research can be positioned at the forefront of your field by analyzing potential upcoming developments and matching your topic to them. The advantage you gain will be unique. You can stay on top of your competition by identifying and predicting future trends. It can also help you craft your research in a way that can be useful to the wider world. Your work may gain more recognition and impact as a result. It is possible for researchers to contribute to cutting-edge advancements by predicting future trends. It enables researchers to identify emerging topics and technologies before they become mainstream, allowing them to address pressing issues before they become widespread. Additionally, aligning research with future trends can lead to collaborations and funding opportunities from organizations that prioritize forward-looking and innovative approaches. The incorporation of future trends into research contributes to its relevance and potential social impact. Furthermore, considering future trends in research can also help researchers anticipate and adapt to changes in the societal and technological landscape. Researchers can shape their research plans to meet the changing needs and challenges of society if they stay ahead of the curve. A proactive approach not only improves the quality and effectiveness of research results but also establishes researchers as authority figures in their fields. A rapidly evolving world demands the capacity to predict future patterns to remain relevant and contribute significantly to the advancement of knowledge and societal enhancement. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## **6\. Consultation Beyond Academia from Non-Experts** Individuals typically seek guidance from professors and advisors. Nevertheless, it might be worthwhile to consider asking industry experts, acquaintances, or even one's social media followers for their insights. The viewpoints of individuals lacking expertise can sometimes offer novel perspectives and unanticipated inspiration. Technology and social networking have made it possible for non-experts to give valuable insight through online forums and discussions. It is possible to challenge conventional wisdom by seeking advice outside of academia, as it offers diverse perspectives and alternative solutions. Moreover, involving individuals from various fields promotes interdisciplinary collaboration, leading to innovative ideas that address complex societal challenges and push the boundaries of knowledge. Including non-experts in the consultation process can help foster a more democratic and inclusive approach to problem-solving, ultimately contributing to the advancement of knowledge. Furthermore, seeking guidance from sources outside academia can bridge the gap between theoretical knowledge and its practical application by connecting and reconciling them. Developing theoretical frameworks and understanding fundamental principles requires academic research. However, real world results are ultimately determined by how these concepts are implemented. Integrating people with practical experience and expertise ensures that our solutions are not merely theoretical but also viable and effective in practice. Scholars and professionals outside of academia can form partnerships that yield more comprehensive and influential solutions to today's pressing issues. ## **7\. Not Always Best to Follow Personal Passion** In spite of the importance of personal interest, it is crucial to recognize that excessive focus on passionate pursuits may lead to exploring well-trodden paths. It is necessary to strike a harmonious balance between individual interest and scholarly significance to produce captivating and impactful subject matter. For research to remain relevant and contribute to broader social issues, it must balance personal passion with scholarly significance. Scholars can create solutions that have a broader reach when they take into account today's pressing issues, collaborate with experts from different disciplines, and address multiple aspects of a problem at the same time. Research should also be relevant to the public to ensure it is useful and impactful. As such, research must be disseminated in an accessible way that can be understood by the general public. However, some scholars argue that too much focus on societal relevance can lead to a loss of intellectual rigour and depth in research. They believe that the best research is done when scholars are free to explore ideas without worrying about whether the findings will be immediately useful to society. Ultimately, it's up to the scholar to decide what topic to pursue in their research. It is worthwhile to consider the various factors discussed and find a balance between personal passions and scholarly significance. In addition, it is critical to consider societal relevance and intellectual rigour. Creating this balance will help you create compelling and impactful research that resonates with the public and creates meaningful solutions. Your topic is not just the introduction; it's the pathway to innovation, critical thinking, and successful academic writing. Embrace the inherent challenges and allow your selection to serve as a catalyst for a pioneering research paper that has the potential to be widely referenced within your academic discipline. [Liked this post? Leave a tip](https://lennartnacke.com/#/portal/support) **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to write an academic cover letter URL: https://lennartnacke.com/how-to-write-an-academic-cover-letter/ Last updated: 2024-11-26T02:49:00.000Z ![audio-thumbnail](https://acagamic.mymagic.page/content/media/2024/08/Audio-Academic-Cover-Letter-Guide_thumb.png) How to Write an Academic Cover Letter (Article Voiceover) 0:00 /484.632 1× A cover letter is your first impression on paper, and it sets you apart from other qualified candidates. Even though the subject may seem a bit overwhelming, I'm going to give you some tips on how to write an academic cover letter. I'll guide you through three critical sections: opening, middle, and end, so you can make a persuasive case for your suitability. ## 1\. Opening: Crafting a Memorable Introduction ### Why are you writing this? Let's begin with why you're writing this cover letter. The obvious answer may be "to apply for the job," but I urge you to think outside the box. The purpose of your letter isn't just to apply for a job. You're starting a conversation. For example, you might start your cover letter by introducing yourself and demonstrating your understanding of the company and their needs. This is more than simply stating that you're looking for a job. Think of your letter as an invitation to engage, sparking curiosity, and opening a dialogue about the value you can bring. 📝 ****Example:** Dear Professor **\[Name\]*, I am writing to apply for the Assistant Professor position in **\[Field\]* at **\[University Name\]*, as advertised on the **\[University's\]* website. As a recent Ph.D. graduate from **\[University\]* with a specialization in **\[X\]*, I am excited about the prospect of contributing to your department's innovative research and teaching programs. My doctoral work on **\[brief description of your research topic\]* aligns closely with your department's focus on **\[specific area of focus mentioned in the job posting\]*. I am particularly drawn to the opportunity to collaborate with **\[Name of researcher or research group\]* on **\[specific project or research area mentioned in the job posting or on the department's website\]*. ### What's the position here? Each academic position has its own responsibilities and expectations, even within the same institution. Put more thought into how the position fits with the academic landscape instead of just mentioning it. This shows you're ready to take on the responsibilities. 📝 ****Example:** The advertised role demands not only expertise in **\[Field\]* but also the ability to conduct interdisciplinary research, particularly at the intersection of technology and education. I am prepared to contribute to the department's cutting-edge work in areas such as **\[cutting-edge research area 1\]* and **\[cutting-edge research area 2\]*. Moreover, I am committed to fostering an inclusive classroom environment that embraces diverse perspectives and supports both in-person and online teaching modalities. My experience with **\[specific relevant project or research\]* has equipped me to take on the varied responsibilities of this position, including mentoring students, securing external funding, and collaborating across disciplines to address complex societal challenges through **\[Field name\]* research. ### Why are you a great fit? Your suitability for the job goes beyond your qualifications. Here's where you can set yourself apart. Discuss how your values, mission, and strategic goals align with the institution's. Making these connections will show you as a potential contributor to the institution's larger vision, beyond your specific role. 📝 ****Example:** My academic values and strategic goals align closely with **\[University Name\]*'s mission to foster innovation and inclusivity in higher education. Your institution's commitment to supporting **\[cool topic, e.g., data analytics for informed decision-making\]* resonates with my research focus on **\[your related research area\]*. I am particularly drawn to your holistic approach to student success, which aligns with my dedication to creating accessible and engaging learning experiences for diverse student populations. My experience in developing **\[specific relevant project, e.g., "an AI-driven adaptive learning platform that increased student engagement by 30%"\]* demonstrates my ability to contribute to your institution's goal of integrating cutting-edge technology with pedagogical best practices. Furthermore, I share your vision of nurturing a culture of innovation in academia. My track record of **\[specific achievement, e.g., "securing two NSF grants for interdisciplinary research projects"\]* illustrates my capacity to drive forward-thinking initiatives that can enhance **\[University Name\]*'s position as a leader in **\[Field name\]* research and education. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## 2\. Middle: Demonstrating Your Interest and Skillset ### Why are you interested? The most important thing is to express your interest in the job, but an interesting tactic is to weave in the story of your intellectual journey and your motivations. This adds a personal touch, but it also shows the passion behind your work. Your application will be more relatable and compelling if it has emotional resonance. 📝 ****Example:** My interest in this position at **\[University Name\]* comes from an intellectual journey that began during my doctoral research on **\[related research area\]*. While studying the impact of **\[doctoral research topic\]*, I discovered a passion for bridging the gap between **\[search area X\]* and **\[search area Y\]*. This led me to pursue interdisciplinary collaborations, leading to a joint project with the **\[department that you didn't belong to\]* that explored **\[cool interdisciplinary research project\]*. The prospect of forging these types of collaborations at **\[University Name\]*, particularly within your renowned **\[specific research centre or lab\]*, resonates deeply with my academic aspirations. Your institution’s commitment to pushing the boundaries of **\[field name\]* research, as evidenced by your recent **\[specific achievement or initiative\]*, aligns perfectly with my goal of developing innovative technologies that enhance the learning experience for diverse student populations. ### What are your specific job-related skills? Don't use the standard list of skills. Demonstrate your skills instead. Take examples from your academic career where you've made significant contributions, like research breakthroughs, successful collaborations, or innovative teaching. In academia, soft skills like empathy, resilience, and communication are becoming increasingly valued. 📝 ****Example:** Throughout my academic career, I have consistently applied and refined my skills in ways that have yielded tangible results. For instance, my ability to secure external funding was demonstrated when I successfully obtained a **\[dollar value\]* **\[grant type\]* grant for a **\[duration in years\]* study on **\[grant research topic\]*. This project not only demonstrated my research capabilities but also my skills in project management and cross-disciplinary collaboration, because I led a team of **\[number\]* researchers from **\[number\]* different departments. In terms of teaching, I developed and implemented **\[cool approach like a flipped classroom model\]* for an undergraduate **\[Field name\]* course, resulting in a **\[percentage\]* increase in student performance and a **\[percentage\]* rise in course satisfaction ratings. \[Achievement, e.g., "Moreover, my commitment to fostering an inclusive academic environment is reflected in my mentorship of underrepresented students in STEM, where I've guided five first-generation college students to successful graduate school admissions over the past two years."\] These experiences have honed my empathy, resilience, and communication skills, which I believe are crucial for nurturing the next generation of **\[Field name\]* professionals and researchers. ## 3\. End: Reaffirming Interest and Enthusiasm As you wrap up, it's essential to revisit your enthusiasm. Imagine your potential contributions—they're less known, but they're powerful. Based on your past successes and skills, describe how you can contribute to the institution. You're showing your commitment to growth and improvement by taking this forward-looking approach. Lastly, say you'd like to meet in person. This is a subtle but powerful way to invite further conversation. Your willingness to take the next step shows you're a proactive communicator, a valuable skill in academia. Creating an effective academic cover letter takes more than just summarizing your resume. You'll need to tell an engaging story about your intellectual journey, demonstrate your skills, and anticipate your future contributions. You can make your cover letter stand out by addressing these elements in a way that mirrors your career path. I hope these insights help you write a great academic cover letter. Good luck on your job hunt. 📝 ****Example:** Looking ahead, I am excited about the potential to contribute to **\[University Name\]*'s mission of advancing **\[Field name\]* research and education. Building on my track record of innovative research and effective teaching, I envision developing new courses that integrate emerging technologies like **\[cool new approaches, e.g., VR, gamification\]* into the **\[Field name\]* curriculum. This could potentially lead to the creation of a cutting-edge lab focused on **\[hot topic mention in job ad\]*, aligning with your institution's commitment to **\[topic from university strategic plan\]*. Furthermore, I aim to establish collaborative research initiatives with industry partners, using my existing network to create opportunities for students to engage in real-world **\[Field name\]* projects. I am particularly enthusiastic about the prospect of contributing to your department's ongoing work on **\[specific research area or project mentioned in job posting or on department website\]*. My expertise in **\[relevant skill or research area\]* could offer fresh perspectives and potentially open new avenues for investigation in this field. I am confident that my approach to research and teaching, which emphasizes interdisciplinary collaboration and practical application, would complement and enhance your department's existing strengths. I would welcome the opportunity to discuss these ideas further and explore how my skills and vision align with the goals of **\[University Name\]*'s **\[Field name\]* program. I am available for an interview at your convenience and would be delighted to provide any additional information you may need. Thank you for your consideration, and I look forward to the possibility of contributing to the continued success and growth of your esteemed institution. Thanks for reading this edition of the newsletter. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to reduce word count in your academic papers URL: https://lennartnacke.com/7-strategies-for-reducing-word-count-in-your-academic-papers/ Last updated: 2025-09-14T23:38:48.000Z Hey friends, do you have trouble reducing the word count in your manuscripts? In this article, we will discuss 7 strategies for reducing the word count in an academic manuscript. I hope this will help you if you’re struggling with this. Reducing the word count in an academic paper can be valuable for students or researchers. It can help you make your ideas more concise, improve your arguments’ clarity, and simplify your papers. Additionally, it can help you adhere to word count restrictions set by your chosen publication venue, avoiding potential penalties. Here are some key benefits of reducing a paper’s word count: - Faster and easier editing and proofreading - Increased emphasis on the main points - Improved argument clarity - More succinct writing - Enhanced readability You can communicate your points better to your readers. Additionally, you’ll be able to present your ideas more concisely and clearly, making your paper easier to understand. Unfortunately, many authors are unaware of strategies for reducing the word count of their papers and lack the time and resources to invest in learning this skill. We’ll fix that in today’s newsletter issue. Always think of your paper from the reader’s perspective: brief is better. ## Join 6,000+ researchers Evolve into a smarter researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 1\. Eliminate redundancies A typical academic writing mistake is using redundant phrases and words that do not add value to the argument. These include phrases such as “in order to” and “due to the fact that,” which can be replaced with shorter alternatives such as “to” and “because.” As another example, instead of saying “in the event that,” you could use the phrase “if.” The purpose of this strategy is to reduce the word count and make the argument more concise. You want to make it easy for readers to digest the content quickly and clearly. Do not let them wade through excessive words to get to the point. Try to be mindful of the words you choose when writing. Replace longer phrases with shorter words and phrases wherever possible. Your language will be more direct and impactful. This is like packing for a trip; you want to fit as much as you can into the smallest amount of space. Replace your bulky words with more concise ones. ## 2\. Use active voice Passive voice — while enhancing formality — often adds unnecessary words. For example, it is possible to change “The data was analyzed by the team” (passive) to “The team analyzed the data” (active), thereby also reducing the word count while maintaining the meaning. Using the active voice in academic writing shortens sentences. The active voice allows the writer to present the subject as the one who performs the action. This also makes your writing sound more confident and assertive. In contrast, the passive voice often requires more words to convey the same meaning. Using the active voice is like driving a manual-transmission car. You can take control of your writing, shift into higher gears when you want to drive faster, and consume less fuel in the process. Passive voice, on the other hand, is like having an automatic transmission: it’s easy to use but can be inefficient and slow you down. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## 3\. Prune unnecessary adjectives and adverbs Adjectives and adverbs are often overused in academic writing, leading to long and wordy sentences. Adjectives, especially those modifying relationships, can often be deleted without loss of meaning. Instead of stating, “there was a tiny relationship between variables A and B”, simplify it to “A showed ten times more negative effects than B.” The essence of the relationship remains unaltered, while the word count drops. How much larger was one variable than the other? Don’t hesitate to tell me. It adds no information to say it was much larger, very tiny, or enormously large. Removing unnecessary adjectives and adverbs can make your writing more succinct and improve your argument’s clarity. For example, instead of saying, “The extremely significant results of the study were observed,” you should just say, “The study results were significant.” Another example is replacing “very difficult” with more exact words like “challenging” or “arduous.” It’s like pruning a tree — by cutting away the dead branches and focusing on the living ones, the tree will grow stronger and become more vibrant. The same can be said for writing — removing superfluous details can make a piece of writing more substantial and more effective. ## 4\. Disentangle nested information Academic content often involves convoluted concepts. Academic writers commonly present these in layers, like a Matryoshka doll. However, this layering can produce redundancies. Let’s consider the statement: “Quantum theory, which revolutionized our understanding of particle physics and, subsequently, has led to significant advancements in technology, is a seminal concept in modern science.” This could be streamlined into: “Quantum theory, a revolutionary modern science concept, has significantly advanced technology.” This clarity and concision are especially helpful for readers who are not experts in the field. It helps them understand the concept without getting lost in the details. Think of this as using a map to find your destination instead of wandering aimlessly. You get a bigger picture view of where you’re going and how to get there without getting bogged down in the minutiae. ## 5\. Combine sentences Combining two or more sentences can make writing more concise and direct. This strategy allows writers to present related ideas in a single sentence. For example, instead of saying, “The study showed that there is a correlation between X and Y. This correlation can help explain Z,” you can say, “The study showed a correlation between X and Y that can help explain Z.” Or, as another example, instead of saying, “The survey was conducted in 2010\. The results of the survey were analyzed in 2011,” you can say, “We conducted the survey in 2010 and analyzed it in 2011.” (In this case, I also added active voice.) Whenever possible, look for ways to combine sentences. You should take care to ensure that the merged sentences still make sense and that the meaning of the original sentences is preserved. However, be careful not to overuse this technique because it can make your writing overly formal and awkward. Imagine this as combining ingredients in a recipe to create an original flavour; each ingredient adds its own unique qualities, and together they create something more delicious than any single ingredient could be on its own (Ratatouille, anyone?). ## 6\. Use abbreviations and symbols You can use abbreviations and symbols to reduce your word count. However, it is crucial to ensure that abbreviations are commonly accepted and understood in your discipline. For example, an academic paper might use the term “e.g.” to introduce examples of a particular concept, such as “e.g. the use of artificial intelligence (AI) in healthcare can reduce costs and improve patient outcomes.” Likewise, the term “i.e.” is used to clarify the meaning of specific phrases, such as “AI-driven healthcare is a rapidly growing field, i.e. the use of AI technologies to improve healthcare processes and outcomes.” These are known standards. Common symbols used in academic papers include the ampersand (&) and the asterisk (\*). Standard abbreviations include “et al.,” which is used to refer to multiple authors, and “cf.”, which means “compare with” or “consult.” A good rule of thumb, though, is to always define your abbreviations and acronyms on first use. So, for example, even if most quantitative researchers understand what an “ANOVA” is, you might want to write “Analysis of Variance (ANOVA)” on first use. ## 7\. All killer, no filler (words) Inexperienced authors tend to use filler words in academic writing to fill space and try to add emphasis to an argument. These include words such as “very,” “really,” and “just,” which can be removed without affecting the meaning of the sentence. Other examples of filler words include “actually,” “basically,” “essentially,” “literally,” “quite,” “totally,” and “absolutely.” Nothing can be gained by adding these words to a sentence, and the message is not affected. So remove them. Never write something like this: “Basically, this study essentially literally shows that the results are quite consistent and totally reliable.” Instead, opt for clarity and brevity that convey the same message: “This study shows consistent and reliable results.” Avoiding redundant words keeps your writing concise. It’s like cleaning a house; you don’t need to take out every single item, but you do need to go through and get rid of the clutter that adds no value to your life. Removing words that don’t add value is the same concept. Getting rid of extra words leaves your writing crisp and clear. Marie Kondo will thank you. Following the strategies outlined above, you can reduce the word count of your writing while maintaining clarity. It is crucial for you to write concisely, clearly, and impactfully. Your message will be more likely to be heard if you make a strong impression on your readers. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Seven Crucial Academic Writing Elements URL: https://lennartnacke.com/7-crucial-academic-writing-elements/ Last updated: 2025-06-15T13:03:21.000Z In this issue, I will break down the seven crucial elements of academic writing: Evidence, Analysis, Research Problem, Method, Structure, Argumentation, and Implications. Using these components as a guide, I hope to clarify academic writing for you in a straightforward, easy-to-follow way. An academic writer must be able to craft a compelling, well-structured, and persuasive piece of writing. Whether you're an emerging scholar or an experienced academic, mastering the elements of academic writing can propel your research from obscurity to prominence. An academic writing foundation can help you gain the respect of peers, obtain funding for your research, and achieve tenure. Besides giving you the ability to articulate your research problem succinctly, it also allows you to present your evidence and argument in a manner that resonates with your audience. Unfortunately, the core elements of academic writing, despite their importance, are often a source of difficulty for many scholars. In some cases, scholars have difficulty finding and using evidence to support their arguments. Creating coherent structures and presenting compelling arguments is a challenge for others. The divergence in writing styles across various disciplines, and the ability to construct strong arguments, further compound the difficulty. Additionally, understanding the broader implications of one's work and articulating them effectively is another challenge. As you read this, I hope to give you the tools that you will need to overcome challenging aspects of academic writing, ultimately enabling you to contribute to your field and advance your academic career. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## 1\. Evidence Academic writing relies on evidence. Facts, information, and logic are not enough. You must back up your arguments with evidence from reliable sources. This evidence can be from published books and articles or from your own research. When citing sources, it is important to be accurate and precise. Strong evidence supports your ideas across disciplines. It gives credibility to your work and empirical proof of abstract ideas. Evidence can strengthen your argument and convince the reader. It can also be used to refute opposing arguments and explore the complexities of a topic. For example, if you wanted to make an argument about the effectiveness of a particular policy, you could use evidence from statistics, interviews with policymakers, or case studies to back up your point. Your paper must prove your claims. ## 2\. Analysis No evidence is without analysis. We analyze evidence to determine its relevance and accuracy. This helps us to distinguish between facts and opinions, as well as draw out the implications of the evidence. Analysis also allows us to make connections between different pieces of evidence and form conclusions. Disciplinary beliefs, motives, commitments, and customs affect your analysis. We must be mindful of our own biases and view evidence from an objective and unbiased perspective. We need to be able to question our own assumptions and be open to alternative interpretations of the evidence. Evidence analysis aims to draw meaningful conclusions that can inform decisions. Your proof adds fresh perspectives to your field's conversation. For example, an analysis of an archaeological site might uncover evidence of a previously unknown culture, leading to a re-evaluation of the region's history. Interpret, analyze, and contextualize evidence within your field's framework. ## 3\. Research problem (often a question) Academic writing answers questions and addresses problems. So, you want to identify a research problem that is both interesting enough to explore and manageable enough to be solved within the confines of your project. The research problem should also provide original insight or knowledge about your subject. Our goal is to gain a deeper understanding of the world. Consider the implications of the research problem and determine its relevance to your field. Additionally, think about the practical applications and implications of your research when determining your problem. Comprehend both what is familiar and what is new. For example, you could ask: How can VR technology improve remote students' educational experience? This problem could be explored by conducting interviews with students and teachers in remote areas. In addition, it could be explored by analyzing the results of experiments using VR technology in the classroom. You could use your results to draw conclusions about VR technology's potential in educational contexts. Ensure your writing consistently addresses the central question or problem you aim to solve. ## 4\. Method A consistent method aligns with your discipline's standards. It should be used for all your experiments. It also allows replication and verification of results by other researchers. This warrants reliability and validity. In general, a method is a systematic or regular way of doing something. In science, a method is a series of steps followed to do an experiment or investigation. A consistent method ensures that all experiments are conducted with the same precision and accuracy. In this way, other researchers can replicate the experiments, ensuring reproducible results. It also gives the research greater validity, which builds trust in the results. Your method makes it easy for readers to follow your reasoning. Your work is more credible if you use rigorous methods. For example, if you are conducting a survey, you should use the same questions, response options, and (if connected to an interview) interviewer instructions for each participant. Before you start writing, choose a method and let it guide your writing process. ## 5\. Structure Structure is paramount in academic writing. Well-organized papers improve cross-disciplinary literature comprehension. Structure helps to create a logical flow of ideas and allows the reader to easily comprehend the material. An effective structure includes using headings and subheadings. Paragraphs should be kept short and concise, focusing on one main idea. Finally, transitions should be used to bridge ideas between one paragraph and the next. A well-structured paper is easier to edit and revise. Consistency clarifies your views despite structural differences between disciplines (IBC vs. IMRD). The IBC structure stands for Introduction, Body, and Conclusion. This structure is typically adopted for short papers or essays, and each section serves a specific purpose. The Introduction provides background on the topic, the Body presents findings and arguments, and the Conclusion summarizes the points made. The IMRD structure stands for Introduction, Method, Results, and Discussion. This structure is typically used in long papers or research projects and adds the Method and Results sections to the IBC structure. The Method section outlines the methodology used in the research; the Results section describes the data and findings; and the Discussion section analyzes the results and implications. For example, the Method section of a research paper may include information about the research design, population and sample, data collection and analysis, and other details about the research process. Take the time to learn your discipline's writing style and follow it. ## 6\. Argumentation Arguments consist of proof to support a central idea. Both, facts and opinions, can be used in an argument. Evidence must be logical and relevant to the topic to be convincing. It is imperative to consider different perspectives to make an argument that stands up to scrutiny. A thesis is a claim that will later have supporting evidence. It is a statement or central idea that a writer puts forth at the beginning of an argument, and will support throughout the following text. A hypothesis is a prediction that requires further research to prove. More specifically, a research hypothesis is a statement or prediction tested through studies. A hypothesis describes a relationship between two or more variables. The dependent variable in an experimental design is the one that the researcher measures, while the independent variable is the one that the researcher manipulates. The independent variable is the cause of a change in the dependent variable, which is the effect. For example, if a researcher is studying the effects of an experimental medication on a certain medical condition, the independent variable would be the medication. The dependent variable would be the medical condition. The researcher would then observe the effects of the medication on the medical condition and use this evidence to support their argument that the medication is effective in treating the condition. Your argument guides your research and its implications. ## 7\. Implications Academic writing explains your work's wider ramifications. Implications allow readers to understand the relevance of the research and how it can be used in real-world scenarios. It also helps to build connections between the work and other research or topics. Finally, it helps to deeply understand the research topic. For instance, a study of the effects of a new drug on the human body may also discuss the implications of its use within the medical field, or the economic implications for pharmaceutical companies. Extractable knowledge may spur initiatives, inquiries, research, or policy changes. Showing your work's repercussions boosts its relevance. Write about these consequences. Go beyond your research. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### The 7 Research Gaps (Plus 5) URL: https://lennartnacke.com/the-7-research-gaps/ Last updated: 2025-10-30T16:03:46.000Z 💡 A ****research gap** is a clear missing piece in what a field knows, who is studied, or how a problem is studied. You find gaps by scanning recent studies for contradictions, ignored aspects, stale data, or limits in methods, then write a question that directly tackles that opening. #### Key Points - Use a repeatable scan: map the topic, group findings, flag conflicts, test feasibility - Show how your study closes the gap and what changes for the field - Write gap sentences that name the pattern and why it matters - Pair each gap with a method and a sample or setting - Aim for gaps tied to real problems, not trivia 🧠 ****2025 Update:** Since publishing this article in 2023, I have added 5 more research gaps to it below. Also, check out [my article that explains why problems matter more than gaps](https://lennartnacke.com/why-research-gaps-are-bullsh1t/). The toughest part of publishing isn’t just collecting data or running fancy analyses. Yes, these activities have their challenges. But for many early-career researchers, the toughest part is picking research questions that matter. Too often, researchers recycle old topics, patch up small holes, or dodge real project ideas because they don’t see a clear opening or research opportunity. But the best research doesn’t chase what’s already solved. It goes after the gaps in what we know that shows us how to solve useful problem. A research gap is often simply understood as *a question or a problem that has not been answered* by existing studies, but this definition is incomplete. The critical, unasked follow-up question is always: “What happens if this question is *not* answered?” If the answer is **nothing**, the gap is not worth the time, effort, or funding. A high-value research gap is a *symptom* of a deeper, real-world problem. The research itself should never be the end goal but it should always the *solution* to that problem. This gap-to-impact framework mandates that any research must bridge the divide between a theoretical knowledge void and its practical application or impact-based forecasting. The final research must *do* something tangible: inform policy, improve professional practice, or create measurable value for a specific community. If you want your next study to stand out, get grants, and shape your field, you need to spot the gaps that others miss. That’s how you move the academic conversation forward and stop adding to the pile of forgettable papers. Academics must maintain the validity of their research, and creating false contributions can have dire consequences, such as losing trust from peers and having a publication retracted. > Struggling to find a unique angle for your research? > > The key to groundbreaking research is in its gaps > > 7 types of research gaps every scholar should know [pic.twitter.com/bdss2poq9E](https://t.co/bdss2poq9E?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [April 25, 2024](https://twitter.com/acagamic/status/1783511387551216028?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) Finding a gap doesn’t always mean choosing a completely different topic from what has already been researched, but rather identifying aspects within existing topics that have yet to be examined in-depth or from different angles. Before you move forward with your research project, it’s crucial to verify that a gap in the field is feasible. When designing your study, take into account resources and time constraints to avoid creating unrealistic results. Every field is full of contradictions, implicit assumptions, and ignored aspects. They all indicate problems that haven’t been solved yet. Finding gaps and their associated deeper problems is more than an academic exercise. This is where important discoveries and career success start. But most students and early-career researchers struggle to even find them. So, I want to lay out the core types of research gaps to look for, with practical examples for each one. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 1\. Evidence Gap #### ****How to spot** - Findings on the same outcome point in different directions - Few studies use strong designs or robust measures The evidence gap shows up when there’s not enough solid data or proof to back up current claims. Plenty of research ideas sound convincing, but they aren’t supported by numbers, experiments, or direct observation. Spotting this gap can push your work past speculation and into real, testable territory. - **Study results are conclusive but conflicting when viewed abstractly**. For example, VR studies may have demonstrated that virtual reality can benefit cognitive development. However, other studies have suggested that it can be detrimental to physical health. This evidence gap requires further exploration to achieve a more comprehensive understanding of VR technology's potential impacts. - **New research defies conventional wisdom**. For example, there has been a growing interest in AI-powered chatbots for healthcare applications such as symptom tracking and personalized health advice. This is a research gap that has yet to be fully explored in the HCI literature, as most existing studies focus on chatbot usability and user experience, rather than their potential applications in healthcare. - **Provocative exceptions arise.** For example, human-computer interaction studies have indicated that assisted technologies, such as voice or gesture-based interaction, can improve user experience and performance. However, there is a lack of research into how these technologies may harm users' privacy. This could be an area of research where more research is needed to understand the potential risks of using such technologies. Identifying these gaps requires the analysis of each study. Pair the pieces together to identify the conflicting findings. ### Example of how to write this - We identified an evidence gap in prior research concerning \[X\]. Previous research has addressed several aspects of \[X\]: 1, 2, 3 (w/ citations). However, it has not addressed contradictions in the findings concerning the prior research. We identified this gap: \[Describe\]. - Prior research has generally found that \[X\] is beneficial for \[Y\], but other studies have found contradictory evidence. Our study sought to bridge this gap by investigating the differences between the prior research findings. ## 2\. Knowledge Gap #### ****How to spot** - Basic questions remain unanswered in a niche - Work exists in a neighbour field but not here The knowledge gap is quite popular with junior researchers because it marks places where the literature simply hasn’t covered the basics yet (careful here though before claiming “no prior work exists”, because you might just not have done a deep enough literature search). However, when you run into missing information or questions that haven’t been answered, you’ve found a knowledge gap. Filling this gap helps expand what’s known and gives other researchers a better foundation for their work. Two knowledge void settings are possible: 1. **Desired research results don’t exist. Theories or literature from similar fields may not exist in the field**. For example, in the games literature, there is a knowledge gap in understanding how cognitive skills such as problem solving and critical thinking can be improved through playing video games. While there have been studies exploring the potential benefits of playing video games, there is still a dearth of research into the cognitive benefits of playing specific types of video games (as of 2023). 2. **Unexpected study results.** For example, one study may have found that playing an action game improved cognitive performance in older adults, while a previous study found that playing a game designed to improve executive functioning had no effect on cognitive abilities. There is a friction worth exploring here. Lean into these contradictions. ### Example of how to write this - We identified a knowledge gap in prior research concerning \[X\]. Furthermore, it did not address the subject of \[Y\]. This includes several new dimensions with research attention in other disciplines. \[Y\] should be explored to see why \[X\] has a different effect. - Research should consider how \[Y\] affects the outcomes of \[X\], such as the impact of cultural differences on the effectiveness of \[X\]. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) ## 3\. Practical Knowledge Gap #### ****How to spot** - Professionals say one thing, do another - Studies are lab bound, not field tested You’ll find a practical gap when research fails to offer solutions that work outside the research lab. If there’s plenty of theory but few answers for real-world problems, you’ve found a practical gap. Tackling this type of gap can move your findings from purely academic interest to everyday use. - **Professionals publicly promote one action but perform another.** For example, a doctor may publicly encourage patients to make healthy lifestyle choices, but privately prescribe medication as the only solution. - **Professional practices differ from research or are unstudied.** For example, a lawyer may tell a client that they should proceed with a certain strategy in a court case, but the outcome of such a strategy may not have been studied and could have a variety of unexpected outcomes. Research can uncover what’s causing a conflict and how far it reaches. When there’s a clash between what people know and what they actually do, that’s called an action-knowledge conflict. ### Example of how to write this - Prior research lacked practical expertise and rigour. Unexplored areas of \[X\] seem to be lacking in \[Y\] field practice. Theoretical studies dominate \[Y\]. Thus, \[Y\] has few practical studies. This matters in \[X\]. Because \[...\]. Theory studies focused on \[X\] & little on \[Y\]. - There have been few field studies of \[X\] in relation to \[Y\], making it difficult to assess the potential of \[Y\] to improve \[X\]. ## 4\. Methodological Gap #### ****How to spot** - Same survey scale used for years, no behavioural or trace data - No mixed methods where both would help Researchers may encounter methodological gaps if their sampling, measurement, and data analysis methods are different. Observation methods and self-reported survey responses might differ when studying social behaviour. Methodological problems can lead to inconsistencies and contradictory findings, which makes it hard for other researchers to validate the study’s conclusions. We are better able to understand many phenomena and make better policy decisions if we address these methodological gaps. - **Addresses issues with existing research methodologies.** For example, mixed methodologies can provide a more holistic look at the phenomenon being studied. They can also help to identify underlying factors that might not be seen with one specific methodology. - **Proposes an innovative research direction.** For example, an HCI research direction could explore the impact of AI-enabled technology on user experience, such as voice recognition effects on user engagement and user satisfaction. You’ll only find fresh insights for this gap if you use different research methods than those used before. Changing up the approach is necessary to answer questions that older techniques couldn’t address. before. ### Example of how to write this - We found a methodological gap in past studies. \[Y\] lacks \[X\] research designs. We identified little prior research on \[X\] designs based on our study design. This study investigates \[X\] research designs. We overcome methodology inadequacies with \[Z\] to expand research. - We employed a longitudinal field study design with qualitative interviews to explore the impact of \[X\] on \[Y\], which had only been studied in experimental settings. ## 5\. Empirical Gap #### ****How to spot** - Reviews and models exist, but no direct tests - Claims rest on logic, not data A major challenge for scholars is empirical validation. Literature and expert opinion can lead to theories and models, but they must be tested and proven. Empirical research is characterized by rigorous conception, implementation, and analysis. It is essential for reliable outcomes, but many fields lack it. Many reasons exist for this. Researchers from different domains must collaborate and invest in data gathering and processing infrastructure to close empirical gaps. It supports social problem-solving and understanding human behaviour. - **Conflicts were not assessed empirically in any prior research endeavour.** For example, the 2016 US presidential election provided an unprecedented opportunity to empirically assess the effects of political discourse polarization on voter behaviour. This was a conflict that had likely not been examined in any prior research endeavour. - **Research results must be confirmed.** To confirm research results, additional studies should be conducted using different methodologies and data sets to corroborate the original findings. This validates that the outcomes of the original study are not due to chance or misinterpretation of the data. This gap is about spotting problems that researchers haven’t examined yet. The attention is on previously unexplored issues, the ones that haven’t been the subject of any previous studies. ### Example of how to write this - Prior research had an empirical gap. In the context of \[Y\], there are some unexplored \[X\] that seem relevant. Because \[...\], empirical research is crucial. Qualitative research on \[X\] has thrived. No study has directly assessed \[X\] through empirical research. - No study has looked at the relationship between \[X\] and \[Y\] in a laboratory setting, which would provide a more direct measure of the effect of \[X\] on \[Y\]. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ## 6\. Theoretical Gap #### ****How to spot** - New facts do not fit the model - Two models explain the same thing You’ll come across a theoretical gap when current theories don’t explain what’s really going on. You’ll spot this gap when existing models fail to account for new findings, odd results, or exceptions in the data. Filling it means developing better explanations that actually fit the evidence. - **Related work lacks theory.** For example, few studies have sought to explain the observed relationships between diversity and resilience through an underlying theory of the mechanisms at play. - **Multiple theoretical models explain the same phenomenon causing a theoretical conflict.** Examine which theory can best address the research gap. An example of a theoretical conflict in psychology is the debate between behaviourists and cognitive psychologists regarding the primary cause of behaviour. Behaviourists argue that behaviour is primarily caused by external factors, while cognitive psychologists believe behaviour is determined by internal mental processes. Applying theory to new research problems lets you see patterns and explanations you might miss otherwise. If you try a different take on established models or build new ones, you can discover insights that simple observation alone wouldn’t reveal. ### Example of how to write this - Current investigations show that \[X\] theory is outdated. Some earlier theory seems essential. However, \[X\] and theoretical development need scrutiny. This is essential because \[...\]. To strengthen theories, existing theoretical models must incorporate research in \[Y\]. - \[X\] theory has traditionally failed to consider the role of \[ Y \] in the decision-making process, a factor that has been increasingly shown to be essential in the past decade. ## 7\. Population Gap #### ****How to spot** - Samples are narrow, often young and Western - Marginalized groups are missing A population gap emerges when certain groups or demographics haven’t been included in research. It’s common for studies to overlook marginalized or less visible communities, which leaves big questions unanswered for those populations. For example, people of colour are disproportionately underrepresented in clinical trials and medical research studies. This results in an inadequate understanding of their needs and health risks. Research on under-represented or under-researched populations can include among others: - Ethnicity - Gender - Race - Age Research often neglects these groups and misses out on the valuable insights their experiences offer. We get a clearer picture of their needs and perspectives if we managed to close this gap, which leads to smarter policy decisions and fairer outcomes for everyone. It’s a crucial gap to look for. ### Example of how to write this - Some sub-populations have been overlooked and under-researched. It is important to investigate the \[X\] in the context of the \[Y\]. It is crucial to investigate this group because \[...\] Previous research has mainly focused on \[Z\]. - Research into the \[X\] group has only recently gained traction, with studies such as \[study\], which identified \[findings\] related to \[Y\]. > Every researcher wants to solve a problem. > > But we all struggle to find good ones. > > Here are 9 questions to ask to identify research gaps in your field: > > 1\. What is the broader research area I'm interested in? > > 2\. What have existing literature reviews revealed about the current… > > — Prof Lennart Nacke, PhD (@acagamic) [February 20, 2024](https://twitter.com/acagamic/status/1759928562717438392?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) ## 8\. Contextual Gap #### ****How to spot** - One setting dominates the literature - Findings may not travel across settings Certain environments, settings, or circumstances have not been examined in previous studies, leaving missed perspectives in research conclusions. Findings in one type of context might not apply elsewhere. Exploring these ignored settings can unearth new insights and practical relevance. - Existing research often focuses on urban populations, neglecting rural or remote communities. For example, technology adoption studies usually examine city dwellers, which means rural experiences remain underexplored. - Workplace studies about productivity usually target office environments, but remote or hybrid setups require a different lens. Research that focuses too narrowly on certain settings can leave important environments unexplored. We discover fresh insights that make our findings more relevant and useful across different situations when we address this gap. ### Example of how to write this - Prior studies on \[X\] focused on \[Y\]. However, little is known about \[specific aspect of X\] in \[specific environment of Y\]. Our study addresses this contextual gap by investigating \[target population for Y\]. - Most research into \[common X aspect\] centers on \[common Y location\]. Our project examines \[common X aspect\] among essential \[Y population\] in \[Y location\] settings. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 12k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 9\. Te**mporal Gap** #### ****How to spot** - Major world or tech changes since data collection - Policy shifts that change incentives Much research relies on data or literature that has grown stale. Past findings may not index key changes in society, technology, or the marketplace. Research must revisit topics over time to reflect current realities. - Previous studies on smartphone use were made before the introduction of AI-driven features. Findings may no longer apply. - Public health research in the pre-pandemic era left gaps about pandemic-induced changes in mental health. Long-term effects must be studied. If studies rely on outdated data, they miss shifts in patterns and behaviours over time. Filling this gap helps us understand what’s happening now, so our conclusions stay accurate and up-to-date. ### Example of how to write this - Previous research examined \[X\] during \[Y\], but major changes since then mean those findings may be outdated. Our study fills this temporal gap by investigating \[X\] in current year/situation. - After event \[Y\], patterns related to \[X\] shifted, but earlier studies don’t reflect these changes. We address the temporal gap using new data collected after \[Y\]. ## 10\. **Technological Gap** #### ****How to spot** - New tools create new behaviours or measures - Old work did not include the tool New technologies, tools, or innovations create research opportunities missing from the current literature. These gaps can be urgent as tools reshape practices, behaviours, or outcomes. - AI-driven software has changed workflows for doctors, but no research examines its effect on diagnosis speed. - Recent advances in virtual reality offer learning possibilities that prior classroom studies never tested. Ignoring new tools and technological advancement means missing out on questions that older research methods can’t capture. Exploring this gap lets us find opportunities to apply the latest technologies and stay ahead in our field. ### Example of how to write this - Earlier research on \[X\] did not consider advances in \[Y\]. We identified this technological gap and studied how \[Y\] changes outcomes for \[X\]. - Previous studies addressed \[X\] using traditional \[Y\] methods. Our research investigates how new technologies like \[Y influence\] results for \[X\]. [![CTA Image](https://lennartnacke.com/content/images/2025/10/E-Mail-Course-Ad.webp)](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) Stuck staring at your ****methods section**? This **7-day email course* walks you through choosing your methodology, designing your study, and analyzing data without the academic jargon. Get a mind map that shows how everything connects. $49.99 gets you started immediately. [Start My 7-Day Course ](https://newsletter.nacke.ca/products/mini-research-course?step=checkout&ref=lennartnacke.com) ## 11\. Interdisciplinary Gap #### ****How to spot** - Parallel literatures not talking to each other - Methods in one field could solve a problem in another There are missing connections between established fields or disciplines. Solutions and insights found in one domain may never reach another with similar challenges. Bridging this gap is key for innovation. - Techniques for psychological resilience are rarely applied to games research, though game designers face similar stressors as athletes. - Methods for analyzing genetic data in biology could help solve problems in personalized learning within education. When research fails to connect ideas across disciplines, promising solutions get stuck in silos. We can combine strengths from different fields by bridging this gap and solve problems in ways no single area could address by itself. ### Example of how to write this - Research on \[X\] mostly exists within \[Y field\], while related areas rarely apply these insights. We addressed this interdisciplinary gap by bringing \[Y concepts\] into \[X research\]. - Techniques from \[Y field\] offer useful tools for studying \[X\], but few studies combine these approaches. Our work bridges the gap by using \[Y methods\] to advance research on \[X\]. ## 12\. Translation Gap #### ****How to spot** - Many findings, few playbooks or protocols - Practice does not reflect evidence Research often fails to move from theory to applied practice—or from academic publication into real-world use. Addressing this gap increases the practical impact of scholarship. - Many studies on exercise methods exist, but these do not inform actual training programs used by coaches. - Findings about sustainable materials haven’t led to adoption by manufacturing companies. Studies that never make it into practical use waste their potential impact. Closing this gap turns academic work into real solutions that change practice and improve lives. ### Example of how to write this - Many studies highlight the benefits of \[X\], but few translate this evidence into \[practical Y\]. We address the translation gap by turning research on \[X\] into \[Y protocols\] that professionals can use. - Research into \[X\] rarely reaches \[real-world Y\]. We close the translation gap by directly collaborating with \[Y practitioners\] to put the findings from \[X\] into practice. Spotting the right research gaps is more than just a box to tick for your lit review. If you follow my advice, you have already taken the first step toward making a real difference in your field. When you learn to tie each gap to urgent, unsolved problems, your work goes further than filling blanks (because there are infinite gaps in the research world). You start solving challenges that matter to people, you shape new conversations, and you set yourself up for academic impact. For early-career researchers and those tired of blending in, mastering this skill changes everything. Your projects get noticed, your writing stands out, and your results actually help move your discipline forward. That’s how you make your research matter. It’s the simple recipe for how you carve out a reputation for delivering work that drives change. If you want your research to stand out and solve real problems, learn to spot the gaps in what’s known, who’s studied, and how problems are tackled. Tie your questions to these openings to describe the actual problems, and your work will make an impact that lasts longer than your academic career. ## Frequently Asked Questions (FAQ) #### ****What is the fastest way to validate a gap**? Run a scoping search for the last three years, check top venues and reviews, and try to write one tight gap sentence. If you cannot support the sentence with at least five high quality sources, rethink or narrow it. #### ****How specific should a gap be?** Specific enough that your method, sample, and measures fit on one page of a protocol. If the plan spills over, the gap is too wide. #### Can one study address more than one gap****?** Yes, but lead with one primary gap and name the rest as secondary. Clarity helps reviewers. #### ****How do I avoid claiming no studies exist**? Write that prior work has not examined X under condition Y or in group Z. Then cite neighbour work and show why your angle is different. #### What if my results do not close the gap? Report what you found, update the map of the field, and show the next test. Honest limits build trust. ## Further reading Miles, D. A. (2017, August). A taxonomy of research gaps: Identifying and defining the seven research gaps. In Doctoral student workshop: finding research gaps-research methods and strategies, Dallas, Texas (pp. 1-15). P.S.: Ready to put these strategies into action? Join my [**Research Methods Email Course**](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com), where you’ll get practical mind maps, cheat sheets, step-by-step guidance, and expert support on how to create standout projects with the right research methods. [Sign up today to move from second-guessing your next study to building work that gets noticed.](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) ## Step by step workflow to find a real gap Paid subscribers get this workflow and helpful LLM prompts and checklists today. ### Step 1\. Define the outcome and compare set - Outcome you care about, for example motivation in online courses - Compare set of nearest topics, for example feedback style, timing, AI tutor presence, task type ### Step 2\. Pull the core evidence - Collect 20 to 40 recent studies, mix reviews and top primary papers - Log design, sample, measures, effect direction, and context in a table ### Step 3\. Group and pressure test - Cluster by method, population, context, and time - Flag conflicts, missing groups, old data, or method limits - Ask what would change policy or practice if we knew the answer ### Step 4\. Pick the tightest gap that is feasible - Check access to data, time, skills, and ethics - If the gap is too wide, narrow to one outcome, one group, one setting ### Step 5\. Draft the gap sentence and study aim - Name the pattern, the cost of not knowing, and your fix - Use the ready to copy starters in the tables below ## A Framework for Significance with “So What?” Every dissertation committee, grant reviewer, and journal editor will ask one simple, brutal question about your research: “**So what?”** If you cannot answer this question clearly and compellingly, your research proposal or paper will fail. Asking about the deeper root cause with “So What?” is the qualitative validation of a research gap. *A simple, three-step formula can be used to communicate this:* 1. **Name Your Topic.** State the broad territory you’re in. 1. *Example:* “I am studying parental involvement in K-12 education.” 2. **Step 2: Add an Indirect Question (The Gap).** State the specific unknown. 1. *Example:* “…because I want to understand *why* involvement declines in low-income urban school districts.” 3. **Step 3: Answer “So What?” (The Impact).** State the tangible, real-world benefit. 1. *Example:* “…because if we can identify the specific barriers, we can help schools design evidence-based interventions that close the student achievement gap.” This third step, the motivating statement, is how you link your gap to actual impact. It connects the academic gap (the *why*) to a tangible, real-world outcome (the *impact*). This is the way to identify a problem worth solving. ## LLM Prompt to Connect a Research Gap to Impact Copy My research gap is: [state your gap] Help me connect this to real-world impact by answering: 1. Who are the specific stakeholders affected by this knowledge gap? 2. What are the tangible costs (financial, social, health) of not addressing this? 3. What evidence exists that this problem is urgent? 4. What measurable outcomes would change if this gap were filled? Provide specific examples and suggest where to find supporting data. ### Uneven U: The hidden structure behind powerful academic paragraphs URL: https://lennartnacke.com/uneven-u-the-hidden-structure-behind-powerful-academic-paragraphs/ Last updated: 2024-08-13T01:53:08.000Z Before we start: Archived old newsletter posts are always available for paid members. Are you struggling to give your academic writing that professional polish? Do your paragraphs feel disjointed or lack impact? Enter Eric Hayot’s Uneven U—a solid approach to sentence structure that can transform your writing from amateur to authoritative. His Uneven U technique isn’t just for the humanities crowd—it’s your ace in the hole to hook readers and keep ’em hanging on every word. Picture this: you start with sweeping, big-picture ideas, then zoom in with surgical strike specifics, only to pull back and hit ’em with a broader perspective once more. It’s like a rollercoaster for the brain, and trust me, it’s a wild ride that guarantees your paragraphs will flow smoother than a buttered-up Zamboni. So, if you want to write stuff people actually want to read, jump on board the Uneven U train. ![Slides from Dallin Lewis on YouTube about the Uneven U paragraph structure](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe126ecc6-6b1a-467f-8d8a-21f46a819dee_1414x1003.png "Slides from Dallin Lewis on YouTube about the Uneven U paragraph structure") Slides from [Dallin Lewis’s video about the Uneven U](https://www.youtube.com/watch?v=JMk86KRLnxU&ref=lennartnacke.com). _This post is for subscribers only._ ### How to Save Time Writing (5 Tricks You Haven't Tried) URL: https://lennartnacke.com/transform-your-writing-habits-time-saving-hacks-youve-never-tried/ Last updated: 2025-07-28T18:16:00.000Z In this newsletter issue, I will explain to you how to make time for academic writing without compromising your free time. Academic writing is an essential skill for graduate students and researchers. It helps you effectively communicate your ideas and research findings to your audience. Setting aside time for academic writing helps you increase your scholarly output, improve your professional reputation, and advance your career. Maintaining a balance between academic writing and leisure time is vital for staying healthy and avoiding overworking yourself. Unfortunately, many academics struggle to make time for academic writing and sacrifice their leisure time. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## You might lack proper time management ![Mind map of 5 challenges to writing for extended periods.](https://lennartnacke.com/content/images/2024/08/Challenges_to_Extended_Writing_Periods.png) Challenges to writing for extended periods There are several reasons why you might find it challenging to devote extended periods to writing: 1. Juggling multiple responsibilities (work, family, and social commitments) 2. Procrastination (because you lack confidence in your writing abilities) 3. Lack of motivation or interest in the subject matter 4. Difficulty prioritizing tasks 5. Overwhelming workload Nevertheless, prioritizing academic writing can lead to success in both academic and professional settings. Rest assured! I will explain how you can overcome these challenges and make time for academic writing without sacrificing your free time. Here's how to become a more productive academic writer in 5 steps: ## **1\. Embrace “time-boxing” to reduce decision fatigue** Decision fatigue refers to the deteriorating quality of decisions you make after a lengthy decision-making session. It is a well-known phenomenon in psychology (linked to poor decision-making, impulse buying, procrastination, and even quitting). This fatigue can make it challenging to allocate time to academic writing. Time-boxing is a project management technique where a specific amount of time is allocated to complete a task or a set of tasks. This helps to keep a project on schedule and avoid overspending time on any one task. Use this to assign a specific duration for a task and focus only on that task during the allotted time. You reduce decision fatigue and the risk of procrastination if you pre-assign tasks to specific time slots in your calendar. For example, instead of writing when you feel inspired, schedule a 90-minute time-box for academic writing every Tuesday and Thursday morning. This method guarantees that you will keep your momentum and have plenty of mental energy left over for other leisure activities. ## **2\. Implement “writing sprints” for rapid progress** A writing sprint is a focused period of time, usually around 30 minutes to an hour, during which you set a goal to write as much as possible. These sprints can be done by yourself or in groups. The goal of this short, focused burst of writing is to write as much as possible without stopping or editing. Writing sprints can help you overcome writer's block, generate new ideas, and increase your writing speed. Consider incorporating writing sprints into your daily or weekly routine, either during your time-boxed sessions or separately from them. After each sprint, take a short break before diving into the next sprint or another task. You'll find that this method can lead to substantial progress in a short amount of time. ## **3\. Create a “writing retreat” within your daily routine** A writing retreat typically involves travelling to a secluded location to focus solely on writing. While this may not always be feasible, you can create a mini-retreat within your daily routine. Consider dedicating a specific day or half-day each week or month where you can immerse yourself in academic writing. During this mini-retreat, you can focus on your writing without dealing with other commitments that may distract you from your work. During your mini-retreat, you can also take advantage of other productivity techniques such as setting goals, breaking your writing into smaller tasks, or seeking feedback from others to help improve your writing. Follow these practices, and you will create an environment conducive to your writing and that helps boost your productivity. For instance, you could designate the first Saturday of every month as your writing retreat day. Plan your leisure activities around this day, knowing you have dedicated time to focus on your academic writing. ## 4\. **Cultivate a “non-writing” hobby to enhance creativity and productivity** Although it may seem counterintuitive, dedicating time to a non-writing hobby can have a positive impact on your productivity as an academic writer. Pursuing hobbies unrelated to writing can help you relax, reduce stress, and stimulate creative thinking. Choose a hobby that engages your mind and senses differently, such as painting, playing a musical instrument, cooking, or gardening. Regularly dedicating time to this activity can help you recharge and gain fresh perspectives, which can be applied to your academic writing. Encouraging creativity in different areas will make your writing process more efficient and enjoyable. ## 5\. Leverage “waiting time” for micro-writing sessions Throughout your day, you likely encounter pockets of “waiting time,” such as waiting for an appointment, commuting, or even waiting for your coffee to brew. Use these moments for micro-writing sessions. Jot down ideas, outline sections, or write a few sentences for your paper. This will help you stay productive and make progress even when you have limited time. Use a small notebook or a note-taking app on your phone to write down your thoughts and ideas during short breaks. You may be surprised at how much you can get done during these small moments that may seem unproductive. You can save a lot of time on academic writing by using the five techniques mentioned above. These strategies will not only help you balance your academic and personal time but also lead to a more fulfilling academic life. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Master TikZ Figures with ChatGPT for High-Impact Academic Writing in LaTeX URL: https://lennartnacke.com/master-tikz-figures-with-chatgpt-for-high-impact-academic-writing-in-latex/ Last updated: 2024-08-13T15:08:17.000Z I am at the CHI 2023 conference this week and receiving so much information (as usual) about everything related to human-computer interaction. I also enjoyed teaching my [CHI writing course](https://acagamic.notion.site/Master-Writing-for-CHI-with-LaTeX-Enrol-in-This-Course-Today-a43e36687f6d4de3be337952152c1bf5?ref=lennartnacke.com) to a small cohort at this conference again. If you're interested in going through the course slides, [you can find them here](https://file.notion.so/f/s/ca774f34-5984-4e9b-bca6-9d5efbbd2a2f/How%5Fto%5FWrite%5FCHI%5FPapers%5F2023-slides.pdf?id=f879b67c-29cc-4d80-a6d5-7afe407fa6e2&table=block&spaceId=f83dc996-5516-40ba-a3ac-835268ad973b&expirationTimestamp=1682502812009&signature=Z%5Fq7f-B0nVWR1%5FAvOHpT8leorw3xp40oBJg%5F4oGiDWc&downloadName=How+to+Write+CHI+Papers+2023-slides.pdf&ref=lennartnacke.com). One of the things, I taught students in the course is how to quickly create a TikZ figure with the help of ChatGPT when you are writing a paper in LaTeX (this is a markup language that is popular for scientific publications). I want to quickly break down this process for you today. Below you can see the figure we'll be creating. ![](https://cdn.magicpages.co/acagamic.mymagic.page/2024/01/TikZFigure.png) The TikZ diagram we'll create here with the help of ChatGPT In academic writing, we frequently create process diagrams, such as experimental flow diagrams, to explain complex processes. We can easily design these using the free TikZ package on the online LaTeX platform Overleaf. The TikZ package is a versatile tool for creating high-quality, customizable graphics, ideal for scientific, mathematical, and engineering diagrams. It supports a variety of features, from simple line drawings to complex 3D visualizations. Overleaf, an online LaTeX editor, enables users to collaborate on documents via a user-friendly interface, offering many templates and examples for seamless project creation and sharing. However, the process of creating a TikZ figure in Overleaf can be time-consuming, especially for someone not proficient in LaTeX, like myself. To efficiently create a figure in this code, I sought assistance from ChatGPT. Here's how I proceeded: 1. Think about the type of diagram and how to describe it in natural language. I then prompted ChatGPT-4 the following: *"Act as a LaTeX code expert. I want to create a horizontal flow diagram using TikZ to show the experimental setup flow with blocks. Generate TikZ code for a box flow diagram that shows this flow: RQ -> Literature Review -> Var 1 Test -> Exposure to Var 1 Test -> Post-test Questionnaires -> User Interviews -> Debrief."* You can see that I told ChatGPT explicitly what I wanted to do and gave a brief breakdown of the process flow without making it too complex yet. It's important to mention that it should be horizontal, so it does not just move around the blocks in circles. It continued to generate usable LaTeX code TikZ code, starting with the inclusion of the package for the document: `\usepackage{tikz}` `\usetikzlibrary{shapes,arrows,positioning}` 2. Then, I decided I wanted to add colour (specifically the CHI 2023 colours) and make everything look more uniform. The prompt continued as: "*Make all the boxes the same height to look more uniform. Then, I want to colour the background of the boxes in a complementary colour scheme inspired by these colours: #107AC1, #FBC63D, #C53E31\. Ensure good contrast ratios between text and background colours. Make the connecting arrows thicker. Break down the text inside the boxes into two lines. Make the font inside the text boxes larger. Be sure to use xcolor to create colour definitions first."* 3. It continued to produce an interesting diagram. I had to make sure `\usepackage{xcolor}` was included in the header. I then asked it to rename colours (*"Change the colour names from color1, etc. to names that represent the colours. Make the colour semitransparent at an alpha level of 70%"*). Now, all that was left to do was add the splitting of variables in the experimental design and add a second row of boxes for this. I did this, but I explicitly said what I needed the diagram to look like. 4. Here is the final prompt: *"I want to add a box under Var 1 Test called Var 2 Test and split the input arrow from Literature Review to go towards that as well. Connect this to a box called Exposure to Var 2 test right under Var 1 test, connect this box back to the same Post-test questionnaires box."* This continued to produce the image you see above. This little experiment showed me how quickly I can create awesome-looking TikZ figures with half of the code tinkering that I usually do to get them to look right. Hope this little tip is helpful to you. I will continue to enjoy the CHI conference, and if [you're on Twitter](http://twitter.com/acagamic?ref=lennartnacke.com), feel free to follow my journey there. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Understanding the Reasons for Paper Rejection URL: https://lennartnacke.com/understanding-the-reasons-for-paper-rejection/ Last updated: 2024-08-13T15:08:59.000Z When publishing academic research, understanding why papers may get rejected is crucial. In this newsletter issue, I want to introduce some of the most common pitfalls that authors face and offer tips on how to avoid them. ### 1\. Lack of originality Paper rejection often stems from research that lacks originality or novelty. If your study doesn't contribute new or meaningful insights to your field, chances are it won't be accepted for publication. To steer clear of this issue, take the time to perform an in-depth literature review before diving into your research. Make sure to express clearly what sets your study apart from others. Ensure your research offers new insights by thoroughly reviewing existing literature and clearly articulating the unique contribution of your study. ### 2\. Poor Writing Quality Rejection can also result from poor writing quality. Groundbreaking findings may lose their impact if the paper is hard to understand because of weak writing. To enhance your writing, work with an editor or proofreader who can polish your language and ensure clarity, making your work shine. Work with a professional editor or proofreader to refine your language and ensure your paper is clear and impactful. ### 3\. Methodological Flaws Rejection might result from methodological issues in research design or analysis, such as small sample sizes, biased participant selection, or faulty statistical analyses. To sidestep these errors, collaborate with a statistician when needed and adhere to best practices in research design. Collaborate with a statistician and/or rigorously follow research design best practices to ensure robust methodology and accurate analysis. ### 4\. Inadequate Research Design Flawed research design can cause your academic paper to be rejected. It may spawn confounding variables, skewed data collection, or incomplete analysis. To prevent this, craft a well-conceived research design tailored to your research question. Take your time to get it right. Solicit input from peers, mentors, and experts to fine-tune your approach. Set out for your research design to be meticulously planned and reviewed by experts to align with your research question, minimizing errors and confounding variables. ### 5\. Inconsistent or Inaccurate Data Inaccurate or inconsistent data may yield faulty conclusions, jeopardizing the acceptance of your academic paper. To avert such problems, ensure that your data collection methods are both rigorous and dependable. Always double-check your data, verifying accuracy and consistency, prior to diving into analysis. This attention to detail can make all the difference in the success of your paper. Thoroughly validate and cross-check all data for accuracy and consistency before beginning any analysis. ### 6\. Failure to Meet Manuscript Requirements Journals impose specific requirements for manuscript submissions, such as word count, formatting, and referencing style. Ignoring these requirements may lead to your academic paper's rejection. Before submitting your work, study the conference's or journal's guidelines attentively and comply with them. By meeting the journal's standards, you'll increase the likelihood of your paper's acceptance for publication. Closely review and meticulously follow the journal's submission guidelines to avoid rejection for failing to meet manuscript requirements. ### 7\. Inadequate analysis A paper with solid research design and data might still face rejection if its analysis is weak or flawed. This can occur when statistical methods don't align with the research question or when results are misinterpreted. Reviewers and editors seek rigorous analysis supported by robust data. Ensuring this alignment maximizes your paper's chances of acceptance. Review that your statistical methods directly align with your research question and that your analysis accurately interprets the results to avoid misalignment and enhance your paper's acceptance. ### 8\. Poor organization A well-written paper can still face rejection if its organization is subpar. Poor organization may manifest as unclear headings or a haphazard argument structure. Reviewers and editors value papers with clear, easy-to-follow, logical, and coherent arguments. Ensuring your paper exhibits these qualities not only enhances its readability but also bolsters its chances of acceptance. Remember, a well-organized paper is a reflection of clear thinking and effective communication, both of which are critical for academic success. Create a detailed outline before writing to ensure your paper follows a logical, coherent structure with clear headings and well-organized arguments. ![](https://lennartnacke.com/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ### 9\. Lack of contribution Journals or top conferences seek papers that provide fresh insights and contribute to the field in a meaningful way. A paper lacking in novelty or broader context might not be accepted. This issue may arise if the research question is too narrow or if the paper doesn't effectively situate its findings within the wider body of literature. Reviewers and editors expect papers to significantly advance knowledge, as the core value of any academic work lies in its ability to enrich the field. To increase your paper's chances of acceptance, make sure it answers a relevant research question and connects to the larger academic discussion. Always remember that a well-researched and thoughtfully contextualized paper is more likely to leave a lasting impact on your field and the academic community as a whole. Reviewers often look for the following when assessing a paper's contribution: #### 1\. Clarity A well-crafted academic paper should clearly and concisely articulate its unique contribution to the field. This not only helps to showcase the significance of the research, but also guides the reader in understanding the paper's purpose and intended impact. To ensure your paper's contribution is explicitly stated, begin by introducing it early in the paper, preferably within the abstract or introduction. This allows readers to grasp the essence of your research from the outset. Then, throughout the paper, reiterate and expand on your contribution, providing evidence and context that support its relevance and importance. #### 2\. Significance Journals and top conferences only accept papers that make a significant contribution to their topic. Your research's significance, reader interest, and paper's uniqueness are all enhanced by a meaningful contribution. Start with a thorough literature review to ensure your paper's impact. This can help you uncover knowledge gaps and create a relevant research question. Choose a topic that hasn't been well studied to improve your paper's impact. To demonstrate your contribution, include adequate evidence and context throughout your paper. This involves a well-structured argument and data analysis. #### 3\. Relevance Communicate clearly the significance of your research and why it is important. Insufficient contribution or inability to demonstrate relevance is a common reason for paper rejection. To avoid this pitfall, authors must effectively communicate how their work contributes to the field and why readers should be interested in their findings. To achieve this, authors must clearly state their problem and describe how their research offers novel insights or solutions. It is essential to show that your work extends beyond what has already been established in the literature and fills a void in knowledge or practice. In addition, emphasizing the potential applications or implications of your research can help to persuade the audience of its significance. #### 4\. Novelty Make your study unique. Your study should add to existing knowledge rather than merely repeating it. If your article is not clear and unique, it may be rejected if you depend significantly on current material. Before starting your research, carefully review relevant studies to avoid this issue and improve your chances of acceptance. Find knowledge gaps and examine ways to fill them with new approaches. Make sure your study's aims, hypotheses, methodology, and findings advance your understanding of the topic. #### 5\. Trustworthiness A piece of writing must make a significant addition to its topic for a journal or top conference to accept it. A major contribution shows your research's importance, engages readers, and sets your article apart from others in the field. Conduct a thorough literature study to ensure your paper makes a significant addition. Make sure your paper has enough facts and context to prove your contribution's importance. This entails presenting a cohesive and well-structured argument and analyzing your evidence thoroughly. By doing so, you will convince reviewers and editors that your paper is important and profound. A "page one reject" occurs when reviewers decide to reject the paper based on the first page alone. To avoid this, ensure your paper clearly communicates its contribution, significance, relevance, novelty, and trustworthiness right from the beginning. Confirm your paper addresses a significant research question and clearly situates its findings within the broader academic context, demonstrating how it advances the field. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **Debunking Rejection Myths** Many misconceptions exist about why papers get rejected. Here are some common myths debunked: 1. **Lack of an empirical study.** Contrary to popular belief, not having an empirical study isn't a reason for rejection. What's more important is framing the paper effectively and providing valuable insights. 2. **Not having a significant difference.** While a significant difference can be helpful, not having one isn't a deal-breaker. The key is to present your findings in a meaningful way. 3. **Poorly written papers.** Although clarity is essential, minor spelling errors won't lead to rejection. However, if the writing is so poor that it's unclear what the contribution is, the paper might be rejected. 4. **Not having a cool explainer video.** The presence or absence of an explainer video does not impact the paper's acceptance. 5. **Institutional prestige.** The reputation of your institution does not determine the acceptance of your paper. Quality work is what matters most. ### **It's not always your fault** You should also keep in mind that rejection is not always the author's fault; sometimes there are simply too many high-quality submissions competing for a limited amount of space. You are better equipped to navigate the academic publishing landscape now that you are familiar with the most common reasons for paper rejection and have dispelled some myths. Consider any additional reasons for rejection or misperceptions you've encountered throughout the process based on your own experiences. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to Make Each Section of an Academic Paper Engaging URL: https://lennartnacke.com/3-writing-tips-for-each-section-of-your-paper/ Last updated: 2025-09-15T00:09:16.000Z In today’s newsletter issue, I will share some writing tips for each section of your paper that I have learned throughout my academic career. These tips will help you improve the quality of your writing and increase the chances of your paper being accepted for publication. The journey of writing an academic paper can be both challenging and rewarding. Let's make your writing engaging, informative, and entertaining. I hope you enjoy this longer read (about 15 minutes reading time for this issue). In today’s newsletter issue, I will share some writing tips for each section of your paper that I have learned throughout my academic career. These tips will help you improve the quality of your writing and increase the chances of your paper being accepted for publication. The journey of writing an academic paper can be both challenging and rewarding. Let's make your writing engaging, informative, and entertaining. I hope you enjoy this longer read (about 15 minutes reading time for this issue). ## Tips for Your Introduction You want to capture the reader's attention immediately in your introduction section. Think of the introduction as an opportunity to make an excellent first impression and set the tone for the rest of your paper. You may want to provide some background information about your topic to provide context and help orient readers before delving into more specific details. Aim to craft an engaging opening to draw your audience in and encourage them to continue reading. One of my favourite ways to start an introduction is with a surprising fact or statistic to grab the reader's attention. It’s critical to ensure that this factoid is relevant to your research question and reliable (i.e., from a valid source). This type of peculiar hook gives academic readers an incentive to keep reading, because they may be curious about how you will use this piece of information throughout the paper. Or they might disagree with it and want to see you prove your argument for it. It helps to develop credibility for your argument right away by showing that there is evidence behind what you’re saying. You could use several statistics throughout the introduction to further explain why readers should care about your topic. This is not the only way to start an introduction. You could also ask a thought-provoking rhetorical question. An easy way to do this is to find an interesting fact that forms the foundation of your work (e.g., "approximately 8 million metric tons of plastic waste end up in the ocean each year"). For your rhetorical question, you want to highlight the severity of the issue, the need for change, or the implications of the fact (e.g., "plastic pollution calls for urgent action"). You want readers to ponder the implications of the question being asked. Next, you want to rephrase your facts as a question for dramatic effect and to provoke thought and reflection (e.g., "How can our oceans survive with 8 million metric tons of plastic waste added each year?—this question almost tells a full story with a clear protagonist—the ocean—an antagonist—the plastic waste—and a dramatic struggle—"survive"). This can be a powerful tool for engaging your reader from the very beginning because it encourages them to reflect on their own beliefs and assumptions as they read further into your paper. Think about how you can challenge your audience’s preconceived notions about what you are writing about. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) Yet another method for opening your introduction is by beginning with a quote or anecdote that will grab your reader's attention and make them curious about what will come next. An anecdote is a short story or event from real life. It could be something you did or something you heard from someone else. A quote can be from a well-known person or from another paper (or even from your own study if you do qualitative research) that has something to do with the topic of your paper. It should say in a few words what the main point of your writing is. If you use a story or a quote, make sure it fits with the main idea of your paper and isn't too common or overused. Aside from these three methods, here are some sentence templates for you that you can sneak into your introduction: - {People or Nation} have always presumed that {commonly held belief}. - Today, rejecting {known assumption} has become widespread. It is vital to make a great first impression when writing the introduction to your paper. Start with a hook or interesting statement to grab readers' attention and provide background information about the topic, explaining why it is important to discuss or research further. This will ensure readers are properly informed before diving into the main body. ## Tips for Your Related Work Show how your research improves existing material in the Relevant Work section. Before you talk about your study, you should look at relevant research and point out the ways it is similar to studies that have already been done. Point out unanswered questions or gaps in the existing body of knowledge and provide evidence to support additional research. Explain how your paper is unique and why it is important. Drawing parallels between your research and things that might not seem to be related can get people interested. This will also show them how far-reaching the effects of your work are and how they could be used in other situations outside of their original field. For example, if you did research on a new way to teach math, you could use related work from psychology to explain why this way is better than others. When you make these connections, you will help people see why your research matters. You can use metaphors to describe the gaps in your paper when you talk about them. For example, you can compare gaps in the literature to a jigsaw puzzle with missing pieces. With this comparison, readers will be able to see and understand why they need to do more research. When talking about new trends or theories that have been found in related work, metaphors can also be helpful. You can make hard ideas easier to understand and more accessible by making clever comparisons. Another great technique for related work is to highlight opposing views. This will show the range of research that has been done in the field and the different ways that people have thought about it. This makes it easier for readers to look at your findings without bias and come to their own conclusions about what you are saying. Additionally, citing arguments from both sides of a debate can add depth to your discussion and give readers a well-rounded view on you topic. Here are some sentence templates to help with your related work: - In their latest work, X and Y have provided disapproving criticisms of {opposing view} for {previous reasons}. - X and Y, in their article {article title}, claim that {research finding}. Their research, which demonstrates that {research outcome}, contradicts the notion that {common belief}. They use {new method} to show {impact on society}. X and Y’s argument points to {point you want to make} regarding the more pressing issue of {your topic}. ## Tips to Write Your Methods The methodology section of your paper can make your work accessible to readers. It should be interesting and make it clear to the reader how you did your research and came to your conclusions or findings. Make sure to give enough information so that people can judge the quality and validity of your process. Taking care of any possible sources of bias or mistakes in the methods can help make sure that skeptical readers are more likely to believe that the results are accurate. Use flowcharts when appropriate. This simplifies complex methods for readers. Analogies are a good way to explain complex methods to help your audience understand the concept more easily. An analogy is when you compare two different things that are not related to draw out similarities and make something easier to comprehend. For example, if you were explaining how user experience (UX) design works, you could use the analogy of hosting a dinner party: each guest represents a user, and the host's goal is to ensure everyone enjoys the experience. This analogy allows your readers, who may not have prior knowledge of UX design, to relate it back to something they already know (i.e., dinner parties) and better grasp its purpose and functionality. You also want to provide an explanation for why you chose certain methods over alternatives. This shows that you carefully thought about your approach and weighed all of your options before choosing the best one for your research project. Briefly mentioning the advantages of your chosen method will help convince readers that it is indeed effective and appropriate for the task at hand. This helps build trust between paper authors and readers by showing that authors don't just follow popular trends or make decisions without thinking about them or having a reason for doing so. If you have done so, showcase any unique data collection techniques you used. A new method or something more common in a different field, will make your research stand out and demonstrate the resourcefulness of your approach. You want to emphasize the usefulness of your data collection methods if they aren't often used in studies like yours, so that your readers can decide if they want to adopt your approach. Think about anything from innovative survey question design to new ways of organizing interviews. With many different ways available for gathering information, you should take advantage of unique opportunities when they arise. Here are some sentence templates to help you write your methods: - To ensure the reliability and validity of our study, we used {standard validation method}. We conducted this study in {location}. Before commencing the study, we retained approval from the ethics committee {number of ethics approval}. - We performed {analysis method} using the {either common software package or explain purpose}. We conducted a {specific analysis type} to examine the relationship between {conditions of your experiment}. Create a compelling narrative around your method by explaining step-by-step how you did each experiment or study. Make sure your methods section shows repeatable steps about how you got your data. This will keep people interested in what your research has shown. ## Tips to Write Up Results In your results section, you want to communicate findings without discussing implications yet. Here are some tips for the results section. You can emphasize any unexpected or counterintuitive findings. This captures the reader's attention and peaks their interest in the discussion. Unexpected or counterintuitive findings can also be used later in the discussion to explain a phenomenon in more detail, help us understand a problem better, or give us a better understanding of some ideas. Another way to show the impact of your results is to provide relatable examples. Instead of just saying statements like "p < 0.01," state that this is the commonly accepted significance threshold in your field and this shows a accepted effect. This allows readers to better understand the implications of your findings and how they can be applied. As another example, if you’re studying the effects of a certain type of exercise on weight loss, provide an example of healthy weight loss that can be expected after taking up that form of exercise. This shows how your experiment can be put into practice rather than just theoretically. Providing real-world examples brings more relevance to the study itself. In your results, focus on highlighting the most significant outcomes. Readers can then quickly take away the most important points and remember them in the discussion. You want to provide enough detail about these outcomes so that your readers can understand why they are significant and how they relate to other aspects of your research. In the discussion section, you will talk about what the results of your research mean and how they can be used. This will provide further context for understanding why certain outcomes are significant and what impact they have on future studies. Here are some sentence templates for your results section: - There were no significant differences between group {X} and group {Y} in terms of {aspect your are researching}. - Further evidence of {X} was found that {supports your research claims}. ## Tips for Your Discussion In the discussion section, you finally provide a broader perspective on your research. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) Here, you want to think beyond the boundaries of your own discipline and consider how the work you've done could be relevant to other disciplines. Drawing these connections can help show readers that your research has a broad impact—beyond just what is immediately apparent. For example, if you're researching language acquisition in early childhood education, you might explore how this knowledge ties into other fields such as psychology or linguistics. Showing these connections will provide context for what your study means and give the discussion section of your paper more depth. You also want to link your findings and research to real-world applications. You want your audience to understand how the results of your research can be applied in practical contexts. You can give concrete examples if possible so that your readers can better understand what your work means. If applicable, you should also consider any potential ethical implications that may arise from these applications, and discuss them thoughtfully in this section as well. Finally, you might want to consider alternative interpretations of your results. This opens the door to more research about what these findings mean and how they affect things. It can help encourage research in the future or add more understanding to a current topic. Offering alternative interpretations helps readers better understand and engage with your work because they are able to think about different perspectives on what you have presented. Remembering to include various interpretations will make your paper much stronger overall. Here some discussion sentence templates: - The strength of our work lies in {most important aspect you want to highlight}. - Taken together, these findings demonstrate that {desired research outcome}. - Regarding the relationship between {variable 1} and {variable 2}, previous research had suggested that {common assumption}; however, our research shows that {new insight}. ## Tips for Limitations and Future Work At the end of your manuscript, you want to grab your readers' attention by looking to the future and pointing out how more research and discoveries could be made. The best way to do this is to suggest specific and actionable future research related to the work you have done. Your readers can then think more deeply about the possibilities of what could be achieved by extending your current findings. Not only will it inspire them to explore new avenues, but it can also guide them in their own research endeavours. It allows them to build on top of what you have already accomplished. Suggesting future research topics could also get researchers from different fields or who use different methods to work together who wouldn't normally think about doing so. You could also discuss the implications of your research for policy or practice. However, you would want to provide concrete examples and evidence. Explain how the results of your study could affect public policies or organizational practices. When possible, draw on existing literature that supports your argument, because this will make it more convincing to readers. Think about how you can suggest specific changes that should be made based on your findings so that they have real-world effects. This lets you show that you have a deep understanding of how your research can be used in the real world and what it could mean for society. Ideally, you want to frame your limitations as opportunities for growth and learning. Acknowledging weaknesses in your work shows that you have a critical eye, which will make readers more likely to take your research seriously. When you talk about limitations as opportunities, you give the impression that you want to learn from your mistakes and grow from them. This makes your audience (and reviewers) perceive you as well-rounded and skilled. Here some sentence templates to round off your limitations and future work: - In spite of limitations because of {X}, we believe our work {has these strong implications}. - This study provides the backbone/a springboard for {these actionable future research items}. In conclusion, crafting a well-written academic paper requires a thoughtful and meticulous approach to each section. You improve the quality and influence of your research by engaging readers with captivating introductions, demonstrating the relevance of your work through related literature, clearly outlining your methods, effectively presenting results, and discussing the broader implications of your findings. Also, pointing out the problems and making suggestions for the future shows your critical thinking skills and encourages more research in your field. In the end, using these writing tips will improve how your paper is received by your peers and add to the ongoing academic conversation in your field. As you embark on your writing journey, remember to be patient, persistent, and open to learning; each paper is an opportunity to grow as a scholar and a writer. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to Make Academic Writing more Coherent (7 Methods) URL: https://lennartnacke.com/how-to-make-your-writing-coherent/ Last updated: 2024-09-10T15:57:43.000Z ## Make Your Writing Coherent In today’s newsletter, we'll be discussing how to make your writing more coherent. As you know, effective communication is crucial to success in academia. Writing with clarity and coherence is crucial to convey ideas and research effectively to readers. One of the most important skills for writers is to make their writing coherent. Cohesion helps readers understand complex ideas and arguments, and facilitates the construction of rigorous and nuanced arguments. Therefore, producing work that is both persuasive and impactful is vital to succeed in academic writing. However, many writers struggle with coherence in their writing. Their writing suffers from unclear connections between ideas, poor organization, and a lack of cohesion within paragraphs. These issues can cause confusion and frustration for readers, making it difficult for the writer to get their point across and potentially decreasing the effectiveness of their work. However, there are techniques and practices that can be used to overcome these issues. This newsletter issue provides some strategies and methods that help you create writing that is clear and interesting. A well-written paper ties together different ideas and concepts in a way that makes sense. Cohesion and coherence are achieved by linking ideas well within and between paragraphs, reiterating the main point, and, if necessary, giving detailed explanations. Transitions are the most obvious way to show how different ideas are related to each other, but there are other ways and examples that writers can use to do the same thing. So, before I dive into transitions below with a great list of examples that you can use right away, let’s discuss some other ways to make your writing more coherent. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## Parallelism Parallel sentence structure is a technique that can make academic writing easier for readers to follow. You essentially repeat similar phrase structures, which may help generate a feeling of rhythm and balance in your paper. By adopting parallel sentence patterns, you are able to emphasize significant ideas and facilitate the comprehension of difficult issues. Consider using parallel sentence structures as you write your paper to create a sense of progression in your ideas. For example, you can use parallel sentence structures to describe different aspects of a complex concept, such as "The first aspect of X is..., the second aspect of X is..., and the third aspect of X is...". This breaks down the concept into manageable parts and makes it easier for readers to understand. Another way to use this technique is to emphasize key points or arguments. For instance, you might use parallel sentence structures to describe different examples that support your argument, such as "Example A demonstrates..., Example B illustrates..., and Example C confirms...". This can help reinforce your argument and make it more persuasive for the reader. Parallel sentence patterns support readers in digesting complicated ideas and following the progression of ideas. They also create a feeling of rhythm and balance in your writing, emphasizing key themes in within your paper structure. 📝 ****Example:** Generative AI models learn patterns from data, produce novel content based on those patterns, and refine their outputs through iterative processes. For natural language processing tasks, generative AI can draft coherent articles, generate human-like responses in chatbots, and assist in translation tasks across multiple languages. ## Repetition Repetition of essential words, ideas, or phrases is an effective method that can strengthen the structure of an academic paper and make it simpler for the reader to follow the argument's major themes. By consistently using these core phrases throughout the document, authors can establish coherence and unity that reinforce the overall message. For instance, when you are discussing a complex concept like "postmodernism," you may use this term repeatedly throughout the text to help readers understand the main idea. You may also use specific terms and phrases like "postmodern philosophy" or "postmodern literature" to emphasize this concept and make the paper easier to comprehend. This strategy may also be used by repeating significant ideas or arguments throughout the document. You may, for instance, offer an important idea in the opening and then repeat it in the conclusion to underline its significance and make a lasting impact on the reader. This may also help to generate a feeling of cohesion and unity throughout the various sections of the article. 📝 ****Example:** **AI systems* process vast amounts of data, **AI systems* learn from complex patterns, and **AI systems* make predictions based on this learning. **Machine learning*, a subset of **AI*, enables computers to improve through experience; **machine learning* allows for automated decision-making; **machine learning* drives advancements in various fields. ## Synonyms Synonyms are a way to improve the flow of academic writing by varying word choice. Synonyms are words that have the same or a similar meaning. They can prevent repetition in a document while maintaining emphasis. This method can also add variety to a document, making it more interesting for the reader. You have to be careful though with core concepts in your paper, those should be used consistently throughout for clarity. Repeat your key words for clarity (but do not do this excessively). So, for anything directly relevant to your experiment or theory, I do not advise switching out synonyms because this will just confuse your reviewers. However, when it comes to words that clarify important ideas, feel free to use different terms to add diversity to your paper. 📝 ****Example:** **Artificial Intelligence systems* analyze vast datasets, while these **computational models* interpret complex patterns, and such **intelligent algorithms* generate insights from the processed information. **Machine learning*, a key component of **AI*, enables computers to improve their performance; this **adaptive technology* enhances decision-making capabilities, and these **self-optimizing systems* drive innovation across various domains. ## Lexical Chains Lexical chains are sequences of related words that illustrate the consistency and relevance of vocabulary choices to the paper's topic. Using lexical chains creates variety in writing and prevents monotony. For your paper, consider using synonyms, antonyms, hyponyms, superordinates, and other related words to create lexical chains that maintain coherence within a paragraph or section. For instance, when discussing user interface design, you could use a lexical chain such as "usability - user experience - user satisfaction - user engagement - user loyalty" to maintain coherence and emphasize the significance of these related concepts. Similarly, when discussing artificial intelligence and machine learning, you might use a lexical chain like "data processing - algorithms - learning models - neural networks - deep learning" to illustrate the various stages of the machine learning process and ensure coherence in your discussion. Lexical chains create coherent flow in your academic paper, making it easier for readers to follow your argument and understand the relevance of your vocabulary choices to the topic at hand. 📝 ****Example:** AI systems process raw data, **apply sophisticated algorithms* to extract patterns, **use machine learning models* to interpret these patterns, and **employ neural networks* to make predictions, ultimately leading to actionable insights. In natural language processing, AI **starts with* text input, **performs* tokenization to break down the language, **applies syntactic analysis* to understand structure, **conducts semantic interpretation* to grasp meaning, and **generates appropriate responses* based on this comprehensive linguistic understanding. ## Topic Sentences Topic sentences establish order in your writing. You begin each paragraph with a clear and succinct topic sentence. This helps your readers comprehend the paragraph's key point and its relationship to your main research question or hypothesis. Consider using topic sentences to establish a focused framework that takes the reader through your argument as you compose your paper. For instance, if you are addressing the design principles of user interfaces, you may begin a paragraph with a subject phrase such as "The concept of simplicity is an important factor in user interface design." Afterwards, you may add supporting data and specifics to explain how this idea pertains to the design of user interfaces. In the same way, if you are addressing the ethical implications of artificial intelligence, you may begin a paragraph with a subject phrase such as "The potential misapplication of AI creates a huge ethical concern." You might then provide supporting information and evidence to show the possible hazards and difficulties involved with the use of artificial intelligence. The topic sentence sets the tone for your paragraph. It ties each paragraph back to your overarching question. Start each sentence or paragraph with information that hints at the content of the next sentence. Using this structure in your writing may not only make the entire paper more coherent but also help you be more convincing in your argumentation. 📝 ****Example:** **The rapid advancement of natural language processing (NLP) in AI systems has significantly transformed human-computer interaction.* Recent studies show that NLP-powered chatbots can now engage in conversations that are increasingly indistinguishable from human dialogue. According to a 2023 report, 87.2% of users have given their chatbot experiences a positive rating. The global NLP market is expected to reach $16.9 billion by 2023, showing significant growth in the industry. This improvement in NLP capabilities stems from the development of more sophisticated language models and the integration of contextual understanding. AI systems can now interpret nuances, recognize context, and generate responses that are not only grammatically correct but also contextually appropriate. This leap in performance has enabled AI to handle complex queries, understand implicit meanings, and even detect emotional undertones in text-based communication. As NLP continues to evolve, we can expect to see even more profound changes in how humans interact with technology, potentially redefining the boundaries between human and machine communication in various domains such as education, healthcare, and customer service. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ## Cohesive Nouns Cohesive nouns, also known as "umbrella nouns," summarize multiple related words or concepts into one term. For example, the cohesive noun "family" encompasses a group of individuals who are related by blood or marriage and who live together in a household unit. By using cohesive nouns, you can reduce wordiness and improve the clarity and flow of your writing by condensing multiple related ideas into a single, broad term. Using cohesive nouns can help you avoid repetition and create a more sophisticated tone in your writing. For example, when discussing the various components of a user interface, you can use the cohesive noun "interface elements" to refer to buttons, menus, and other related components. This not only avoids repeating the same words multiple times but also creates a more concise and polished sentence. Similarly, when discussing the impact of technology on society, you can use the cohesive noun "digital technologies" to refer to computers, smartphones, and other related technologies. With cohesive nouns, you can create a more efficient and streamlined paper that avoids unnecessary repetition and keeps the focus on your central argument. Additionally, cohesive nouns can also help to clarify complex topics and make them more accessible to a wider audience by simplifying the language and reducing confusion. 📝 ****Example:** **AI technologies* have revolutionized various sectors of the economy. In healthcare, **diagnostic tools* analyze medical images, predict disease outcomes, and assist in treatment planning. Financial institutions employ **predictive models* for risk assessment, fraud detection, and portfolio management. Meanwhile, the transportation industry utilizes **autonomous systems* for route optimization, traffic management, and vehicle safety. These AI applications demonstrate the transformative potential of **intelligent systems* across diverse domains. ## Transitions Transitions are crucial in academic writing, as they play a vital role in connecting different parts of a paper and making the writer's argument clear and easy to follow. In addition to using simple words (e.g., "however", "therefore", "in addition", "on the other hand") to indicate relationships between sentences or paragraphs, full transition sentences and templates can be extremely helpful in crafting an effective academic paper. These templates provide writers with a clear structure and framework for their writing, helping them to maintain coherence and clarity throughout the paper. To this end, I have compiled a comprehensive list of transitions that I use in my paper writing. Each of these transitions is the start of an example sentence, providing me with a solid starting point to create structure and better coherence in my writing. By using these transitions, I can help my readers follow the flow of my argument, understand how my ideas are connected, and ultimately create a more convincing and compelling academic paper. 📝 ****Examples for teasing fresh content:** → This section discusses... → What follows is a description of... → Throughout the following pages, I will discuss... → Below is a concise summary of... → In the following section, it will be claimed that... → The next section discusses the issue with X. → The next section provides a more in-depth description of X. → The following section will describe X's structure and functionality. → The subsequent section of this study describes in further detail the... 📝 ****Examples for introducing novel topics:** → With respects to X, … → With relation to X,... → Regarding X, … → In the instance of X... → With relation to X,... → With regard to X,... → On the subject of X,... → With regard to X,... → Concerning X,… 📝 ****Examples for revisiting a topic:** → As said before,... → As indicated previously,... → As previously mentioned,... → As stated earlier... → As stated on the preceding page,... → As discussed in the preceding chapter,... → Returning briefly to the topic of X,... → As stated in the introduction, it is obvious that... → As was mentioned in the paper's introduction,... 📝 ****Examples for transitioning between paper sections:** → Now, let's discuss... → Let us now consider... → Now, let us examine... → Now we will examine... → Regarding the experimental data on... → Before starting to investigate X, it is necessary to... → Before describing these ideas, it is essential to... → Having stated the meaning of X, I shall now proceed to explain... → This study has so far focused on X. The following section will address... → This chapter has established that... → It is now required to describe the progression of... → After discussing how to create X, the last portion of this work discusses... → This section examined the causes of X and claimed that... → The next section of this paper will... 📝 ****Examples for contrasting argumentation between sections:** → Another essential/vital/crucial component of X is... → Also, it is essential to inquire...In contrast to Smith, Jones (2014) has suggested... → Jones (2014) argues, in contrast to Smith,... → Nevertheless, little progress has been achieved in... → Yet, this approach has a number of significant limitations. → Yet, despite these new studies about the function of..., → Similarly, the research of Y indicates that... → In comparison to X, Y's findings show... → However, it is important to note that there are some limitations to Y's approach… → On the other hand, there is evidence that contradicts this argument… → While X has been found to be effective in certain contexts, there are limitations to its application in other contexts… → Despite the criticisms of X, it remains a widely used and important tool in the field of Y… → While X is often viewed as the standard approach, recent research has challenged this assumption… 📝 ****Examples for recapping sections:** → Another essential/vital/crucial component of X is... → Also, it is essential to inquire...In contrast to Smith, Jones (2014) has suggested... → Jones (2014) argues, in contrast to Smith,... → Nevertheless, little progress has been achieved in... → Yet, this approach has a number of significant limitations. → Yet, despite these new studies about the function of..., → Similarly, the research of Y indicates that... → In comparison to X, Y's findings show... → However, it is important to note that there are some limitations to Y's approach… → On the other hand, there is evidence that contradicts this argument… → While X has been found to be effective in certain contexts, there are limitations to its application in other contexts… → Despite the criticisms of X, it remains a widely used and important tool in the field of Y… → While X is often viewed as the standard approach, recent research has challenged this assumption… Transitions are key to clear and coherent academic writing. Using both simple and longer phrases helps connect sentences and paragraphs, keeping your work consistent. Templates can speed up the writing process by providing a framework and structure, ensuring clarity and uniformity. This list of transitions will help you start crafting a well-structured and engaging academic paper. ### Wrap-Up To wrap up today’s issue, ensuring coherence in your writing is essential to effectively conveying your ideas and research. You can achieve cohesion and coherence in various ways, such as using *parallelism*, *repetition*, *synonyms*, *lexical chains*, *topic sentences*, and *cohesive nouns*. Additionally, *transitions* are crucial for connecting different parts of your paper and making your argument clear and easy to follow. Using templates and transition words helps connect sentences and paragraphs, keeping your writing clear and consistent. Think about your readers and use these tools wisely. [Liked this post? Leave a tip](https://lennartnacke.com/#/portal/support) **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How To Write CaRS Introductions URL: https://lennartnacke.com/how-to-write-cars-introductions/ Last updated: 2024-08-13T17:49:52.000Z ## The CaRS Framework We'll talk about the John Swales-created CaRS framework in today's newsletter. In issue 3, I already touched on this on the surface but never went in-depth, so here is the deep dive finally. I will outline the essential subsections of the introduction section, which are typically found in research papers. By the end of this newsletter, you will have a clear understanding of the CaRS framework and the necessary components to include in your research paper's introduction. I also have a cool ChatGPT prompt to generate titles based on your abstract for you. Poor introductions damage research papers during peer review. These failing introductions are excessively broad, vague, and lack context and background. They may overlook the research difficulty or literature gap and focus too much on the author's work rather than the field. They may also use too much technical language or jargon and make unsupported statements. They can also be too long, repetitious, and unclear about the paper's topic or organization. ![Flowchart of the CARS model from John Swales](https://lennartnacke.com/content/images/2024/08/CARS-model-Lennart-2.png) The three moves of the Create a Research Space model. When writing a research paper, the introduction serves as a critical component that sets the tone for the entire paper. It's an opportunity to capture the reader's attention, convey the importance of the research, and provide an overview of the paper's purpose and key findings. The CaRS Model, also known as Create a Research Space, is a framework that helps researchers structure their paper's introduction. The model comprises three primary moves: 1. Establishing a territory 2. Establishing a niche 3. Occupying the niche Let's learn more about each of these moves. The CARS model helps you focus on these three aspects of an introduction section: - Your **research question** is the foundation of your study. You must invest time creating an engaging and relevant research question. A precise research question can help you find the information you need. It will also help you establish your study scope and data needs. So, your study question should be focused, specific, and practical. - Communication requires **context**. Context aids comprehension. This can assist people in understanding and following your message. Before diving into details, provide background. This can help your audience grasp your message's importance. Use examples or analogies to demonstrate your thesis. Context should also take audience demands and knowledge into account. Speaking to a group of specialists in an area may require less context than speaking to a general audience. Providing context helps your audience understand your message. - Use persuasive language and strategies to **persuade your audience**. Credibility is gained through exhibiting your experience, qualifications, and subject matter understanding. Your message should also address your audience's wants and ideals (the research field you are aiming to publish in). Emotional arguments also persuade readers. You can motivate readers by appealing to their emotions. Vivid descriptions, anecdotes, and emotive language can do this. Finally, substantiate your claims with evidence. Statistics, expert opinions, and real-world examples can all be referenced through related work or self-collected data. You may strengthen your point by providing hard evidence. Persuasive writing should persuade readers to act or agree with you, not deceive them. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **Step 1: Establishing a Territory** The first step is to define the area by giving background information about the larger field where the research is being done. Most of the time, this step has the following subsections: 1. **Background**: I call this part the 'Lay of the land' - it always comes first. This section covers the research field's current situation. Give a brief bit of history if necessary. Exploring the field's current situation might also reveal current research trends, difficulties, and possibilities. 2. **General problem**: The significance of your research cannot be overstated. It is crucial that the reader clearly understands the problem you are addressing and its importance in the field you are publishing in. Providing context or background information may help. Discussing previous research and identifying gaps that your research fills can also be useful. Expanding on these topics will help the reader comprehend the significance of your research and its potential impact on the field. 3. **Specific Problem**: This subsection details the paper's specific research problem. The paper can analyze the topic better by focusing on one element. Clearly state the problem you are addressing. You want to make it clear that your paper adds to the literature and sheds light on your particular issue. This part establishes the paper's significance and relevance by clearly describing the study challenge and approach. ### **Step 2: Establishing a Niche** The second step is to find a place for your research by finding a gap in the literature that you want to fill. Most of the time, this step has the following subsections: 1. **Research gap**: This subsection identifies the literature gap this research addresses. The effectiveness of the research project depends on identifying this gap, which not only clarifies the research issue but also justifies the investigation. A thorough literature study and analysis of major concepts, theories, and research findings in the field identified this gap. The review conclusively showed that the area of interest is understudied, strongly highlighting the need for the current study. This section briefly describes the gap and how the planned research fully addresses it. 2. **Research question**: This subsection is crucial to the research project because it clearly states the core research question and its relevance. Good research questions start with "how" or "why." The trick is to be explicit and succinct. My standard pattern is “How does A influence B.” This part also emphasizes the research question's potential value to the discipline and explains why it's worth pursuing. This part is the study's foundation and starting point. 3. **Hypothesis**: This subsection presents the research hypothesis. The hypothesis states that the independent (factors) and dependent (measures) variables are related. The proposed study usually examines the elements that explain this association. The identified relationship may also be affected by moderating variables and boundary conditions. The research design should account for these elements to represent the relationship's complexity. ### **Step 3: Occupying the Niche** The last step is to fill the niche by explaining the purpose and goals of the paper. Most of the time, this step has the following subsections: 1. **Purpose of the study**: This section details the research's purpose. The research seeks to fill knowledge gaps and improve the field. The study answers an unanswered question that is of relevance to the field. 2. **Objectives of the study**: This part delves into the research's goals, providing a detailed breakdown of each sub-objective to help readers grasp the overall research aims. It's essential to justify each goal, because this not only clarifies the research's scope but also highlights its impact and contribution to the broader field. 3. **Significance of the study**: The importance of this section cannot be overstated. Not only does it justify the research, but it also highlights its potential social impact. For those invested in understanding the phenomena being studied, this subsection offers valuable insights that can inform their understanding and decision-making. Moreover, by contextualizing the paper’s key findings within the broader field, this section provides the necessary groundwork for future research. It is crucial, therefore, to craft this subsection with great care, paying close attention to both its content and messaging, to effectively communicate the research's significance and potential impact. This part can also give a roadmap of what’s to come in the paper for the reader, an outline of its structure. ### Takeaway A great introduction ends with a boom or a strong contribution statement that convinces reviewers (and later the readers) that this research is worth their time reading. Knowing the field where you publish is absolutely crucial for this to ensure this last statement has the right impact on that field. It is vital to understand the venue in which you are publishing, just as it is crucial to know the results of your paper. By doing so, you give your introduction the necessary relevance for the field you are targeting. Keep in mind that the job of your introduction is to: - Capture the reader's interest. - Establish a context for what will happen in the paper. - Convince your readers why your research matters. ## **Bonus: How I Generate Catchy Title Suggestions From My Abstract With ChatGPT** [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) One more thing before I go. After I experimented with larger ChatGPT prompts in the last newsletter, I came up with the following prompt that generates catchy paper titles based on your abstract. Try it out and see how it works for you: **ChatGPT Prompt**ASSISTANT: Act as a famous copywriter who is also a science professor with the following knowledge and traits. KNOWLEDGE: You've edited more than 3 million words. Your approach to copywriting is heavily informed by these books: Cashvertising, The Boron Letters, The Ultimate Sales Letter. You've deeply studied: Gary Halbert’s Desperate Nerd From Ohio, Frank Kern’s consulting letter – Would You Like Me To Personally Double… Your Business, For Free?, Joseph Sugarman’s Vision Breakthrough, Martin Conroy’s Two Young Men letter for the Wall Street Journal, Why Haven’t TV Owners Been Told These Facts, from Breakthrough Advertising by Eugene Schwartz. You have read all of Eddie Shleyner's VeryGoodCopy newsletter. TRAITS: highly intelligent, complex problem-solving skills, adaptability, creativity, interpersonal skills, PhD in STEM, 10 years copywriting experience, published 300 articles in high-impact journals, strong mentoring skills, excellent presentation, and written and verbal communication skills, a growth mindset and excellent networking abilities. TASK: Generate 15 different research paper titles for the following paper abstract: "\[PASTE YOUR ABSTRACT HERE\]" Examples of good research paper title structures with a colon are: - Catchy pun: Actual title - Tool name or acronym: Things the tool does - "Quote from qualitative study": Thematic analyses of thing tells us how to design stuff - Long title: Even longer title OUTPUT: Succinct list of research paper titles with bullet points. Give it a try for your next paper title and let me know if you have any suggestions for improving this. I always love to hear from you. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to Make ChatGPT Your Personal Writing Coach (3 Easy Tricks) URL: https://lennartnacke.com/how-to-make-chatgpt-your-personal-writing-coach-3-easy-tricks/ Last updated: 2025-03-10T14:44:03.000Z And a happy Academic Valentine’s Day to you. Here is my contribution to your academic Valentine for today: > Papers not read? > Rejections so blue? > Learn how to write papers, > so it doesn't happen to you! > > Register now at [#chi2022](https://twitter.com/hashtag/chi2022?src=hash&ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) for my course C08 (How to Write Better Research Papers for CHI), happening virtually/online on > > Friday, 22nd of April, all day![https://t.co/GetROyjV3e](https://t.co/GetROyjV3e?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [April 13, 2022](https://twitter.com/acagamic/status/1514317531175075840?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) Okay, jokes aside. Today, I want to give you some highly actionable tips about using [ChatGPT](https://chatgpt.com/?ref=lennartnacke.com) effectively in your academic editing work. If you’ve not heard about ChatGPT, you’ve probably been living under a rock for the last couple of months. It has taken the Internet by storm and received a $10 billion investment from Microsoft (and now powers the new Bing; who knew that Bing would have a moment in 2023 where it’s cooler than Google?). It’s like virtual oil right now. Everyone is talking about it or creating content about it, so I am, too. Shocking. I know. I talked about some of this yesterday, for which I managed to deep-fake myself (not quite to Deep Tom Cruise levels, but still impressive how easy it was). If you are interested in watching the highly creepy result of that experiment, here is the video: For the rest of this article, I will assume that you know what ChatGPT is or can use your Google-Fu skills to find out how to get access (yes, it’s still free, if you don’t mind slow). Today, I want to do 3 things: 1. Explain to you what a ChatGPT Mega Prompt is. 2. Demonstrate several strategies I use for rewriting your article paragraphs with ChatGPT. 3. Show you a prompt to generate a humanities-style argument for your paper. Let’s dive in. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### ChatGPT Mega Prompts This is a concept that I learned from Rob Lennon, who has been tinkering quite a bit with ChatGPT. Much of this is inspired by his prompting tips. The idea behind it is that ChatGPT gets better the more precisely you prompt it. Really, it’s all about the prompt (crazy enough, there is a marketplace on PromptBase where people sell prompts for real money). So, what’s a Mega Prompt then? It gives the AI enough information to give you answers that are the most accurate. Here is the structure: Here is the example prompt that I created with this in mind (don’t sell it, but feel free to adjust and use it for your purposes): Obviously, you can adjust the fields as you like and even the university (I figured Ivy League is probably good, but you might know a particular lighthouse in your field that is better suited for your prompt). What I'm doing here is basically copying a couple of highly desirable items from professor job ads with the knowledge and skills that universities would like professors to have, so it's not a bad idea to put them all together, I figured. The more precise, the better for ChatGPT. Obviously, you can add and remove to your liking here: Obviously, I went a bit nuts here on all the desirable traits, but many of these can be found with Google or simply by asking ChatGPT: What are the most desirable traits of a \[your field\] professor? And then, you can throw those in there. This is always the most difficult part for me to write. It requires quite a bit of thinking (and, of course, you can also just omit it), and it is individual for the task you want ChatGPT to do. Definitely, don't copy this but adjust it based on the steps to accomplish your task or leave it out. Again, this is a super simple task, but this could easily be something more complicated, such as: write an argumentative essay about the ethical implications of AI or something similar. Make sure your persona and skills match the task. I usually work with the professor persona because I need to accomplish professor things. Your mileage may vary. This is actually my next tip below, which I find super important for ChatGPT: to provide guidance for how to write the output (voice and tone descriptions are immensely powerful and will change the output of ChatGPT significantly). I like removing pre Your constraints can, of course, be many other things as well. Again, this depends on your task description, but I like the goal to tend towards something actionable for the audience, but really think a bit here about what your audience needs. Many people don’t do this, but it’s super helpful for structuring the output of your ChatGPT answers in a way that makes it easy to copy them over into your writing application. For me, the easiest format is always Markdown. That’s it. There is your mega prompt. Feel free to tinker with it and adjust it according to your needs. But really, this is one of the big secrets of getting better ChatGPT answers: providing all of the necessary context and precision for ChatGPT to work. ## Rewriting Article Paragraphs So, when I feed ChatGPT some of my writing (usually only a couple of paragraphs at a time), I can do this either with a mega prompt, but often the persona “act as” hack does the trick to prep the language model for what type of reply I am looking for. One of my favourite personas for rewriting things is “*Act as a professional copy editor with 50 years of technical writing experience and a PhD in English literature*” because why not. So, that’s what I preface my prompt with. Then, I finish my prompt with: I have to be honest about that last one, I don’t care much for rhetorical questions, so I really don’t actually like using them much in my writing, but I know some colleagues, who really dig this for their papers. Either way, this should get you going and make your writing smoother for academic papers. If I write for social media, I usually exchange those strategies for something like “*write at fifth-grade level with simple language that is clear to understand and lean toward using shorter sentences without any jargon.*” This works wonders for the output you will receive. ## Writing an Argumentation for Your Essay I should say that I am not a professor of humanities. A lot of what I write is clearly about science. That's why I find it so interesting that ChatGPT could help me build (even if only in a basic way) the structure of an argument you find in humanities papers. Here is a prompt you can try for that if you are looking to get your first argument done on a topic of interest: Of course, there are many other ways that ChatGPT can help you brainstorm arguments and topics, but this is a nice way to draft an argument that you can then deepen in future drafts. 🫶 I hope this newsletter edition was useful to you. If you enjoy this writing newsletter, please forward this email to a friend or a person who you think would benefit from such tips. ### How to stay motivated to write research articles (without working on weekends) URL: https://lennartnacke.com/how-to-stay-motivated-to-write-research-articles-without-working-on-weekends/ Last updated: 2025-08-14T01:37:37.000Z #### Key Points: How to Stay Motivated - Set clear boundaries between work and personal time to prevent burnout - Break large writing projects into smaller, manageable daily tasks - Build a strong support network of colleagues and peers - Take regular breaks and celebrate small wins to maintain momentum - Prioritize self-care activities to boost creativity and productivity Staying motivated to write research articles without sacrificing weekends requires three essential elements: setting realistic daily writing goals, establishing a consistent weekday routine, and actively protecting your weekend time for rest and personal activities. Most successful researchers write for just 1-2 hours daily during weekdays, achieving more through consistency than marathon weekend sessions. You’re a hard-working researcher. Research motivation is crucial, though. Consistency and motivation help you focus on your goals, maintain productivity, and create better-quality work that’s more likely to get published. But working long hours and sacrificing your personal life can lead to burnout. In today’s newsletter, I give you steps you can take to find a balance between work and life and keep academic writing interesting. ## Why Research Writing Motivation Matters Research motivation directly impacts your publication success. When you maintain consistent motivation: - Your work quality improves - Publication acceptance rates increase - You avoid the burnout that affects 70% of academic researchers - Your creativity and problem-solving abilities stay sharp Writing and publishing academic research articles requires a work-life balance. Setting limits on weekend work is vital for mental and physical health. - **Set boundaries:** To avoid burnout, set a schedule that allows you to complete tasks without sacrificing time with loved ones or activities that bring you joy. - **Take breaks:** Regular breaks throughout the week can refresh your motivation and provide rest. - **Realistic goals:** Set achievable goals to prevent feeling overwhelmed and leading to burnout. The goal of this week’s issue is to outline strategies that keep you motivated while you write academic research articles without making you give up your weekends or other important things in your life. By following these rules, you can find a good balance between your work and personal life and still produce high-quality published work. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Common Writing Challenges Researchers Face #### Time management Writing academic papers typically takes 3-6 months from start to publication. The extended timeline creates unique challenges: - Maintaining focus across months of work - Balancing writing with teaching duties - Managing peer review responses - Meeting publication deadlines #### Publishing pressure Academic careers often depend on publication frequency. This creates stress that can paradoxically reduce productivity. Remember this reality check I talk about below: - 70% of your work will be average - 20% might miss the mark - 10% will be exceptional The key? Volume creates opportunity for excellence. Writing academic research articles can be a long and challenging journey. You face the daunting task of dedicating time and energy to researching, writing, and editing a paper that could take months to complete. And that’s just the beginning — after publication, there could be additional work such as responding to peer reviews or making revisions based on feedback. Balancing this workload with other aspects of your academic life — like teaching and committee work — can make it tough to stay motivated during the extended writing process. Are you feeling unmotivated or lacking inspiration? This is a common feeling when writing an academic paper. To tackle this, rekindle your passion for knowledge and research. Focus on that purpose. This will keep you motivated even when you’re stuck. Reading other articles or taking a walk can also refresh your mind and get you back into the writing mood. Weekends are made for this. The pressure to publish often is a major challenge for researchers. It’s hard to stay motivated for months or years when writing and publishing a manuscript. Add to that the pressure from colleagues and peers to publish more to stay competitive in your field. To stay motivated and produce high-quality work, you must find ways to balance the demands of publishing with the need for self-care and inspiration. ## Practical Strategies for Weekday Writing Success ### Set Realistic Goals | **Timeframe** | **Goal Type** | **Example Target** | | ------------- | ------------- | ---------------------- | | Daily | Micro | Write 300 words | | Weekly | Small | Complete one section | | Monthly | Medium | Finish first draft | | Quarterly | Large | Submit for publication | Set clear, reachable, and measurable goals to keep yourself going while writing and publishing academic research. Track your progress and meet deadlines by breaking down large tasks into smaller steps or milestones, such as a daily word count or researching one article at a time. Understand that 70% of your work will likely be mediocre, 20% will be really bad, and 10% will be category-defining. The more volume, the more chance for success. Celebrate successes along the way to boost your confidence and keep up your momentum. ### Create Your Optimal Writing Routine #### Optimal Writing Routine Example ****Morning Writers** (5-7 AM) - Fewer distractions - Fresh mental energy - Complete before daily obligations ****Evening Writers** (7-9 PM) - Process the day's research - Quiet environment - Wind down with focused work ****Quick Win Approach** - 20-minute daily minimum - 500 words per session - One research article review Discover a writing routine that works for you. Dedicate a specific time each day solely to writing, whether it be two hours or twenty minutes. Experiment with different times of the day and find what works best for you — mornings or evenings with fewer distractions? Create a designated workspace with all the necessary materials for productivity and comfort. Set weekly short-term and long-term goals to keep you on track. And, for Scheibenkleister’s sake, it’s okay if you have a day where you just don’t feel like it. That is completely okay. Forgive yourself. ### Surround Yourself with Support #### Essential Support Network Components - Writing accountability partner - Senior researcher mentor - Peer review group - Online academic community Surround yourself with people who will help you reach your goals and who will encourage, guide, and hold you accountable. With the help of a strong support system, you can get helpful feedback on your progress and work hard to achieve your goals. Spend more time with people who make you feel good and less time with those who make you feel bad. If you’re an introverted person, make sure you get enough time to yourself. ### Take Regular Breaks When writing and publishing academic research, taking breaks and doing things outside of work can help keep you from getting burned out and keep you motivated. Activities like walking, reading, listening to music, or spending time with friends and family can help you get your energy back and clear your mind. You must remember to do those things regularly. ### Celebrate Small Wins Celebrate small wins and milestones to keep yourself motivated and remind yourself that your hard work is paying off. Recognize progress, no matter how small, and celebrate big wins, like finishing a draft or the whole article. Celebrating a job well done will make you more excited about future projects. ### Why You Need to Take Time Off on Weekends Taking time off on the weekends is important for recharging and rejuvenating. You’re constantly juggling research, writing, and publishing. It’s easy to forget that you need a break from all of it. So, put down the books and give yourself a mental and emotional reset. Weekends are perfect for taking long walks in nature, doing some yoga, or trying meditation; these activities will help clear your mind of any stress related to academic work. You’ll return on Monday feeling better equipped, both physically and mentally, to tackle any projects that come your way. ### Prevent Burnout with Weekend Breaks Burnout is a real concern for academics who are overworked and overwhelmed. Taking time off on the weekends can help prevent burnout by giving your mind and body a much-needed break from work to rest, relax, and recharge. Plus, taking breaks will boost your creativity and allow you to work towards your goals with renewed energy. It’s crucial tofocus on yourself in addition to academic success. Taking care of your physical health with exercise or spending quality time with loved ones is key to avoiding burnout and staying motivated. ### Balancing Academic Work and Life Keeping a good balance between work and life is important if you want to keep writing and publishing academic research articles. Without taking time off on the weekends, it's simple to become overburdened with work. But taking just a few hours each weekend away from your research will help you come back refreshed, recharged, and ready to tackle any challenges that may arise during the week. If you keep spinning your thoughts about writing, you should ask yourself, “Am I present? Does this help? Will this matter to me in a year?” Take advantage of your time off to refresh your creativity and come up with new ways of approaching problems or topics within your field of study. Getting some distance from your work will also help you see things more clearly when you look at data or try to come up with hypotheses. This will help you come to more accurate conclusions in your research projects. It might also help you re-prioritize your writing. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## Unwind on Weekends: 3 Steps to Achieving Work-Life Balance ### Step 1: Set Boundaries with Clarity #### Communication Strategies - Email auto-responders for weekends - Shared calendar showing availability - Clear project timeline expectations - Negotiated deadlines that respect boundaries Do you feel the constant pressure to work even on weekends? It’s time to set some boundaries with your colleagues and co-authors. By clearly communicating your availability, expectations, and deadlines, you’ll establish a focused work environment. But don’t just stop there; be assertive and make sure you’re not biting off more than you can handle in terms of workload or commitments. If you can, try to negotiate deadlines that give you enough time during the week so you don’t feel like you have to work extra on the weekends. ### Step 2: Prioritize and Plan Your Week Don’t let work creep into your weekends. To avoid this, prioritize your tasks for the week ahead. Start by making a list of all the tasks you need to complete, then rank them by importance. Focus on tasks that are critical for your research or academic goals. Break them down into manageable steps. Set realistic deadlines for yourself, so you’ll have a clear timeline to work with during the week, to keep you motivated and organized. ### Step 3: Say No to Non-Essentials #### The Power of Saying No Make ****No** your default response to weekend requests. Instead, offer: - Alternative weekday meeting times - Asynchronous collaboration options - Delegation to team members - Rescheduling for priority alignment Saying no to tasks that aren’t important is the key to staying motivated and not having to work on the weekends. Prioritize the most important tasks. Let go of activities that can wait or don’t require your immediate attention. And don’t just set boundaries for others. Set them for yourself, too. If a colleague asks for help but it will take up too much of your weekend time, politely decline and explain why you can’t do it at the moment. Even better: Make “No” your default answer to any request. ### Bonus: Dedicate Time to Personal Interests Take a break from academic research and writing and dedicate time to your personal interests and hobbies. Find something enjoyable to do on weekends, like going for a hike, reading a book, playing board games, doing yoga, or (I keep mentioning it) taking a walk in nature. Keeps your motivation levels high. Provides a much-needed opportunity for relaxation. So go ahead, take a step back. Reset your mind. Focus on you. ### The Secrets to Stay Motivated & Avoid Burnout Use the right strategies outlined above to find motivation and stay energized, focused, and productive — even when facing challenging obstacles or long library hours. Here’s a quick summary of what you need to keep in mind: ### Self-Care is Key Making self-care a priority is the foundation of staying motivated and avoiding burnout (e.g. taking regular breaks, setting achievable goals, seeking feedback from other experts, and creating a supportive working environment). Don’t forget to celebrate successes, even small ones, to stay driven towards bigger goals. **Daily Practices** - 5-minute meditation breaks - Healthy snack preparation - Hydration reminders - Posture checks **Weekly Practices** - Complete digital detox (4+ hours) - Meal prep for writing days - Exercise routine (3x per week) - Social activity planning **Monthly Practices** - Progress review and celebration - Goal adjustment session - Professional development activity - Full weekend off ### Strike a Balance Finding a good balance between work and personal life is the best way to stay motivated while writing and publishing academic research articles. This can be difficult but is essential for mental health and well-being. Take regular breaks, get enough sleep, eat well-balanced meals, exercise regularly, and do activities that give you joy outside of your career. Set realistic goals to stay on track without feeling overwhelmed or exhausted. ### Make Time for Writing and Researching Writing and publishing research articles can be daunting, but with the right approach, it doesn’t have to involve weekend work. Set aside dedicated time, even if it’s just an hour or two, and break down big tasks into smaller ones. Use other people as motivation by joining online groups or attending academic publishing events. #### Weekend Activities That Enhance Writing ****Physical Reset** - Nature walks - Yoga or stretching - Swimming or cycling ****Mental Recharge** - Fiction reading - Creative hobbies - Social connections ****Creative Boost** - Museum visits - Music or podcasts - New experiences With these tips and tricks, you can write and publish meaningful research articles while still enjoying your free time. Don’t let burnout hold you back; start taking action today to stay motivated, productive, and on the road to your success! ## Weekend Workers vs. Boundary Setters Comparison | **Aspect** | **Weekend Workers** | **Boundary Setters** | | ---------------------- | ------------------- | --------------------- | | Burnout Rate | 78% within 2 years | 23% within 2 years | | Publication Quality | Decreases over time | Maintains or improves | | Career Satisfaction | Low to moderate | High | | Family Relationships | Often strained | Generally positive | | Creative Output | Diminishes | Stays consistent | | Long-term Productivity | Unsustainable | Sustainable | 🫶 I hope this was useful to you. If you got value from this writing newsletter, the biggest thing you could do for me is to please forward it to a friend or a person who you think would benefit from such tips. As always, I appreciate your support. #### FAQ ****Q1: What if I have a deadline on Monday?** Plan backwards from deadlines by at least one week. If you consistently need weekends for deadlines, your planning system needs adjustment, not your weekend time. ****Q2: How do I handle advisor pressure to work weekends?** Document your weekday productivity, showing consistent progress. Present data demonstrating that rest improves your output quality and quantity. ****Q3: What about conference deadlines that fall on weekends?** Submit on Friday afternoon or use scheduling tools to auto-submit. Never wait until the weekend to finalize submissions. ****Q4: Can I write for just one hour on weekends?** No (but also yes). Complete separation creates better Monday productivity. Even one hour breaks the boundary and reduces the restorative power of time off. That being said, feel free to break any of these rules if it works for you with your routine. Everyone is different. If you take good breaks during the week, a weekend hour might not hurt you. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### How to Write Research Papers Faster (7 Challenges to Fix) URL: https://lennartnacke.com/how-to-write-research-papers-faster-7-challenges-to-fix/ Last updated: 2024-08-14T02:32:05.000Z I want to address seven challenges to writing research papers faster in today’s newsletter. You will also walk away from reading this with knowledge about how to handle every academic writer’s struggles such as these. ### 1\. Lack of motivation or focus A lot of hard work and patience are needed to write a research paper. During the process, it can be hard to stay motivated, and many problems may come up. You might have trouble focusing on the task, not have enough time because of other commitments or distractions, put it off, worry about finding reliable sources of information, have trouble understanding complicated topics or ideas, not know enough about the subject being researched, or have bad writing skills. To get past these problems with motivation, you need to make a plan and set deadlines for each step. Break down your tasks into manageable chunks. You should set specific and measurable goals for each part of your task, such as setting word counts or time-boxed deadlines for finishing certain sections. This will help you focus on one part at a time and keep you from feeling like the whole project is too much. Also, as you reach each goal, you’ll feel like you’ve done something good, which will keep you motivated and excited about your project. Reward yourself for small accomplishments and take regular breaks. Accomplishments can include completing a paper section or reading through your sources. Your rewards don’t have to be anything big — it could be just taking five minutes away from the task at hand to watch an episode of your favourite podcast or YouTube video or grab a treat from the kitchen (there you go, Pavlov). Make sure you take regular breaks throughout writing; this will give you time to step back and clear your head so that you’ll feel refreshed and more motivated when you return to work on the paper. Find a person to help keep you motivated. Having a writing buddy or accountability partner who understands how important it is to stay on track and meet deadlines will help you stay on track and make progress on your paper. They should be someone you can trust, who understands your research, and who is willing to give you honest feedback so you can get the best results. It might also be helpful if they have experience writing research papers because they could offer valuable advice about what has worked for them. Working with a partner or friend can also bring fun into the process and break up any monotony from working alone for long periods. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 2\. Not organizing research materials It can be challenging to organize all the sources and notes. With so much information to work through, it is easy for important details to get lost in the shuffle. Set up a way to file all the essential papers to ensure nothing gets lost. If you’re a paper person, you could use binders or folders to store documents and highlight critical passages. Taking digital notes or using an online database can help keep resources sorted efficiently. Setting up a reliable way to organize things early on can save time and effort when you need to get to resources later. Create a system for keeping track of sources and notes and stick to it. The key is to develop a strategy that works best for you, whether it’s using an index card filing system, creating digital documents or folders for each source, or using a note-taking app on your computer (Pro Tip: [Use Readwise Reader](https://readwise.io/i/lennart17?ref=lennartnacke.com), [Notion](https://www.notion.so/?ref=lennartnacke.com), or [Obsidian](https://obsidian.md/?ref=lennartnacke.com)). Once you have the system in place, be sure to take the time to use it correctly — inputting all necessary information about sources and taking detailed notes as needed. Staying disciplined with this task regularly will help prevent having to go back and search through piles of materials when trying to write up your paper later on. Use citation management software. It can be used to store reference lists, notes, and other documents related to the research paper. Citation management tools also make it easy to cite sources in different styles and put them in folders so they are easy to find. Also, they have features like annotating and tagging that make it easy to find information when you need it. With these tools, organizing references and creating bibliographies becomes much easier and faster than manually doing it yourself. Organize your notes by topic or theme and use keywords to label them. This allows you to easily find what you need without having to sift through all of your notes. Additionally, try colour-coding similar topics or themes so that they stand out from each other and are easy to identify at a glance. Using this organizational strategy will make academic writing faster and more efficient. ### 3\. Difficulty in outlining the structure of the paper Organizing complex topics with multiple elements can be difficult. When attempting to outline the structure of a research paper, it is important to take some time upfront to think through how all the pieces fit together. Ask yourself questions about which data points are essential and what order they should go in for maximum clarity. Break up any big ideas into smaller pieces that are easy for readers to understand. Also, if you want to explain your ideas more clearly and quickly, you might want to use diagrams. Make a mind map or concept map to see how different ideas relate to one another. This method allows you to clearly visualize the relationships between different ideas and concepts that are present in your paper, helping you to create a structure quickly and easily. Mind maps involve writing down key words, phrases, or ideas related to your topic in circles or boxes and connecting them with arrows or lines. Depending on how detailed you want the outline of your paper to be, these connections can represent entire sections of text or smaller subsections within each section. This technique is useful for organizing information into clusters so that it’s easier to understand how everything fits together without having to write it all out in sentence form first. Additionally, this tool is helpful for those who may struggle with traditional outlining methods because creating a visual representation of their ideas makes it easier for them to break down their thoughts into more manageable pieces. Use a structure guide or template for your paper. The classic IMRD structure (Introduction, Methods, Results, and Discussion) is used a lot in academia because it helps organize information in a clear and logical way. In the introduction, you should give a brief summary of the topic you’ll be talking about and also introduce the main argument or thesis statement. In CHI papers, we also find a Related Works section right after the introduction that presents the main theories, models, or frameworks that inform your research. In the methods section, you explain how you went about researching and collecting data relevant to your topic. After that come results, which display all of your findings through tables, graphs, and other means, followed by the discussion, which provides an analysis of those results in light of existing work as well as their implications for future studies. By following this basic structure, you can make sure that each section has all the necessary parts. Outline the structure of your paper before you begin writing. This can help provide clarity and direction for how you want to approach the topic, as well as make sure that all of your ideas are logically linked together. You can start outlining, following the IMRD structure or IRMRD (in the case of CHI), to begin collecting your thoughts for each of the paper sections. You can also create more specific subtopics or evidence to support each argument within those sections. Once you’ve finished this, use it to create an outline that follows a logical flow from one point to the next, so readers can easily understand what they’re reading. Additionally, if there are any gaps in your research or ideas that need further development, consider adding them to the outline so they don’t get forgotten when you start writing! [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 4\. Struggling with revising and editing Revising and editing a research paper can be tricky to navigate. It may be hard to tell which parts of the paper need to be changed and how much needs to be changed for the paper to still make sense. Before making any changes, you should read through the whole document with an unbiased eye. This will give you a sense of how everything fits together and make it easier to figure out which parts need more attention or improvement. Also, watch out for mistakes in grammar or spelling. These should always be fixed right away. Focus on one section at a time instead of trying to tackle the whole paper at once. Set aside time for revisions and editing, and be honest about how much time it will take. Depending on the length of your paper and how much work it requires, this process could take anywhere from a few hours to several days. It is best not to underestimate how long this process can take; if you rush through it, then mistakes may still be present in your final version. For this task, quadrupling the time you thought it would take is a good estimate, though this is likely still an ideal. Get feedback from peers on your rough draft. This is a great way to get a fresh perspective on what you’ve written and make sure that it makes sense for readers outside of your own headspace. When seeking feedback, be sure to give people plenty of time so that other people have enough time to review and respond before the final deadline approaches. Use a grammar checker and proofreading software to help with editing. Grammar checkers are great for picking up on simple mistakes like typos, missing words, or incorrect word usage. Proofreading software helps identify structural issues such as sentence structure, clarity of language, and overall flow. Many modern tools offer both functionalities, and AI is getting really good at correcting your drafts (you can use ChatGPT to do it or Grammarly). ### 5\. Not knowing how to seek feedback Getting feedback from peers and professors can be scary, especially when the feedback is important. It can be hard to figure out who the right people are to help, and it can be even harder to let yourself be open to constructive criticism without getting down or losing motivation. But if you want to improve the quality of your research paper, it’s important to get useful feedback from experts in your field. Getting feedback on your work helps you become more resilient, and learning how to take criticism in a positive way can help you grow as a person as you write. Identify people with relevant expertise and experience to provide feedback. Identify these individuals before beginning your work, because their input can be invaluable for improving the quality of your paper. You can meet academic experts at a conference and build a network of them, or you can ask your professors for suggestions. You could also reach out to academics who have written similar works that you like and ask if they would be willing to give you feedback on yours. Once you’ve found possible sources of feedback, talk to each one separately and tell them why their help would be helpful and what they’d get out of helping you. Make it clear what kind of feedback you want so that they can give you helpful advice as quickly as possible. Be open to constructive criticism and seek feedback early and often. When you get feedback on your research paper often, you can figure out what works well and what needs more work so that you can improve your paper in the right ways. By asking for feedback often while you’re writing, you’ll not only improve the accuracy and quality of your work, but you’ll also save time by avoiding rewrites that could have been avoided if you’d asked for feedback earlier in the project’s timeline. Use feedback to improve your paper, but remember that ultimately, it is your paper and you have the final say. Be open to constructive criticism and ideas from others, but also feel comfortable making the final decision about what changes will be made. After all, it’s your research paper, and only you can say how it should read when it’s done. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ### 6\. Not using productivity tools When writing a research paper, time is valuable, so it can be tempting to avoid learning new tools and programs that can help you get things done faster. But if you take the time to learn how to use these tools correctly, they can save you a lot of time in the long run. Some word processors, for example, have features that make it easy to quickly add citations or make bibliographies without much work. Other helpful tools include project management software for keeping track of deadlines and to-do lists, as well as speech-to-text programs for turning audio interviews into text documents without having to type out each line by hand. In the end, putting in a little extra time up front to learn how to use these productivity tools will save you hours in the long run. Research and choose the tool that best suits your needs and goals. Do your research and find out which one is best for you. This could be something as simple as an online calendar or timer (shout out to LLamaLife) or more advanced software like project management apps or word processors with built-in features like spell checkers and grammar checkers. Each tool has its own benefits, so it’s important to look at what’s out there before deciding on one. Also, if you have access to free trials of any programs, use them. They can give you an idea of how easy (or hard) it is to use certain tools before you decide to buy anything. Take the time to learn how to use the tool effectively. Take the time to learn how these tools work and how to use them in your workflow to get the most out of them. By learning how to use these tools correctly, you can save yourself hours or even days of work. Start by reading user guides or watching online tutorials. Sometimes you will also need to try and fail. Experiment with different tools and settings to find what works best for you. Some people might like one type of software or way of taking notes more than another. By trying out different tools and settings, you can find the best way to use them for you in terms of efficiency and productivity. This could mean trying out new apps, changing the way you work, or looking into options like voice recognition software. ### 7\. Dealing with writer’s block Writer’s block can be one of the most difficult challenges to overcome when writing research papers. It can lead to a lack of inspiration, originality and creativity, which may leave you feeling stuck in your paper. Taking a break and talking about your ideas with peers or mentors who know about the topic or have experience with academic writing can give you valuable feedback and tips on what could be done better. Lastly, using online resources will help you add new information to your paper and get you thinking creatively about possible solutions or points of view related to the topic. Take a break and engage in activities that inspire you. Instead of forcing yourself to write your research paper, take a break and do something else. This could mean taking a walk outside, talking to friends or family, listening to music, reading a book, or making something new in the kitchen. It could be anything that gives you energy and new ideas. By giving yourself this time, your mind will be able to calm down and start over. Experiment with different writing techniques. Here are two examples. Freewriting is when you set a timer for five minutes and write without stopping. This lets you get all of your thoughts on the page without worrying about grammar or structure. Brainstorming is another good way to find new ideas. It works best when done in groups, where everyone talks about what they think and feel about the subject at hand. Both of these methods can help get your creative juices flowing and get you out of a rut if you’re having trouble writing. Seek inspiration from outside sources, such as books, articles, or podcasts. Reading a book about your topic might help you come up with more ideas and get in the creative mood you need to write your research paper. On the other hand, if you read an interesting article about the topic, you might get some new ideas that could help you shape your argument. Listening to a relevant podcast while doing other research paper-related tasks can also be helpful. This way, you can stay interested in the material without having to write anything down. 🫶 I hope this was useful to you. If you enjoy this writing newsletter, please share it with a friend or a person who you think would benefit from such tips. As always, I appreciate your support. Hey, I was also thinking about offering a free writing seminar in February. If you’re interested, please reply to this email. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to write a good revision for CHI (and rebut some reviewer requests) URL: https://lennartnacke.com/how-to-write-a-good-revision-for-chi-and-rebut-some-reviewer-requests/ Last updated: 2024-08-14T02:26:38.000Z ### 1\. Read the paper reviews, then sleep on them After receiving your reviews from the CHI conference, be sure to read the paper reviews first. This will help you identify any issues that need to be addressed in your revision. Then, please spend some time sleeping on them so that you can come up with a better version of your work. Don’t vent about your reviewers on social media. Not only is it unprofessional, but it also reflects poorly on you and your work. You’re not alone in feeling frustrated or misunderstood after reading reviews. This is part of academic life, where we find constant rejection and criticism. Reviews are never completely objective or fair. That’s an illusion. Sometimes reviewers are lazy, mean, or wrong. The reviews might not be false, but you might need time and distance to appreciate the feedback. Don’t post if you’re an author, because it will likely backfire. Don’t complain to the technical program chair (TPC). It’s rare for complaining to the Program Chair to change the outcome of a decision regarding your paper. It is normal to feel this way about reviewers, but try to remain objective and keep in mind the workload of the program chair. If you think a reviewer has not followed the proper process (like not enough reviews) or has been excessively unprofessional, you can contact the TPC. If you have an emergency that will impact your ability to write a rebuttal promptly, you can also contact the Program Chair. ### 2\. Determine whether to revise the paper 1️⃣ ****Option 1: Highly negative feedback.** If the input you receive from reviewers is highly negative, it might not make sense to revise the paper. In this case, it may be better to withdraw your article and begin a thorough revision process. This will allow you to make the necessary changes to improve your paper and increase the chances of it being accepted at CHI’s next conference. Reflect on whether writing a rebuttal is a good use of your time. 2️⃣ ****Option 2: Too many (unrealistic) change requests.** If you find yourself with a reviewer requesting too many changes, it is crucial first to determine whether those changes are realistic. If the reviewer is asking for things that are outside of the scope of your paper or that would require major overhauls, it may not be worth revising the paper. In these cases, it may be best not to do the revision and submit to another conference or to rebut the requests and explain why they are not feasible and outline what was in your power to address in the time available. 3️⃣ ****Option 3: Positive endorsement.** If you’ve received highly positive feedback from your reviewers, they likely believe your paper is well-researched and well-written. In other words, they think it has high study rigour and clarity. If this is the case, then you should only need to make minor changes to your paper to get it accepted at CHI. ### 3\. Save the reviews Copy and paste the original reviews into a document to save a record of them. Paste all the review text into your document, summarizing the originality, significance, rigour, and recommendation ratings at the top (possibly in a table). Make it clear which parts of the text are from each reviewer by dividing up the sections accordingly (e.g., \[1AC\], \[2AC\], \[R3\]). Highlight any critical parts of the reviews and add your own comments as needed (without modifying the text of the reviews). Essential elements to highlight are anything reviewers ask for clarification and positive and negative comments. ### 4\. Extract questions, criticism, and suggestions into a separate document Reviewer requests for changes, questions, and criticisms can be helpful when revising a paper for CHI. Compile all of these requests into a separate document so you can more easily track which issues have been addressed and which need attention. This will help you track what reviewers thought about your work and how their feedback has helped improve the final product. Use colours to highlight meaningful sentences in the review. Keep the colours subtle and light, so they don’t distract from the text. This will help you quickly identify what the reviewer is saying and what they are asking for. It will help you to focus on essential points when you are revising your paper and make it easier to spot where you need to make changes. Sharing this document with your research group or author team can help you get feedback on the extracted chunks of the review. This can be a valuable way to identify any confusion or disagreement and develop a plan for addressing reviewer requests. ### 5\. Summarize, group, and synthesize reviewer issues For each review, distill the key points into bullet points that would fit on a post-it note. This will help you to quickly identify and address the main issues raised by each reviewer. Reviewers often disagree on what constitutes a strength or weakness, so it is helpful to distill each point down to its essence. This will make it easier to understand when an issue is simply a matter of opinion versus when it points out something that needs to be addressed. Tag each summary with the committee member \[1AC\], \[2AC\], \[R3\], \[R4\], etc., to specify its origin. Pay special attention to the points the 1AC lists in the meta-review, as these will likely be the most critical issues for your revision. One of the best ways to synthesize reviewer feedback is by creating an affinity map. This is a technique where you take all the input and group it into groups or categories that each capture a specific idea. This can help you see patterns and identify areas where reviewers had similar concerns. Once you have your groups, you can start brainstorming ways to address each. This takes you from having a list of loosely connected points to a smaller list of clusters that all capture concrete, specific ideas. To make your sets most effective, try to group together reviewer requests that suggest a single solution (i.e., one solution to address different concerns). In the end, these clusters are the to-do list for your paper revision. Next up, you want to order that to-do list by priority. We will use severity rankings (which you might know from usability reports). [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### 6\. Rank review issue clusters by severity Add a number ranking for each reviewer comment, with the most severe issues ranked first. This will help you prioritize which areas to focus on in your revision. I would rank along 4 severity levels: *Major*, *Important*, *Regular*, and *Minor*. **Major**: If the comment relates directly to an issue identified in the 1AC meta-review. If it does, then rank it as major. **Important**: You should rank an issue as important if it was addressed in depth by the 2AC review. **Regular**: If a reviewer brings up an issue in their comments, but it doesn’t seem like the 1AC addressed it in their meta-reviews, rank it as a regular issue. **Minor**: You should rank a revision issue as minor if the comment concerns typos, table or figure fixes that are quickly addressed, or if the statement seems like a minor aside about something that could have been done differently. Sort your review cluster to-do list from major to minor. Then brainstorm which issues you need to address and which you can rebut. Another alternative tagging/decision-making framework that can help you decide what changes actually to address is putting these issues (you could use post-it notes for each cluster) into a two-dimensional grid along the dimensions of importance (‘not’ to ‘extremely’) and revision difficulty (‘easy’ to ‘difficult’). Things that are not easy to revise should be rebutted. Although, for items that are important but not easy to revise, you have to make a thoughtful decision about how much this would improve the paper. Maybe it can be done in the time available. I prefer using the tagging above with four different severity levels, though. ### 7\. Brainstorm review responses and make revisions Now that you have a sorted list of the changes that need to be made to your paper begin working on them from the most important ones at the top of the list. For this, I would create a new separate document (and of course, a separate copy of your paper where you highlight the addressed changes). This new document contains your final responses to how you either rebutted or addressed the changes in your paper (a short statement clarifying how you addressed each review cluster). Every time you address a change from the list to your satisfaction, write a brief paragraph in this document pointing out where in the paper you made the change. This will help you keep track of your progress and make sure that all of the most crucial changes are addressed. This document will also help you remember what was said about your paper and will make it easier for reviewers to understand why you made the changes that you did. I would do a top-bottom-alternation strategy here. So, begin a revision from the top point of your list. Once you are done revising your paper to address that, take a break from revisions by writing a rebuttal paragraph or creating a fast fix to a minor issue before you tackle the next important point back on top of your list. ### 8\. Refine and format your revision document The main goal of this document is to keep the 1AC and 2AC content. You want to show the committee that you are making the right changes. It is essential to be reasonable and competent in your revision document. By doing this, you will be able to demonstrate that you are willing to work with the reviewer’s requests and make the necessary changes. By taking the time to respond to reviewers’ comments and make changes based on their feedback, you can ensure that your paper is as strong as possible. Addressing reviewer requests can also help build goodwill and improve relationships with potential future reviewers. Make sure that the themes requested by the 1AC or 2AC form the majority of your response. This will show that you’ve taken their feedback seriously and addressed their concerns. Address every reviewer request cluster directly. Explain how you implemented each revision in your document. Be sure to include specific examples and details in your response. Revisions can be time-consuming, so plan ahead and give yourself plenty of time to make the changes requested by reviewers. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to Shift Your Writing from Good to Great (13 Secrets) URL: https://lennartnacke.com/how-to-shift-your-writing-from-good-to-great-13-secrets/ Last updated: 2024-12-11T12:02:52.000Z ![audio-thumbnail](https://lennartnacke.com/content/media/2024/08/How-to-Shift-Your-Writing-from-Good-to-Great--13-Secrets-_thumb.png) How to Shift Your Writing from Good to Great (Article Voiceover) 0:00 /600.72 1× And a happy September to everyone in this writing community. Welcome back to another fresh issue of The Writing Newsletter from Professor Lennart Nacke. Things are getting intense for [**CHI writers**](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) this month because the annual deadline is upon us. Today, abstracts are due and next week, full papers will need to be submitted. If you are submitting to CHI this year, I've got some hot tips ready for you. But first, I wanted to share three tools that I found really useful for the last couple of weeks of writing papers: 1. [Audemic](https://audemic.io/?ref=lennartnacke.com) is a really cool app that lets you listen to your papers (e.g., for me that's in the shower or the tub), supercool. 2. [Citationgecko](https://www.citationgecko.com/?ref=lennartnacke.com) lets you throw in a couple of seed papers on a topic and the app extracts all references in and to these seed papers and visualizes them for you, really nice way to explore citations, and finally 3. [Powerthesaurus](https://www.powerthesaurus.org/?ref=lennartnacke.com) has been essential and helping me condense those CHI abstracts for my papers. Enough about my writing experience; here are some strong writing tips for you. Please forward this newsletter to a friend if you feel they can benefit from these tips. ![](https://acagamic.mymagic.page/content/images/2024/08/writebetterpapers-course.png) #### How to Write Better Research Papers Tired of rejection and obscurity? This course delivers the insider secrets you need to get published and cited in social sciences and at the most competitive HCI venue: CHI. [Go from unpublished to 10k+ citations](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) ## How to write the perfect CHI abstract I have mentioned online before that there are five research questions that every scientific abstract must answer, but I haven't actually broken that down into percentages. So, out of the 150 words for the abstract of your CHI paper, this is how I would split it: 1. Context: What is the space we are in? (15%, 22 words) 2. Problem: Why did you do it? (15%, 22 words) 3. Solution and methods: What did you do? How did you do it? (20%, 30-31 words) 4. Results: What did you find out? (20%, 30-31 words) 5. Implications: What does it all mean? What are the detailed takeaways? What does it all mean when put together? (30%, 45 words) In my experience, many student-led papers are top-heavy in their first-draft. This means too much space is given to describing the context and the problem, when really the reviewer is most interested in your findings and implications, so try to prioritize this in your writing when you are polishing your abstract for submission. I hope this helps you get the clarity and structure together for your abstracts today, if you are submitting. And if you’re not submitting, I’m sure your next paper abstract will benefit from this structure. Quick reminder to never use citations in your abstracts, that’s a rookie mistake. Good luck with those abstract submissions. Now, on to the next week, when the paper is due. I’m hoping that you are currently just polishing and not putting fresh data into your paper. Either way, use these 13 writing strategies to polish your paper into a better version of itself. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 6,737 researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. ## 13 Writing Strategies for a Better Research Paper Every scientific subfield has its own rules and quirks, but I’ve found the following 13 rules quite useful when attempting to publish a [CHI paper](https://chi.acm.org/?ref=lennartnacke.com). ### **1\. Your credibility comes from using specific numbers and explaining things with specific language** So, I would argue for writing something specific like p = 0.003 not p < .01 in your paper. However, Jacob Wobbrock commented on Twitter (and he’s got a good point), that in this example, the whole point is to show significance cleared an (arbitrary) threshold. Athanasios Mazarakis argued that for replication exact p-values are useful. Andy Field replied to always “report the exact p and let the reader determine the threshold (if they’re into them) based on the research question, not based on a century of habit. \[…\] It’s not saving space and it prevents readers from making judgements based on a different justification of an appropriate alpha.” I think providing exact value is important, if not in your text, at least in additional materials or have a pre-registered study protocol on [OSF.](https://osf.io/?ref=lennartnacke.com) Also, don’t write: “The study had various effects.” It is much better to phrase this with more specificity as: “Y increased X under Z conditions.” Always be specific in your writing. ### **2\. All killer, no filler** Cut out the fat in your writing, delete these filler phrases: Basically, Rather, Just, As a matter of fact, At all times. This is what polishing is all about, removing embellishments that your paper really does not need to tell it’s story. ### **3\. Complexity is a crutch** Always try to explain things in simple terms. This is highly related to point 1 about being specific, but it’s even better to not write with unnecessary complexity. It makes you harder to understand. That’s not good. Change these: A lot of → Many, In order to → To (my personal pet peeve), Hard to do → Difficult, For the purpose of → To, On an annual basis → Yearly. Be specific — not arrogant — in your writing. Respect your reader’s time and get to the point. ### **4\. Do not use contractions in academic writing** Avoid things like: Don’t → do not, Mustn’t → must not, I’dn’t’ve → I didn’t have, Y’all’d’ve’f’Id’ve → You all would have if I would have. It makes you sound informal and removes the professionalism required in an academic paper. Always search for those contractions. When writing conversational style (like what I’m doing in this email), then it’s okay. In academic papers, it just looks weird and casual. ### **5\. Vary your sentence structure** Most sentences are SUBJECT → PREDICATE → OBJECT. You were taught to do it that way in school. It’s boring. It is more interesting to make these elements dance so your writing creates music. Shake up your sentence length and construction. **Here is an example inspired by the amazing Gary Provost:** > This is a sentence. It has four words. Here are four more. Four-word sentences work. Many together become monotonous. Listen to my writing. It is getting boring. This record sounds stuck. Its boring hum drones. Your ear demands alternation. As soon as you begin varying the sentence length, your words become unstuck. It’s music. My words are singing a tune. I alternate medium and short sentence lengths. And then — when I believe the reader is rested and ready — I write a long sentence that is a little harder to follow but exciting enough for the reader to fully read until the end, to end in a crescendo, a signpost that this was important. > How I made my writing more engaging with one simple trick: > > Vary sentence length. > > Short is punchy. > Long is bunchy. > > Both have visuals. > Both have sounds. > > There is a time and place for each. > Finding a delicate balance is the trick. > > Your sentences want to sing. > Give them a voice. [pic.twitter.com/m4jssvfpAg](https://t.co/m4jssvfpAg?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [April 24, 2024](https://twitter.com/acagamic/status/1783107387953729859?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) ### **6\. Always use storytelling in your writing to make it more engaging** *“… evidence points to the use of ‘creepy’ as a buzzword for the nebulous push-and-pull between consumer-driven convenience and dubious data practices.”* See: John S. Seberger, Irina Shklovski, Emily Swiatek, and Sameer Patil. 2022\. Still Creepy After All These Years:The Normalization of Affective Discomfort in App Use. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 159, 1–19\. [https://doi.org/10.1145/3491102.3502112](https://doi.org/10.1145/3491102.3502112?ref=lennartnacke.com). Illustrate your research with examples. ### **7\. Write parts of your paper EVERY DAY even if no one reads it** “You look ridiculous if you dance. You look ridiculous if you don’t dance. So you might as well dance.” — Gertrude Stein. Pushing content forward will make editing so much easier before the deadline. I bet you wish, you would have followed this tip half a year ago. Try this for next year’s deadline, you’ll notice the difference. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### **8\. The first sentence of every paper should be short and compelling** A hook, an opener, a worldview. I love this example from last year’s CHI: *“From squeezing pliers to holding a key: the human hand has evolved a considerable dexterity for powerful and precise grips.”* See: Martin Schmitz, Sebastian Günther, Dominik Schön, and Florian Müller. 2022\. Squeezy-Feely: Investigating Lateral Thumb-Index Pinching as an Input Modality. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 61, 1–15\. [https://doi.org/10.1145/3491102.3501981](https://doi.org/10.1145/3491102.3501981?ref=lennartnacke.com). Think of it as a wide panorama shot. > The first sentence of every paper should be short and compelling. > > A hook, an opener, a worldview. > > Think of it as the wide panorama shot in a Hollywood movie. [pic.twitter.com/ssCIoZuOe3](https://t.co/ssCIoZuOe3?ref=lennartnacke.com) > > — Prof Lennart Nacke, PhD (@acagamic) [March 28, 2024](https://twitter.com/acagamic/status/1773322913531523264?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) ### **9\. Adverbs are asinine** Try to avoid them. [Stephen King hates them, too](https://www.goodreads.com/quotes/430289-i-believe-the-road-to-hell-is-paved-with-adverbs?ref=lennartnacke.com). Look at how I have rephrased these adverbial structures: The effects were really interesting. → The effects were profound. The participant walked quickly forward. → They sprinted ahead. They were speaking loudly. → They were yelling. Use the proper verbs instead. Don’t rely on crutches to communicate clearly. ### **10\. Avoid hyperboles and false interpretations** Another common mistake of junior researchers is overclaiming the results or saying something was significant when it really wasn’t (hot tip: always report and focus more on effect size than significance). Examples: The results were encroaching significance. → The results were not significant. This finding has a massive impact on research. → This finding has an impact on research because … \[X\]. Don’t make or blow stuff up. ### **11\. Always read your writing out loud when editing** Alternatively, get the built-in [Read out loud function](https://www.adobe.com/acrobat/hub/how-to/how-to-read-pdf-aloud?ref=lennartnacke.com) of your PDF software to do it for you. It will make you catch awkward phrases and parts that make no sense twice as fast. > Here are 3 quick tips to improve writing your [#chi2023](https://twitter.com/hashtag/chi2023?src=hash&ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) paper now: > > 1) Write for 1 specific scientist → pick the person most likely to review your paper → write for them. > > 2) Read your draft out loud → if it doesn't flow, give it another go. > > 3) Proofread on your mobile phone. > > — Prof Lennart Nacke, PhD (@acagamic) [August 3, 2022](https://twitter.com/acagamic/status/1554862381859217409?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) ### **12\. Use the best voice for your reporting (predominantly active voice)** Active voice energizes your writing and helps you quickly make your point. It’s ok to say “we did” in research papers. It is also ok to refer to “we” when you are talking about the researcher and the reader together. Only if you describe an inactive object is passive ok. ### **13\. Convert nouns to verbs** This is super useful for your paper titles, too. Get rid of those stiff nouns. Make your research DO stuff. Examples: We collaborated on the creation process of new guidelines. → We created new guidelines together. BTW, 9 out of 10 times, you can delete the word “process” from your writing, too. You probably never really need it. It’s like defrosting a smoothie. Liquid and delicious. Goodbye, stiffness. ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Join 6,737 researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. > Every successful CHI author follows these 13 rules of writing. > > Most people do not know them. > > Here they are for free to help you become a better research writer. > > 🧵⬇️ > > — Prof Lennart Nacke, PhD (@acagamic) [September 7, 2022](https://twitter.com/acagamic/status/1567513566261006336?ref%5Fsrc=twsrc%5Etfw&ref=lennartnacke.com) If you follow these rules, you’ll probably cut your paper polishing time in half. It’s always good to have these on hand when going into the final draft stage. Maybe print them out somewhere where they are easy for you to look up. [Liked this post? Leave a tip](https://lennartnacke.com/#/portal/support) **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to write a strong discussion section for your CHI paper URL: https://lennartnacke.com/how-to-write-a-strong-discussion-section-for-your-chi-paper/ Last updated: 2024-09-10T15:58:23.000Z Discussion sections are often considered by reviewers to be the make-it or break-it moment of your paper. Here, you show that you can synthesize your findings with the existing literature. You show your skills in arguing the unique scientific contribution of your work and your consideration of existing literature. Knowing how to write a strong discussion section has the following benefits: - If the reviewer had any doubts about your contribution, this is your big chance to convince them - You can contextualize your findings, imbuing them with more meaning - You can outline and shape the work that comes after this research - You get another chance to discuss related work in light of your findings, engaging with the existing literature Unfortunately, academic writers often miss this opportunity to strengthen their research and engage with it based on their understanding of the existing work. Too often I see people just writing a plain old summary of their work. That is not enough. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) ### Discussion sections are like business cards for your current study. Do them well and you'll make connections. This is really the best way to view a discussion section. You have a great chance here to convince people that your work has meaning for the scientific community in which you publish. If you write this well, it will lead to more citations and more engagement with your work. And this should be a goal for important research, you want it to be noticed and used by the scientific community. Therefore, you should focus on the following areas in your discussion section: 1. **Importance statement**. At the beginning of your discussion, you want to quickly (really only very briefly) summarize your main results and then say right away why they matter. If you have many results, use this structure: "This was result x. It is important to HCI research because ..." and then do this for each result. One or two sentences outlining the summarize result and why it matters. 2. **Findings extend previous work**. Now, it's time to engage with existing results. Revisit your related work section (in writing mode, this is also a great moment to revise it and add some papers to discuss there). Make a table to tie your main results to existing related work. Did you find the same? Did you find something different? Can you explain this? Compare. 3. **Scientific implications of findings**. Here you want to reach out and oracle within the space of reason of your results will change things. Either a theoretical understanding of a concept or the way we approach a method. Or implications for life at large. Focus on the impact on the scientific community you are writing for first, then broaden to people at large. 4. **Forward look to facilitating future work**. What comes next? How are you opening up the research space for more work to come? Think about the future. What future work that either your yourself are capable of doing or others in your field? The more specific you outline the next steps, the better. Researchers often read discussions looking for inspiration on what to with their research questions. Provide this context for them. 5. **Limitations of current approach**. It is important to be honest about what you were able to achieve without lessening your contribution. Try to be succinct but fair in your evaluation of your own work and what it can explain. It is important to reign any visionary moments you had in point 3 back into context and say what conclusions are reasonable to draw from your approach. 6. **How this research is important to the current field?** Yes, we already mentioned this at the start, but here we are going full circle by just putting a little oomph at the end of your paper. It is good to end on a high note and emphasize your contributions once again. This will leave reviewers with a positive understanding regarding your impact. And that's it. Focus on these six steps and you'll give your results section your best chance to make an impression in your current paper. The benefit of a strong results section is that it can really elevate a mediocre paper and tap into a committee's emotions when it comes to making a decision about the paper. Good luck with your writing. BTW: [Daniel Buschek shared his 10 tips for making a discussion](https://dbuschek.medium.com/how-to-write-better-discussions-for-your-hci-study-be851092f351?ref=lennartnacke.com) better on Twitter with me. I specifically like how he recommends: - You should already be writing your discussion section right now if you are planning to submit to CHI. It's no good starting too late. - Discussion should be about 2-3 pages long (in the new format) don't write a discussion that's too short and lacks depth. - Use subheadings to structure your discussion section. Nobody wants to read a wall of text. Also, if your subheadings are actual findings or takeaways, it helps skimmability of your paper, which is an asset for you. - Answer the question: "What does it all mean?" in your discussion. This is crucial for reviewers and readers. - Connect back to the literature and don't just write a summary (I already mentioned this above). - If you consider the academic hourglass, be aware to broaden your implications, move from specific to broad in your discussion. - Explore alternative interpretations. This is best done in discussions with colleagues online or at your institution. Make your work matter beyond your niche. - Point out contradictions and disagreements with existing work. Be critical but courteous. - Explain your decision in your research that might limit your findings candidly. - Here are the concrete writing prompts that Daniel gives at the end of his article: - Discuss the impact of each experimental condition or effect in a separate subsection. Relate it to other studies on this condition or effect in detail. - Discuss two ways of interpreting a certain result. Do you favour one explanation? Why? - Discuss divergent/convergent findings. - Discuss your study design choices. Reflect on them with your results. Would you recommend your method to others? - Discuss limitations (like sample diversity and size, study duration, novelty effects, development shortcuts, and aspects of internal vs. external validity). - Discuss broader impact (regarding, for example, society, privacy, and human biases). - Was there a lighthouse paper inspiring you to do this work? Relating your findings to that paper, and others who have cited it, is worth a discussion subsection of its own? - Addressing a critical or curious question from a peer would be interesting to many and is worth a subsection. Buying an online course that can improve your CHI paper writing is like getting an oil change for your car. You can go a long time without it, but everything runs smoother with it. If you feel you could use the extra help for your CHI paper and you are a student, please reach out to me via email for a discount. [Liked this post? Leave a tip](https://lennartnacke.com/#/portal/support) **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### How to Master Introduction and Paragraph Strategies URL: https://lennartnacke.com/how-to-master-introduction-and-paragraph-strategies/ Last updated: 2025-09-15T00:01:08.000Z Happy July. Thank you again for subscribing to my The Writing Newsletter. I hope you are enjoying the Summer (or Winter in the Southern Hemisphere). It's been an interesting month for me. The professorial work at the University is not slowing down but ramping up. The summer has many writing-hungry graduate students just getting ready to submit their work to CHI this year, and I am still trying to understand how selling online works. 🫣 While I recorded some awesome new content for the CHI course in the last month, I always want to do more. And there are more coming this month. I also produced some more free content on my [YouTube channel](https://go.lennartnacke.com/YouTube?ref=lennartnacke.com) (I wanted to do one every week, but man, that's a struggle if you work full time). I would love for you to check them out (subscribe, like, and all that funny business): Professionally, I wrapped up most of my duties as Papers co-chair of CHI PLAY 2022 (together with Guo Freeman and Scott Bateman) and with our new revise and resubmit process much work went into the extra reviewing in the second round. Quite a challenging process. But the publications are now part of the journal Proceedings of the ACM on Human-Computer Interaction, which has now been ranked at #4 in the updated Google Scholar metrics this year, so CHI PLAY is getting closer to CHI's impact. ## Get Write Insight Become a smarter researcher in 5 minutes per week. Join 10k+ researchers Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. After we've sent out the notifications, many researchers probably thought: "Rejected! Again! I hate academia!" I know it's soul-crushing to feel like this after getting another paper rejected. Nobody wants to be in that situation over and over again. It sucks. Not to mention the self-doubts that creep in about your writing abilities, questioning the validity of your research, or feeling like you don't belong. That can be so exhausting. But don't give up or quit academia yet. You know you're not alone. Others have gone through this process for years. For example, I have built systems to combat cluelessness and systematize strategies in research writing. Once I've built my systems and understood the publication process in detail, writing papers became fun again. I wasn't frustrated anymore about what I could submit, I struggled less with last-minute writing scrambles, and I wasn't confused about the type of contribution I'm making to the HCI community. The training that I created was to help my graduate students avoid suffering from the same frustrations that I went through when I learned how to write for CHI and HCI journals. There's always this writing newsletter, [my podcast](https://anchor.fm/how-to-write-chi-papers?ref=lennartnacke.com) (which I promise to update again soon) and the [How to Write Better Research Papers](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com) course to teach you everything I know about writing better research papers. I ran a 50% off sale last month, which surprisingly wasn't as effective as I thought, but I figure many people are currently just busy doing the research or find it challenging to pay for the course. My goal is to implement parity purchasing power next month for the course, if I can figure out how to do it. Enough about my life and how the course is coming together. Here are some hot writing tips for this month. ## How to write an introduction to a paper In 2004, Linguist John M. Swales discussed a deviant take on how to write introductions. Most researchers could learn a thing or two from his work. Here’s the gist of it. In 1990, Swales proposed to structure the introduction section in research papers as the **CARS** (Create A Research Space) model 1. Establishing a territory 2. Establishing a niche 3. Occupying the niche. 11 years later, Lewin et al. proposed a modern update on this rhetorical structure of a research paper introduction as 1. Claiming relevance of field 2. Establishing the gap present research is meant to fill 3. Previewing authors’ new accomplishments. Additionally, computational linguists propose a much simpler 4-step model for research paper introductions: 1. Background 2. Other research 3. Contrast/Research Gap 4. Aim of the paper This prompted Swales to update his CARS model into a different process for writing introductions: Establishing a... 1. Territory (citations a must) - Discuss general topics with increasing specificity 2. Niche (citations optional) - Indicate a gap OR add to what we know - Present positive justification (optional) 3. Presenting the present work (citations optional) 1. (obligatory) Announcing present research descriptively and/or purposively 2. (optional) Presenting RQs or hypotheses 3. (optional) Definitional clarifications 4. (optional) Summarizing methods 5. Announcing principal outcomes 6. Stating the value of the present research 7. Outlining the structure of the paper ## The PEEL Technique for Paragraph Writing PEEL or sometimes TEEL stands for **Point** (sometimes *Topic*), **Evidence**, **Explanation**, **Link**. And this is how you structure writing paragraphs. Every paragraph should be broken down into those four sections. 1. **Point**: What’s the fastest way to make your point? Simply state it in the first sentence of your paragraph. This is also called the topic sentence. 2. **Evidence**: People who succeed in presenting evidence support their main point with examples. So, here are a few easy ways you can expand upon the point you made with examples: 1. Facts 2. Statistics 3. Research findings 4. Quotes from credible authorities or primary texts in the field 5. Anything that plausibly supports your point 3. **Explanation**: Success for explanations in a paragraph 100% comes down to (Note that sometimes you can switch the order of evidence and explanation): 1. Showing your understanding of the topic 2. Explaining it in more detail 3. How and why your evidence supports your point 4. Help the reader interpret the evidence 4. **Link**: The last paragraph sentence serves to boost your original point or link directly to the next paragraph’s main point. This link sentence serves as a transition to the next topic. The faster you can adopt this PEEL framework, the easier it will be for you to: - Structure your writing - Reinforce your argumentation - Guide the reader through your paper I wish you good luck on your journey and hope this tip was valuable to you. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### 10 issues that will end your academic writing career URL: https://lennartnacke.com/10-issues-that-will-end-your-academic-writing-career/ Last updated: 2024-08-14T01:55:55.000Z Happy June. Thank you again for subscribing to my Writing Newsletter. Once a month, I am sending you this email with writing tips and updates from the world of my writing course. The following tips will take only a couple of minutes to read. ## **1\. Escape distractions** Academic writers love distractions. Laundry becomes an important chore all of a sudden. So, does chatting with lab mates about your research results. Avoid putting yourself into a situation that lets you get away with not writing. ## **2\. Under-edit your first draft** Academics love a good debate about finding the most appropriate term for something to be crystal clear about their topic. This takes some time though and prevents you from getting words on paper. So, forget about syntax and grammar and just throw up some words on a page. Done. Edit in detail later. ## **3\. Avoid over-iterating the manuscript** You've been there. It's called thesis hell. You keep editing and finding mistakes in your writing. The reality is, though, that you will probably never be completely happy with a manuscript. At some point, make the call and submit. This is why CHI writers love the CHI deadline. It gives them a reason to finish. When submitting to a journal, set yourself a deadline with a colleague that holds you accountable to achieve the same results and actually submit. ## **4\. Prevent procrastination** Yes, sometimes it is not just distractions that keep you from writing your paper. Sometimes, you just keep putting off getting that first manuscript done. You keep wading through data but no words are hitting the paper. The secret here is to just create a daily writing habit. Put yourself in a chair and do not allow yourself to get up until you have 500 words ready. Quality doesn't matter. Get words on paper. ## **5\. Build your confidence** At the beginning of every academic writing journey is a lack of self-confidence. You don't really know what you are doing or whether you are doing things right. Fun fact, most academics are making the process up as they go. You become more confident over time. Reframe your mindset: confidence is built by writing regularly. Your writing skills will improve as a result. ## **6\. Face the fear of scooping** Yes, some academics get scooped. This is a thing in research. However, it's usually not the end of the world and often it just requires a reframing of your initial idea. I constantly see graduate students afraid of someone having already done what they are doing. The thing is, when you find very closely related work, it allows you to steer the ship of your research away from that tide. The best way to overcome this is to always work on being highly specific about your research question and results. This makes it less susceptible to being scooped. ## **7\. Overcome impostor syndrome** You constantly ask yourself why anyone would believe you with your research results, specifically when you are just starting out as a grad student. However, even the most accomplished academics suffer from impostor syndrome. There is always someone higher up in the food chain who has done more. The goal is not to climb to the top of the mountain but to stay in the village and socialize. Be vulnerable about your fears and you will find an army of supporting academics around the world who are going through the same challenges. ## **8\. Be a consistent writer** If you want to set yourself apart from anyone else doing research, really the only criterion that matters over time is to consistently work on manuscripts. Manage the scope to keep your work-life balance in check, but definitely consider academic collaborations to allow you to stay consistent in your writing even if you have not run your own study for one year. ## **9\. Strategize for publication venues** You are obviously, as part of this newsletter, interested in publishing at the CHI conference, but there are (a) other subcommunities in CHI with their excellent conferences, like CHI PLAY, CSCW, UbiComp, and others, and (b) journals with equally interesting articles but a more forgiving revision process for manuscript submissions. Settle on 1 to 2 venues per year and focus your efforts on those. Being specific is good for your research visibility. No need to submit to every conference out there. ## **10\. Create a time and space for your writing work** I am highly creative at night when everyone is asleep and I have large writing spaces in the house to myself to take my laptop to and write away. This is not everyone's jam. Some people love writing first thing in the morning or in the afternoon. Whatever it is for you, it is important to schedule this as a regular thing in your calendar and turn off everything else on your computer, so you can focus on writing itself. That, more than anything, will get your paper accepted at some point. [Subscribe to this newsletter](https://go.lennartnacke.com/newsletter?ref=lennartnacke.com) Don't worry. A lot of writers are struggling with these issues and it's common to face these challenges as an academic writer. I wish you good luck on your journey and hope this tip was valuable to you. If you're not getting value from these writing tips, please consider unsubscribing below. It’ll clear up your schedule for more important things for you, and I really won’t mind. If you enjoy this writing newsletter and you are feeling generous today, the best way you could support me would be to share it with others on Twitter. Or share it with a friend or a person who you think would benefit from such tips. As always, I appreciate your support. **Curious to explore how we can tackle your writing struggles? I've got 3 suggestions that could be a great fit.** 1. **[Get my CHI paper writing masterclass](https://go.lennartnacke.com/chicourse?ref=lennartnacke.com):** Unlock your potential with the How to Write Better Papers Course for HCI researchers. This course offers concise, actionable video lessons you can absorb at your own pace, saving you time. Get expert guidance tailored for CHI and HCI publications with proven strategies. Gain the skills to succeed in a competitive field. 2. **[Learn how to write papers with AI ethically](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com):**Access the AI Research Tools Webinar to improve your research and writing skills. Enjoy a 3-hour tutorial with subtitles in 19 languages, 46 detailed slides, and a 1-hour ChatGPT bonus tutorial with 39 prompts. Learn from 3 app tutorials (Yomu AI, SciSpace, and Sourcely) and get a 184-page Mastery Guide on 34 AI tools. This bundle provides everything you need for AI-powered academic success. 3. **[Defend your thesis with confidence](https://go.lennartnacke.com/thesis?ref=lennartnacke.com):**Increase your productivity and graduate success with this thesis workshop. Get instant access to a 3-hour video, 64 instructional slides, and curated productivity software. Use our online whiteboard and a 7-page workbook of checklists and prompts. Prepare confidently with a 10-page Viva questions guide and a PhD exam checklist. Optimize your thesis workflow and excel in your studies. ### Master Academic Writing with This 6-Step Framework URL: https://lennartnacke.com/a-six-step-framework-for-academic-writing/ Last updated: 2025-09-14T23:57:11.000Z Happy First of May, student academic. Thank you again for being a part of the [CHI Paper Writing Course](https://chicourse.com/?ref=lennartnacke.com) , a writing center or a learning center for thoughts and ideas, and tips to help improve your writing abilities. And if you are presenting your academic paper at CHI 2022 this year, welcome to the conference. Beginning today, I will send you an email once a month with writing tips and updates from the world of academic writing skills. (BTW, if you're struggling with more than just grammar and punctuation, [get my free thesis statement guide to help you improve your writing skills, get your dissertation published, and keep academic integrity](https://newsletter.nacke.ca/thesis-statement-guide?ref=lennartnacke.com)). ## Get Write Insight Become a smarter researcher in 9 minutes tops per week. Subscribe Email sent! Check your inbox to complete your signup. No spam. Unsubscribe anytime. Join 6,000+ subscribers every week. # A six-step framework to help you master academic writing Here is a professional academic writing framework of six steps in any writing process that goes beyond any particular topic, use of language, and critical thinking skills that are essential to getting scholarly articles into academic journals: ## Master academic writing essential skills with this structure 1. **Choose a topic or a research question.** Before you begin writing, you will need to select a topic or some ideas of what to write about. For a research paper, the initial moment is often your research question. Academic writing requires one. For any piece of writing, the research question is tied to addressing a problem that matters to you and hopefully other academics. 2. **Gather ideas.** When you have a topic or find a research question, think about what you will need to write about that topic. When writing a research paper, we often engage in length and depth with ideas and arguments in related work to find out two things: (1) how have people solved similar problems or questions and (2) why do they think it matters to your research field or your types of research? Spend some time exploring your field and refining your research question before you begin organizing your thoughts and present them in a clear manner. Every academic needs a clear thesis statement. Writing effectively and concisely enables students' success in higher education. 3. **Organize.** Part of this is just writer's time management. But decide specifically which of the ideas for solving the research problem you want to use and how you want to use them. Students struggle with academic writing but most research papers follow a tight template of introduction, related work, methods, results, discussion, limitations, and future work with some variation of these common types. So, for your next paper, these bins help you identify how to fit your writing to meet the content of these sections. Do you have an idea for framing? Consider putting a bullet point in the introduction or discussion. 4. **Write.** Write your paper from start to finish. Write it rough, make mistakes, and don't edit it. Don't worry about writing styles. Worry about how to provide valuable insights that help you develop or make you able to explain you ideas. To achieve your goals to answer the research question. Ideally, use bullet points in each section to expand. Use your notes from step 2. 5. **Review structure and content.** Now, inspect what you have written. Use online resources used by writers. Tools that let you edit and proofread your writing and spot errors in grammar. Read your writing aloud to yourself or others. This is the best way to find awkward spots. Identify spots where you can add more information, and check if you have any unnecessary information. Discuss with peers. Ideally, find a writing community where they read your article, and you read their article. Getting your peers' feedback is an excellent way to know if your writing is clear and compelling. Learning to give feedback about other people's writing helps you improve your own. You may want to go on to step six now and revise the structure and content of your article before you correct it. 6. **Revise structure and content.** Use the feedback from step five as guidance to help rewrite your article, improving the structure and content. You might need to explain something more clearly or add more details. Proofread. Other papers can help. Reread your article. Check your spelling and grammar when rereading the article, and think about your word choices. Are they adequate to communicate what you want to share? Once all errors are corrected, your article is finished. Then just check your references (to avoid plagiarism), maybe they are in American Psychological Association (APA) style. Either way, for online academic writing, you want to link citations to your references. [Get my free guide on how to write a thesis statement](https://newsletter.nacke.ca/thesis-statement-guide?ref=lennartnacke.com) # What type of proficient academic writer are you? That's it. Academic writing is a skill. This framework applies to most writing, and I first found it in a [book from Zemach and Rumisek](https://amzn.to/3xbwoWI?ref=lennartnacke.com) about academic writing and have adjusted it since for my article writing process. Writing is one aspect of academic writing. Essential for success in higher education and university study. But you also want to enable students to write words that might sound formal and objective but really help them express their ideas, enable valuable insights, and use evidence to support their argumentation. Use these resources to help you write better and put your ideas in a clear system that meets the writing demands of your next publication. It is a great way to get started. ## Structure of academic writing work An good writer can be divided into three different types based on aspects of their academic writing approach and experience. Some key types are: 1. The beginner writer. This writer is just starting out in academic writing and may be unsure of the conventions and expectations of this genre. 2. The experienced writer. This writer has published several papers and is familiar with the academic writing process. 3. The expert writer. This writer is a leader in their field and serves as a mentor to newer academic writers. And regardless of your type, by following this 6-step framework, you can continually improve your academic writing skills. But it's important to understand the unique challenges each type of writer faces. Therefore, I hope these tips and resources will be helpful as you continue to build your academic writing skills. ## Resources are available for academic careers and types of academic writing If you enjoy this newsletter and feel generous today, the best way you could support me would be to share it with people online. And don't forget [to check out my free thesis statement guide.](https://newsletter.nacke.ca/thesis-statement-guide?ref=lennartnacke.com) Have an excellent CHI conference. Until next month. P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: [A seven-day email course](https://newsletter.nacke.ca/products/mini-research-course?ref=lennartnacke.com) that teaches you the basics of research methods. Or the recordings of our [​AI research tools webinar​](https://go.lennartnacke.com/aitoolswebinar?ref=lennartnacke.com) and [​PhD student fast track webinar​](https://go.lennartnacke.com/thesis?ref=lennartnacke.com). ### How to Write CHI Papers, Online Edition (CHI 2021) URL: https://lennartnacke.com/how-to-write-chi-papers-online-edition-chi-2021/ Last updated: 2024-01-21T07:54:26.000Z Writing papers is at the heart of our craft as CHI researchers. Knowing what reviewers are looking for in a paper helps us write and structure papers more clearly. Yet, skillful writing sometimes seems ephemeral to us when trying to structure our research ideas around what we perceive as the CHI community’s demands. I am excited to be teaching this course again as we have a unique chance for sharing knowledge around creating successful CHI papers. ## **Course Schedule (EST timezone)** - **11:00-12:00** Unit 1: Structure and Style, Miro board - **12:00-12:30** BLinner Break with Video - **12:30-14:00** Unit 2: Abstract and Intro Exercise - **14:00-14:30** Coffee Chat - **14:30-15:30** Unit 3: Fundamental Discussion - **15:30-16:00** Coffee Chat - **16:00-17:00** Unit 4: Bullet Pointing to Polish Exercise Get access to the extended online course ‘[How to Write Better Research Papers](https://www.chicourse.com/courses/how-to-write-better-research-papers?ref=lennartnacke.com)’ based on this CHI course. ### The CHI Paper Writing Podcast is live URL: https://lennartnacke.com/the-chi-paper-writing-podcast-is-live/ Last updated: 2024-01-21T07:53:47.000Z After months of working on it and on our own CHI papers in the HCI Games Group, I finally had time to cut and edit together the first episode of the How to Write CHI Papers podcast with many insightful interviews from 14 CHI rockstars Susan Dumais, Andrés Lucero, Erin Solovey, Effie Law, Andy Cockburn, Nick Graham, Meredith Ringel Morris, Kristina Höök, Anirudha Joshi, Annika Waern, Hrvoje Benko, Anind Dey, Albrecht Schmidt, and Derek Reilly. [The podcast is currently hosted here](https://anchor.fm/how-to-write-chi-papers?ref=lennartnacke.com) and [on Spotify](https://open.spotify.com/show/2xhrkxOuy0KirT0um7PxMi?ref=lennartnacke.com), but you can also listen on [Google Podcasts](https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy9iMjUzZTE0L3BvZGNhc3QvcnNz&ref=lennartnacke.com) or [Apple Podcasts](https://podcasts.apple.com/ca/podcast/how-to-write-chi-papers/id1477199050?ref=lennartnacke.com). I am looking forward to your feedback. Thanks to those of you that have taken this course in the past. I am looking forward to preparing more course-related materials in the years to come. For now, all current materials are hosted on [this website](http://writing.chicourse.com/?ref=lennartnacke.com). As we are headed straight towards the CHI deadline (definitely the abstract submission deadline tomorrow), I wish everyone good luck with their papers and hope you can find this podcast episode helpful when preparing your CHI submissions. [Listen to the Podcast](https://podcasters.spotify.com/pod/show/how-to-write-chi-papers?ref=lennartnacke.com) ### How to write CHI 2019 Papers URL: https://lennartnacke.com/how-to-write-chi-2019-papers/ Last updated: 2024-01-21T07:53:09.000Z This course has been taught at CHI three times and before that at CHI PLAY and SIGCHI summer schools and via invitations at several institutions. It is highly popular with young CHI researchers. The instructor is also available to teach this course at your institution and the course has been taught as part of research skills workshops across the world. ![](https://cdn.magicpages.co/acagamic.mymagic.page/2024/01/image--7-.png) ## **CHI 2019 Schedule** Some core information and materials for this course: 1. [Link to the course description of ‘How to Write CHI Papers (Third Edition)’ by Lennart E. Nacke in the ACM Digital Library](https://dl.acm.org/citation.cfm?id=3298817&ref=lennartnacke.com) 2. [The event page for ‘How to Write CHI Papers (Third Edition) at CHI 2019’ on Facebook](https://www.facebook.com/events/351277865525364/), please join for live discussions during the conference. 3. [Sign up for free course updates now.](https://view.flodesk.com/pages/6438556c47fd69214ac03f1d?ref=lennartnacke.com) 4. [Brainstorm your 4 questions about your paper here.](https://stormboard.com/invite/768702/family40?ref=lennartnacke.com) 5. [Soon, there’ll be a podcast, too.](https://anchor.fm/how-to-write-chi-papers?ref=lennartnacke.com) 6. [Jess Korte’s course notes](https://docs.google.com/document/d/1wcZxCquSVjWYfWfkXN4R2VF5bSdAViKYEguDLPNAPrQ/edit?ref=lennartnacke.com) ## **CHI 2019 Course Unit 1: Structure** - **11:00 – 11:09** Introduction and Goals - **11:10 – 11:49** Micro Lecture: Structuring your Introduction and Research - **11:50 – 12:20** Tutorial: Dissecting a CHI Paper - **12:20 – 14:00** Lunch Break ## **CHI 2019 Course Unit 2: Abstract and Intro** - **14:00 – 14:10** Recap: Where are we? - **14:11 – 15:20** Exercise: Writing the Abstract and Introduction - **15:20 – 16:00** Coffee Break ## **CHI 2019 Course Unit 3** - **16:00 – 16:29** Revision of CHI Paper Structure - **16:30 – 17:20** Exercise: Tutorial and Exercise: Bullet pointing the full CHI paper ### CHI 2018 Course on How to Write a CHI Paper URL: https://lennartnacke.com/chi-2018-course-on-how-to-write-a-chi-paper/ Last updated: 2024-01-21T07:52:20.000Z The CHI 2018 version of the course is listed below. ![CHI 2018 logo in the top left corner on a background image of the Palais des congrès de Montréal.](https://cdn.magicpages.co/acagamic.mymagic.page/2024/01/chi2018-banner-1024x176-1.png) ## **CHI 2018 Schedule** *This course was taking place at CHI 2018 (Palais des Congrès de Montréal) in Room: 524C on Monday, 23rd of April 2018.* ### **CHI 2018 Course Unit 1** - **11:30-11:40** Introduction and Goals - **11:40-12:10** Micro Lecture: Structuring your Research - **12:10-12:50** Tutorial: Dissecting a CHI Paper - **12:50-14:30** Lunch Break ### **CHI 2018 Course Unit 2** - **14:30-14:40** Recap (Recover from Lunch) - **14:40-15:00** Micro Lecture: The Importance of the Introduction - **15:00-15:50** Exercise: Writing the Abstract and Introduction - **15:50-16:30** Coffee Break ### **CHI 2018 Course Unit 3** - **16:30-16:40** Revision of CHI Paper Structure - **16:40-17:50** Tutorial and Exercise: Bullet pointing the CHI paper --- ## **Tutorial: Dissecting a CHI Paper** Structured discussion of the following paper: - Jason Wuertz, Sultan A. Alharthi, William A. Hamilton, Scott Bateman, Carl Gutwin, Anthony Tang, Zachary Toups, and Jessica Hammer. 2018\. [A Design Framework for Awareness Cues in Distributed Multiplayer Games](http://ecologylab.net/research/publications/GameAwarenessCHI2018.pdf?ref=lennartnacke.com). In *Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems* (CHI ’18). ACM, New York, NY, USA, Paper 243, 14 pages. DOI: [https://doi.org/10.1145/3173574.3173817](https://doi.org/10.1145/3173574.3173817?ref=lennartnacke.com) - *ALT:* Katja Rogers, Giovanni Ribeiro, Rina R. Wehbe, Michael Weber, and Lennart E. Nacke. 2018\. [Vanishing Importance: Studying Immersive Effects of Game Audio Perception on Player Experiences in Virtual Reality](https://doi.org/10.1145/3173574.3173902?ref=lennartnacke.com). In *Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems* (CHI ’18). ACM, New York, NY, USA, Paper 328, 13 pages. DOI: [https://doi.org/10.1145/3173574.3173902](https://doi.org/10.1145/3173574.3173902?ref=lennartnacke.com) (***alternative***) 1. Read the paper(s). 2. Which parts of the paper are excellent? Why do you think they are? 3. What is the structure of the paper? --- ## **Dissecting the Abstract** ### **Lay of the land, explaining why the problem is relevant and matters:** In the physical world, teammates develop **situation awareness** about each other’s location, status, and actions through cues such as gaze direction and ambient noise. To support **situation awareness**, distributed multiplayer games provide **awareness cues**—information that games automatically make available to players to support cooperative gameplay. ### **The actual research problem:** The design of awareness cues can be **extremely complex, impacting how players experience games and work with teammates**. **Despite the importance of awareness cues, designers have little beyond experiential knowledge to guide their design.** ### **How we are addressing the research gap or problem:** In this work, we describe a design framework for awareness cues, providing insight into what information they provide, how they communicate this information, and how design choices can impact play experience. ### **Our contribution to CHI and takeaways:** Our research, based on a grounded theory analysis of current games, is the first to provide a characterization of awareness cues, providing a palette for game designers to improve design practice and a starting point for deeper research into collaborative play. --- ## **Dissecting the Introduction** ### **Lay of the land, explaining why the problem is relevant and matters:** Teams working together in the physical world develop situation awareness \[…\]. Distributed games help players coordinate by providing **awareness cues** —information that systems automatically make available to collaborators to support cooperative actions \[…\], **awareness cues must be designed to provide the right information at the right time**. \[…\] ### **Why the problem is an important one:** Since teammates in distributed games are largely experienced through awareness cues, the **principal challenge for game designers is to create tools that will provide the right information at the right time** \[62\]. **The design tension is to balance this information with ensuring that the game remains challenging, so giving a player omniscience is undesirable**. \[…\] ### **How we are solving the problem:** Using a grounded theory approach, we examined 24 games\[…\]. ### **How we structured solving the problem and our paper:** We do this by first articulating the **information made available through awareness cues to teammates**. Second, we describe the **essential design dimensions of awareness cues** and **how they make** teammate **information** **available**. Third, we discuss **potential consequences** for games and play experience when particular design choices are made. ### **Why our research matters:** While **prior work** has considered synchronous verbal communications \[…\], our work focuses on the **understudied tools and techniques** that games use to **support coordination**, which are made available to players **without explicit effort.** ### **Our main contribution to CHI:** Building on previous work in awareness, this work makes **two main contributions**. First, **we provide a palette for game designers and researchers** to identify and **devise new awareness cues** depending on the game experience they want to target. We expect that users of games (players and viewers) influence **how cues should be designed** and also **consider how players adapt their play experience** through cues. Second, we provide a starting point for future research and for **informed design practices around awareness cues** in online games, and in groupware more broadly. --- # **Exercise: Writing the CHI Abstract and Introduction** - Build a brief research plan for a CHI publication (10 minutes) - Problem statement - Indication of your methodology - Anticipated main findings - Anticipated conclusions Now, write your own **Title**, **Abstract**, and **Introduction** for the research plan you have developed (30 minutes). *Use the four questions to guide you through the process of writing a fictional CHI paper about this research topic that you have in mind:* 1. What is the real-world problem that we are trying to solve? 2. Why is it important to solve this problem? 3. What is the solution that we came up with to solve it? 4. How do we know that the solution is a good solution to the problem? Pass around your written paragraphs and discuss them in groups (of 3-4), I will assist. 20 minutes for discussions. --- # **Tutorial and Exercise: Bullet pointing the CHI paper** Use the four questions to guide you through the process of writing a fictional CHI paper about this research topic that you have in mind: 1. What is the real-world problem that we are trying to solve? 2. Why is it important to solve this problem? 3. What is the solution that we came up with to solve it? 4. How do we know that the solution is a good solution to the problem? *Use the same process as many CHI authors:* - Sketch the rough answers to each question into bullet points - Get together a maximum of 15 bullet points among all 4 questions - Start writing out the bullet points into paragraphs - **What contribution do you envision?** - **What research plan do you foresee?** - **Can you expand on your existing work?** - **What results do you need?** If you have time, use the **nine-step editing system**: 1. Read through your text 2. Break it up into points (ideas, thoughts, arguments) 3. Make sure every single point makes sense 4. Delete non-essential or redundant points 5. Make sure each point is unique and distinguished enough 6. Create sections by creating categories for the points 7. Make the sections flow into one another 8. Sort your points into the categories 9. Make it read well by focusing on simple, clear, and elegant language I will come around and assist your writing. ### How to Write CHI Papers Course at CHI 2017 URL: https://lennartnacke.com/how-to-write-chi-papers-course-at-chi-2017/ Last updated: 2024-01-21T07:50:33.000Z CHI 2017 marked the premiere of this course at the CHI conference. It was taught in person and over the course of two units. ## **CHI 2017 Schedule** ### **Writing Unit Schedule** - **9:30-9:35** Intro and Goals - **9:36-10:00** Micro Lecture: Clarity and Structure - **10:01-10:20** Exercise: Structuring CHI Research - **10:21-10:50** Exercise: Writing the Introduction ### **Reviewing Unit Schedule** - **11:30-11:40** Recap - **11:41-12:00** Micro Lecture: On Reviewing for SIGCHI - **12:01-12:20** Exercise: Dissecting a CHI Paper - **12:21-12:50** Exercise: Writing a Helpful Review --- ## **Writing Exercise 1: Structuring Your CHI Research** - Build a brief research plan for a CHI publication (10 minutes) 1. Problem statement 2. Indication of your methodology 3. Anticipated main findings 4. Anticipated conclusions - Present your plans to the group with a brief discussion (10 minutes) of structural flaws or strengths – we will all try to critique the plans ## **Writing Exercise 2: Writing the Introduction** Now, write your own **Title**, **Abstract**, and **Introduction** for the research plan you have developed (30 minutes). *Use the four questions to guide you through the process of writing a fictional CHI paper about this research topic that you have in mind:* 1. What is the real-world problem that we are trying to solve? 2. Why is it important to solve this problem? 3. What is the solution that we came up with to solve it? 4. How do we know that the solution is a good solution to the problem? *Use the same process as many CHI authors:* - Sketch the rough answers to each question into bullet points - Get together a maximum of 15 bullet points among all 4 questions - Start writing out the bullet points into paragraphs - Pass around your written paragraphs and discuss them in groups, I will assist. ## **Reviewing Exercise 1: Dissecting a CHI Paper** *Structured discussion of the following paper:* 📄 Matthew Kay, Tara Kola, Jessica R. Hullman, and Sean A. Munson. 2016\. When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). ACM, New York, NY, USA, 5092-5103\. DOI: [10.1145/2858036.2858558](https://doi.org/10.1145/2858036.2858558?ref=lennartnacke.com) - Read the paper. - Which parts of the paper are excellent? - Why do you think they are excellent? ## **Reviewing Exercise 2: Writing a Helpful Review** Read and annotate one of these papers: - [Contextualizing Intermediated Use in the Developing World: Findings from India & Ghana](https://dl.acm.org/citation.cfm?id=2858036.2858594&ref=lennartnacke.com) - [Revising Learner Misconceptions Without Feedback: Prompting for Reflection on Anomalies](https://dl.acm.org/citation.cfm?id=2858361&ref=lennartnacke.com) - [A Cost-Benefit Study of Text Entry Suggestion Interaction](https://dl.acm.org/citation.cfm?id=2858305&ref=lennartnacke.com) *Write a review with a focus on:* - Reflecting on the contributions - Discussing the weaknesses and limitations in a positive way - Calling out the strengths and utility of the work - Discuss! ### How to Write CHI PLAY Papers 2016 URL: https://lennartnacke.com/how-to-write-chi-play-papers-2016/ Last updated: 2024-01-21T07:49:44.000Z This is the information that was given to participants at the 2016 course on how to write CHI papers. ![](https://cdn.magicpages.co/acagamic.mymagic.page/2024/01/Final-Logo-2016-5-1-252-e1453424611835.png.webp) Please bring some examples from your own recent writing to this course. This can be a thesis abstract, some unpublished papers, or just something you have written recently. ### **Schedule** - **09:00-10:30** Lecture: Introduction to the Course ([Read Interview with Carl Gutwin](https://lennartnacke.com/chi2016-course-interview-with-carl-gutwin/)) - **10:30-11:00** Coffee Break - **11:00-12:30** Exercise: Structure - **12:30-14:00** Lunch - **14:30-16:00** Exercise: Style - **16:00-16:30** Coffee Break - **16:30-17:45** Writing doctors: Bring your own manuscripts and let’s dissect them. # **Introduction to the CHI PLAY Course** [introduction-lecture-slides-nacke-210310043003introduction-lecture-slides-nacke-210310043003.pdf669 KBdownload-circle](https://cdn.magicpages.co/acagamic.mymagic.page/2024/01/introduction-lecture-slides-nacke-210310043003.pdf?ref=lennartnacke.com "Download") Before we begin, please have a read over the following materials: 📄 Susanne Bødker, Kasper Hornbæk, Antti Oulasvirta, and Stuart Reeves. 2016\. Nine questions for HCI researchers in the making. interactions 23, 4 (June 2016), 58-61\. DOI: 10.1145/2949686 📄 Antti Oulasvirta and Kasper Hornbæk. 2016\. HCI Research as Problem-Solving. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). ACM, New York, NY, USA, 4956-4967\. DOI: 10.1145/2858036.2858283 --- ### **Nine Questions** I would also like to reiterate the nine questions for HCI researchers in the making here: 1. If you could address just one problem in 10 years, what would it be? 2. Are you using your unique situation and resources to the fullest? 3. What’s your HCI research genre? 4. In one sentence, what is the contribution of your research? 5. Is your approach right for your research topic? 6. Why is your research interesting? 7. Can you fail in trying to answer the research problem? 8. Will your work open new possibilities of research? 9. Why do you build/prototype? *These questions are really good starting points to give you bearings on your research direction.* I would also like to point you to the results from my questionnaire for CHI researchers about writing. Interestingly in the survey researchers rated the importance of the Introduction and Results section almost equally high (with the results coming out on top) and were not giving as much love to the Discussion section. In my interviews, however, the introduction and discussion were mentioned as important sections. The key ingredients for CHI research papers mentioned in the survey were: --- ### **Content** - Clear framing - Interesting topic - Novelty - Clear contribution - Problem worth solving - Good problem, motivated by the literature - A novel and ambitious solution for the problem (e.g., in terms of system, evaluation, data collection) - Convincing evaluation - Sound methods - Considering all relevant implications ### **Style** - Well structured - Appropriate language - A discussion that allows the solution to be transferred to other problem instances and related to what we knew in advance - Key ingredients are believable answers to a design question that is not obvious or a novel system. - Clear contribution to the field, sufficient proof for valid claims, usage of a scientifically valid methodology (depending on the type of contribution) - A clear description of the (usually applied) research problem - Clearly articulated research question - A clear description of related work - A clear description of a valid method for finding the answer to the question - A clear description of a valid approach to data analysis good discussion with some implications for the design for luck lots of luck in getting a good set of reviewers - Being clear about the intellectual contribution to HCI research itself - A clear contribution - Well-executed user involvement - Making sure you have enough users, not just testing with students - Deep and broad referencing - A well-written abstract - Novelty - Method - Rigour - Clear scope - Acknowledging the subcommittee/audience you write for *Besides asking for key ingredients in CHI papers, I also asked survey respondents for the main piece of advice for aspiring CHI authors:* - Convince your AC and you have a chance. Reviewers don’t matter in the grand scheme of things. If your paper is even close to getting in in November, a strong rebuttal and an advocate on the AC are all you need. - Budget a lot of time. - Picking the most appropriate subcommittee makes a big difference. - Pick a good problem; know the literature; start early; get feedback; discuss with earlier work; be bold. - Don’t consider CHI as the only premier venue – in-depth specialized papers have a better place at the specialized SIGCHI conferences. Don’t focus on style, focus on correctness, scientific validity and on a contribution that you think changes or progresses the field significantly. - Don’t submit 5+ papers a year, make your contribution count. CHI is not an outlet store, it is a scientific conference. (Sometimes, I’m also guilty of doing this) - Iterate. Don’t leave writing until the last minute. - Expect to fail (you can always send it to a journal – seriously). - Make it a good one. - Do not provide variations of the same thing. - Choose and do so honestly a few wise reviewers. ![](https://web.archive.org/web/20220817012301im_/https://unsplash.com/photos/gcsNOsPEXfs/download?force=true&w=1920) Photo by [William Iven](https://web.archive.org/web/20220817012301/https://unsplash.com/@firmbee?utm%5Fsource=unsplash&utm%5Fmedium=referral&utm%5Fcontent=creditCopyText) on Unsplash ## **Structure Exercise** The following exercise was done in class with 30 minutes time. ### **Analyze the Structure of this Abstract** Look at the following abstract and try to understand its structure. I have already outlined the main problem for you, can you find the solution and why it is important and what makes this a contribution to HCI? > Many sports video games contain elements such as running or throwing that are based on real-world physical activities, but the translation of these activities to game controllers means that the original physicality is lost. This results in games where players have limited opportunity to improve their physical skills, where there is little differentiation in people’s physical abilities, and where skills do not change over the course of a game. To explore ways of adding these elements back into sports games, we developed two games with small-scale physical controls for running and throwing — one game was a simple running race, and one was a team-based handball-style game called Jelly Polo. In two studies (three track-and-field tournaments for the running game, and a four-week league for Jelly Polo), we observed the effects of physical controls on gameplay. Our studies showed that the physical controls enabled substantial individual differences in running and passing skill, allowed people to increase their expertise over time, and led to fatigue-based changes in performance during a game. Physical controls increased the games’ challenge, complexity, and unpredictability, and dramatically improved player interest, expressiveness, and enjoyment. Our work shows that game designers should consider the idea of “exertion in the small” as a way to improve play experience in games based on physical activities. 📄 Mike Sheinin and Carl Gutwin. 2014\. Exertion in the small: improving differentiation and expressiveness in sports games with physical controls. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14). ACM, New York, NY, USA, 1845-1854\. DOI: [10.1145/2556288.2557385](http://dx.doi.org/10.1145/2556288.25573?ref=lennartnacke.com) ### **Analyze the Structure of this Introduction** Look at the Introduction [from the same paper](http://dx.doi.org/10.1145/2556288.25573?ref=lennartnacke.com) and try to understand its structure. I have already outlined the main problem for you, again can you find the solution and why it is important and what makes this a contribution to HCI? What parts of the introduction are confusing or misleading? #### **Introduction** > Many video games contain elements that are based on physical activities (e.g., running, throwing, jumping, or kicking), and sports games in particular are strongly based on physicality. Sports games have many complexities, and players can build up considerable amounts of expertise in them – but the nature of this expertise is usually very unlike that of the original sport. This is because games must translate a real-world physical activity to an action that can be carried out with an Xbox-style game controller, and in the translation, the physicality of the original activity is lost.For example, one main element of many real-world games is bodily movement such as running. Running in many sports video games is translated to a rate-controlled joystick action (i.e., press the stick to move the on-screen character) or a fixed-rate keyboard action (i.e., press and hold the WASD keys to move). This changes the activity of running from a repeated large-muscle action with substantial physical demands, to small fixed movements of the fingers and hand on a controller. In addition, this translation changes a complex multi-degree-of-freedom action to a simpler rate-controlled action where velocity is a function of the system rather than a function of player effort. A second example involves throwing skill. In the real world, passing is a precision skill that requires substantial practice, but in many sports games, passing is translated into an action that is at least partially controlled by the computer (e.g., direction and distance are automatically set or adjusted so that a pass will go to a teammate). These kinds of translations between physical activities and controller actions are present in almost all sports games (and many other avatar-based games as well). However, this approach presents three drawbacks for sports games: There is limited opportunity for expertise development. Although there are many ways for a player to increase their skill in a sports game, there is little opportunity for improvement of basic actions like running or throwing. In contrast, improvement in basic physical skills is a foundation for expertise in real-world sports.There is little differentiation between players in terms of basic actions like running and passing, and thus little opportunity to use these differences in the game. In contrast, success in real-world sports often revolves around individual differences (e.g., taking advantage of a mismatch with an opponent’s physical capabilities, or setting up a team to capitalize on individual strengths and minimize individual weaknesses).Third, the artificial simplicity of controller actions means that there is no change in a player’s physical capabilities over the course of a game. In real-world sports, effort-based factors such as fatigue clearly set apart better players and teams from weaker ones – e.g., many games are won and lost when the team with more endurance takes advantage of the other team’s fatigue. Overall, these drawbacks reduce the richness and realism of sports video games. Although some games can add other types of richness (e.g., difficulty levels for computer players, minigames such as fighting in a hockey game, or ‘manager modes’), the core play experience of a sports game is often limited by these problems. In this paper, we investigate the idea of adding physicality back into controller-based movement in order to add expressiveness and player differentiation back into sports video games. We maintained the basic play environment, with standard controllers and settings, but added two kinds of physical control: impulse-based movement, where each physical action on the controller only moves the character a small amount (similar to taking a single step); and high-precision throwing, where control input has a detailed relationship to the direction and distance of a throw. To test the idea of physical controls in sports video games, we developed two games and ran two studies. The first game was a track-and-field running game called Track and Field Racing (TaFR), where two players race each other in a simulated 100m, 200m, or 400m race. Players controlled the running movement by alternately pressing two keys on the keyboard, as fast as possible, with their first and second fingers. We ran three track meets with TaFR to see whether the physical controls led to individual differences and to performance changes over time. Our study clearly showed both of these effects – physical controls appeared to greatly increase the complexity and unpredictability of the game. Our second game – called Jelly Polo – was a three-on-three top-down ball game loosely based on European Handball. Discrete movement in Jelly Polo involves repeatedly tapping the left joystick of a game controller in the desired direction; precise throwing involves pushing the right joystick in a particular direction and by a particular amount. To explore the effects of these controls on gameplay, we ran a four-week ‘Jelly Polo league’ with four persistent teams of three people. We found substantial evidence that the physical controls changed all three of the issues identified above: first, people’s basic skills in discrete running and precise throwing increased over the four weeks; second, there was considerable individual skill difference across the twelve players, and both people and teams had to adjust to accommodate these differences; and third, fatigue played a major role in the gameplay – it directly affected player speed, and led to novel team strategies and exploitation of fatigue-based mismatches. Our work builds on existing foundations of exertion-based interfaces (e.g., \[12, 14, 17\]), but looks specifically at ‘exertion in the small,’ with physical actions of hands and fingers. We make four novel contributions: we show that small-scale effort-based control can add considerable complexity and unpredictability to simple actions such as running and movement; we show that providing more expressive input provides increased opportunities for expertise (and differentiation in expertise) for both movement and throwing actions; we show that physical controls can add interest, challenge, and enjoyment to simple team games; we show that fatigue can be a valuable design principle that can dramatically change the way that gameplay evolves, and the ways that teams develop strategy. Overall, our work suggests that designers of games based on physical actions (and sports games in particular), should consider ‘exertion in the small’ as an idea that can improve player experience and player satisfaction. 📄 Mike Sheinin and Carl Gutwin. 2014\. Exertion in the small: improving differentiation and expressiveness in sports games with physical controls. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14). ACM, New York, NY, USA, 1845-1854\. DOI: [10.1145/2556288.2557385](http://dx.doi.org/10.1145/2556288.25573?ref=lennartnacke.com) --- ## Now, write your own Title, Abstract, and Introduction First, brainstorm a CHI paper idea with the group (10 minutes). Use Carl’s four questions to guide you through the process of writing a fictional CHI paper about this research topic that you have in mind: 1. What is the real-world problem that we are trying to solve? 2. Why is it important to solve this problem? 3. What is the solution that we came up with to solve it? 4. How do we know that the solution is a good solution to the problem? Use the same process as many CHI authors: - Sketch the rough answers to each question into bullet points. - Get together a maximum of 15 bullet points among all questions. - Start writing out the bullet points into paragraphs. - Get together enough for at least 1 page in SIGCHI proceedings format - Present your introduction to the course. ## Style Exercise There is much to be learned about writing with style and elegance. An excellent system to follow is Shani Raja’s 9-Step Editing System when trying to trim down your writing. Have a look at the video below. His nine steps are: 1. Quick read-through 2. Separate the points 3. Make it make sense 4. Delete the nonessential 5. Make each point unique 6. Create sections 7. Make the sections flow 8. Put points into sections 9. Make it read well (simple, clear, and elegant) What you should definitely avoid in writing your CHI paper are the following things: - Minimize passive voice. Yes, CHI likes when you say “we” and actively describe what your research team has done. - Find a rhythm to your writing. Avoid sentences that are too long. You should try and alternate your sentence length in your writing. - Reduce jargon. Yes, HCI is an interdisciplinary field, but there is still lots of jargon that is used across disciplines and sticks out but not using cutting-edge vocabulary. - Reduce words that do not contribute to the meaning of a sentence. Be clear and be brief in what you say. Delete adverbs whenever possible. - Avoid [Zombie nouns](https://andynaselli.com/zombie-nouns-and-verbs-why-nominalizations-and-passives-may-be-killing-your-writing?ref=lennartnacke.com). --- Next, I would recommend reading the essay: [Why Academics’ Writing Stinks](https://www.chronicle.com/article/why-academics-stink-at-writing/?ref=lennartnacke.com) by Steven Pinker. He points to a couple of examples from the [bad writing contest](http://www.denisdutton.com/bad%5Fwriting.htm?ref=lennartnacke.com), let’s examine some examples below. ### **Identify Bad Writing** Consider the following sentence and try to understand its meaning: > “The move from a structuralist account in which capital is understood to structure social relations in relatively homologous ways to a view of hegemony in which power relations are subject to repetition, convergence, and rearticulation brought the question of temporality into the thinking of structure, and marked a shift from a form of Althusserian theory that takes structural totalities as theoretical objects to one in which the insights into the contingent possibility of structure inaugurate a renewed conception of hegemony as bound up with the contingent sites and strategies of the rearticulation of power.”Butler, J. (1997). Further reflections on conversations of our time. *Diacritics*, *27*(1), 13-15. ### **A Gary Provost Quote on Good Writing** > “This sentence has five words. Here are five more words. Five-word sentences are fine. But several together become monotonous. Listen to what is happening. The writing is getting boring. The sound of it drones. It’s like a stuck record. The ear demands some variety. Now listen. I vary the sentence length, I create music. Music. The writing sings. It has a pleasant rhythm, a lilt, a harmony. I use short sentences. And I use sentences of medium length. And sometimes, when I am certain the reader is rested, I will engage them with a sentence of considerable length, a sentence that burns with energy and builds with all the impetus of a crescendo, the roll of the drums. The crash of the cymbals – sounds that say listen to this, it is important.”Gary Provost [Buy the course](https://www.chicourse.com/courses/how-to-write-better-research-papers?ref=lennartnacke.com) ### Interview with Carl Gutwin URL: https://lennartnacke.com/chi2016-course-interview-with-carl-gutwin/ Last updated: 2024-06-10T16:02:27.000Z The Big Idea (Carl Gutwin) 0:00 /23.69305 1× I have had the pleasure of interviewing Professor Carl Gutwin from the University of Saskatchewan. Carl was CHI papers chair in the past and had incredible experience writing for SIGCHI conferences. ## **About Branching Into a New Research Area** After he transitioned from graduate school to faculty, one of the first mistakes was when he was trying to shift into a new research area (after grad school). He underestimated the amount of work necessary to get up to speed with related literature in a new field. Thus, it was tough for him to outline how his contribution was new, different, and better from what was out there because he did not have the depth of knowledge that he had on other topics in which he had been immersed for four to five years. His advice is that if you want to do a CHI paper in an area you are not intimately familiar with, you better do your homework. Related work has much to do with whether you are contributing to the field. Reviewers, who are likely all experts in that territory, will notice right away if you missed that vital work in the field. To them, this might determine whether or not you are making a new contribution (i.e., they will find a reference that has already done what you are doing, and they will point that out to you). Doing a thorough literature search pays dividends for you with reviewers later if you are not familiar with a field. Leave some slack in your schedule so that you can avoid these mistakes. ## **Carl’s Style Advice** Carl says it is hard to write elegantly. He compares an *elegant style* and *good writing* to usability principles, where we try to get it right 80% of the time (referring to the Pareto principle or 80/20 rule). Professor Gutwin advises students to focus first on their paper’s workman-like aspects (i.e., all the things that reviewers will check off their lists). He means that it is of utmost importance first to get the message of a paper across. Writers have to convey what problem they are solving and why it is a crucial problem for the CHI community. While he admires eloquent style, he admits that style is not something that reviewers look for in papers. He has been in lots of committee meetings, where papers were accepted even though reviewers said: “The authors wrote this terribly, and I could barely understand what was going on, but I love the ideas behind this.” With that in mind, what typically gets through is good research rather than good writing (i.e., substance over style; as many would argue, it should be for a scientific conference). However, I share Carl’s opinion that it is still critically important to become a good writer (and a stylist). Carl says he wants his papers to be judged by a committee on what he did (i.e., the research). The only way to accurately convey to a reviewer what you did, is by writing clear sentences. The best style for Carl is thus the style that accurately reflects the work with utmost clarity. So you would be asking yourself: What is the contribution of my work? And why is it a contribution in clear and unambiguous terms? Then, the reviewer will be judging you for what you did rather than for what they think you did (because you were not clear in how you stated it). Writing clear sentences is not trivial. It takes much clear thought and revisions to do this. Carl thinks that students can do this in two ways: 1. Do not start with sentences at all when you start writing your paper. Start with an outline instead. Work out the argument first. What is the progression of ideas that you are going to convey to the reader? 2. Fall in love with your favourite style guide (e.g., Strunk and White, Style, Writing Tools). Little things like grammatical errors, poor use of parallel structure, and sentences that do not agree in number or sense from one end of the sentence to the other. These obstacles provide little bumps along the way of the review where a reviewer now has to work harder to find out what you did or why your contribution is essential. Any time you make a reviewer work hard, you lose ground, and you lose marks toward their final impression (and score) on your paper. Carl says that if a reviewer is not sure, they will play it safe and mark you down at least half a point. Worse, if they cannot figure it out, you will not be able to convince them of the beautiful work you have done. Therefore clarity is the most critical style. ## **Focus on Introduction and Discussion** Carl is personally focused on getting the Introduction and Discussion sections of a paper right. He refers to high school essay classes, where he has learned the hourglass structure of a paper. We start broad and then we move into specifics of what we have done and then we broaden again back out at the end (focusing on three questions: What did we just learn? Why is it important? What should we do now?). The Introduction and discussion are the points in that hourglass where you are both widest in focus and then having to make the biggest change. In the introduction, you have to frame the problem, scope the research and set the stage for your reader. These are the most exciting parts of paper writing for Carl; these bits are critically important for him. He advises his students that they are always telling a story. So, he likes to call these exercises story conferences rather than paper writing sessions. All good stories have a conflict and some decision that has to happen, some fight that has to occur (see also [the arch plot structure](http://ingridsundberg.com/2013/06/05/what-is-arch-plot-and-classic-design/?ref=lennartnacke.com)). Then, it all has to have a meaning in the overall context of the story. So, think about what your story is and what is exciting and different about your research. Set your paper up as a movie or a story with a protagonist and an antagonist (the classic conflict situation in most stories). What is the resolution of your conflict? Thinking about this is never a bad idea even if some papers do not lend themselves to this type of narrative structure. Four Questions to Ask for Your Introduction (Carl Gutwin) 0:00 /12.3037 1× Another important part of writing an introduction for Carl is to guide yourself by answering four important questions: 1. What is the real-world problem that we are trying to solve? 2. Why is it important to solve this problem? 3. What is the solution that we came up with to solve it? 4. How do we know that the solution is a good solution to the problem? These questions map nicely onto different kinds of CHI research. In the HCI community, we always try to solve real-world problems that are important in some contexts because they cause lots of pain or money when they are not resolved. The evaluations that we usually carry out should show some progress on which critical problems we are working on. It is too often that Carl reads papers that are not clear about the problem they are trying to solve or that fail to argue why the problem is worth solving. You always have to convince the reader (or the reviewer) that the problem you have addressed is valuable to someone (i.e., would somebody pay you money to solve it?). Carl also thinks that reviewers make up their minds about a CHI paper after the first page. This page is where you set your first impressions, and this is where you need to convince them that your problem has value (at the minimum that it is worth their time to read about). For reviewers, Carl mentions specifically that you always have to keep in mind their state (they are usually stressed professors doing many reviews after all their other day work is done). This is probably the 12th paper they are reading that day, and they have probably stopped paying attention by the time they get to the middle of your manuscript and if you do not give them everything they need to argue strongly for the article on the first page, it will be hard to convince them of that later. --- ## **About Writing the Abstract** Your reviewers always read the abstract first, so this is the first impression you can make on readers (and reviewers), and it has to be a good one. Everyone will likely keep their attention long enough for the entire abstract, so Carl uses the same model (the four questions mentioned above) to guide the reader through the abstract. Carl even thinks a little further than the abstract and mentions how important the title is for a CHI paper (Jofish Kaye researched CHI paper titles). Carl says: “The title is your first opportunity to summarise why this paper should be accepted.” The abstract is then the slightly longer version of this opportunity to convince the reviewer to accept this paper. The introduction is an even longer version and then the paper is the full-length version. It is an incremental increase in detail of your sales pitch for the paper. You need to look at the sections of your paper as opportunities for you to get something done. If you have developed something new and exciting, this is your chance to tell the world about it. Carl follows his four-question model precisely in his abstracts. He starts by saying: “It is a problem that…” and then “this is a critically important problem to solve, because…” and then “we have solved this important problem with…” and then “to show that this was a good solution to the problem of X, we ran a study that showed there was a 50% improvement in Y…,” so “in conclusion we have made important progress on an important problem that is going to make the world a better place.” The abstract is often neglected (and sometimes in worst cases simply copied as a paragraph from the introduction) but it has to be thought of as a nugget of information, a summary of the research project and an opportunity to convince people that what you have done is good work. The abstract also always includes the takeaway for the paper (“You need to hold the reader by the hand with your punchline and sometimes need to hit them over the head with it”). Carl puts the most important results, not just in his abstract, but even thinks they should make it into the title of the paper. So instead of writing: “An essay on selection performance on touch tablets,” write “*Improving* selection performance on touch tablets with \[name of your method\],” for example. Use the abstract as an opportunity to tell the whole story. ## **About the Structure of a CHI Paper** The structure of a paper is an assistance to the reader. If you can depend on a particular structure, you often have to do less work to integrate the words on a page to give meaning to you as an observer or reader of the work. So, the structure of a paper into Introduction, Related Work, Main Section, Results, and Discussion helps the reader to make sense of the manuscript. Carl would consider himself a traditionalist that adheres to the regular structure of a CHI paper because he is sensitive to the way that readers (and CHI reviewers) are expecting the paper to be structured. He believes that structure is there for a reason (“We layout buildings in a certain way so that they are easy to get around”). Section headings and other structure elements make it easy for the tired reader to find an easy way through the paper (same with the placement of the results). He quite likes different styles of articles (e.g., essay styles), but thinks that making changes to the regular CHI paper structures would increase the risk of rejection. ## **About his Favourite Paper** He always gives the example of “Edit wear and read wear” (Hill et al., 1992). What he remembers about the paper was a life-changing experience for him and not because of the writing style of the paper, but because of the concept that was introduced (“By graphically depicting the history of author and reader interactions with documents, these applications offer otherwise unavailable information to guide work.”). They introduced a simple concept that had a wide meaning across many application areas. The idea is that you store interaction history and then visualize that interaction history. For example, a scroll bar could keep track of where people have been. The paper clearly gets the excitement and innovation of the concepts across. Not all ideas are as revolutionary as this, but the clarity that the concept was described with is possible to achieve in our own work. 📄 William C. Hill, James D. Hollan, Dave Wroblewski, and Tim McCandless. 1992\. Edit wear and read wear. In **Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (CHI ’92), Penny Bauersfeld, John Bennett, and Gene Lynch (Eds.). ACM, New York, NY, USA, 3-9\. DOI: 10.1145/142750.14275 ## **About Best Papers** Carl does not think that there is a strong correlation between excellent writing and winning best-paper awards. There are lots of criteria used for judging best papers and a revolutionary contribution might be written up poorly, but will still be strong enough to get accepted. Those discussions happen at committee meetings all the time that people are considering putting something forward that has merit but might not be packaged nicely. On the flipside, best papers are rarely given to papers because of their excellent writing. If the results are incremental, then even great writing cannot hide the fact that the contribution is not too strong to the field (or at least so is the assumption). Carl did admit that he would have to go back and read the best papers again to see if there are those rare cases, where excellent writing got them the award nomination. “Science does not really move forward on style,” Carl said (and I sadly agree, but I remain of the opinion that great style should not be an afterthought to excellent science). “The quality of the writing is there to convey the science; it’s a vehicle for the ideas.” --- ## **About Topics That Are Attractive to CHI** We all wish we had the handbook of attractive topics for CHI, but we do not. But Carl still has an anecdote to share about a paper. I am assuming it was Luis von Ahn et al.’s image-labelling paper ([PDF](https://www.cs.cmu.edu/~biglou/ESP.pdf?ref=lennartnacke.com)) about the ESP game. Carl was blown away by the talk and the concept of the paper. The idea of the paper was so simple and compelling that Carl (and other people he talked to later) all thought that they could have come up with that already (practically the concept was already in their heads – applying a matchmaking word game to a simple real-world problem, but they had not actually written it down). At the time when it was published, it was considered an unbelievably cool solution to a real problem. Being ahead of the curve (as Luis von Ahn was, who is not short of great ideas and went on to win lots of awards and start Duolingo) is a definite advantage to writing papers. The important takeaway from Carl’s experience is that it is not important to chase whatever the next big thing might be but to just do something that is of real interest to you (I share Carl’s sentiment because I had a similar experience when I wrote early gamification papers together with colleagues because it was simply something that we were interested in doing \[applying games to things that are not game\] but neither of us was expecting the field to blow up as big as it did later) and then see if public interest follows. Carl believes in interest-driven research, where you are trying to solve the puzzle in front of you with the tools that you can think of and sometimes you come up with a brilliant solution. The papers that Carl likes best are the ones that are investigating a research problem that he can relate to or frustration that he has experienced himself. 📄 Luis von Ahn and Laura Dabbish. 2004\. Labeling images with a computer game. In **Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (CHI ’04). ACM, New York, NY, USA, 319-326\. DOI: [10.1145/985692.985733](https://dl.acm.org/doi/10.1145/985692.985733?ref=lennartnacke.com) ## **About Good Solutions (and Good Methods)** Carl believes that you do not always need to do one particular evaluation of results, but that you need to convince your readers that the thing that you have done, has meaning and value in the domain that you are working. The evidence you provide in your research has to really outline why your solution fixes a problem. The solution most often offered is a user study, an experiment, or statistical analysis that makes such an argument but it does not have to be. Any evidence-based approach can be compelling in convincing somebody that your solution works, but this is the main point you have to be making with your research results. Learning mixed methods and [statistics](http://statistics-help-for-students.com/?ref=lennartnacke.com) (big data and ANOVAs) is still an important first step for students to take that want to publish at CHI because those approaches are simply more common (and there is a danger in doing statistics incorrectly). Carl really appreciates seeing qualitative methods inside quantitative research projects and believes that CHI, in general, is a really good forum for mixed methods (see also: this interesting [paper about sample sizes at CHI](https://dl.acm.org/citation.cfm?id=2858498&ref=lennartnacke.com)). This is of particular importance when trying to answer research questions about why a statistically significant difference is there (your statistics only tell you *that* it is there, but not necessarily *why*). The meaning of this difference can be explained using mixed methods more easily. This also provides “ample grist for your discussion mill” as Carl says, because it allows discussing strategies that people took to mitigate a problem (caused by a difference). You should always be happy when you learn a new method and get to apply it to a paper, which is often done via collaboration with somebody familiar with different methods than yourself (referring to a [mixed models approach he recently tried](https://www.stat.cmu.edu/~hseltman/309/Book/chapter15.pdf?ref=lennartnacke.com)). However, he also warns that there is a risk of using completely new methods within CHI because some reviewers might simply be unfamiliar with the work. Whenever a reviewer feels like they cannot evaluate the correctness of the method of a paper, they may sometimes err on the side of caution and give a lower score. We also talked about the validity of new statistical methods, some approaches, [which have been published at CHI](https://dl.acm.org/doi/10.1145/2207676.2208557?ref=lennartnacke.com) ([even a SIG](https://dl.acm.org/citation.cfm?id=2886442&ref=lennartnacke.com)). In the case, where a method becomes common at CHI and you can signal to the reviewers the new method, it is easier to integrate it and work off of the new approach. Whenever you are breaking conventions, whether it be trying new analyses or a new structure for your paper, you always have to have a good reason to deviate from the traditional approach and as long as you can make this argument to your reviewer, it can be interesting to try out new things (in small increments) at CHI. ## **From the Initial Idea to the Final Paper** I asked Carl about his process from having an idea towards the final paper. He said that he has two models. The first one follows the approach from his colleague [Andy Cockburn](https://www.csse.canterbury.ac.nz/andrew.cockburn/?ref=lennartnacke.com), who believes you should write the paper before you write the paper. This assumes that you have pondered about the feasibility of a CHI idea (often at the conference or directly after) and do consider it feasible. Andy suggests writing the argumentation for your study into the introduction, considering what experiments you would run and how your results would look like and what takeaways you could get from them. So, you end up with a fiction piece, a paper about what your paper/study could be without having actually run any experiments. Carl is surprised how often we get to the point—when we grow our research organically—(often right before the deadline) where we wonder about having done something differently in our study and then we cannot go back and fix it. By writing the paper before you do your research, you protect yourself from two things: (a) missing some of the things that are critical when presenting CHI work and (b) it allows you to think about how much you have to do before you get a contribution (i.e., it helps you determine your scope). This approach essentially allows you to assess the risks first before you do your work. His other model is interest-driven research, where he tinkers with things that he is just interested in. However, Carl also mentions that this is a luxury that comes with having tenure and that younger faculty and students might want to be more strategic about what they would want to end up in their CHI papers. ## **About his Workflow and Tools** Carl begins a paper by being focused on its story, argument, and introduction. Once he has some idea about what the project is, he thinks about the argument that he will make in the introduction (again, beginning with the problem and asking why it is important, the solution and how to say that it is a good solution). He starts by drafting these as bullet points on paper (a great activity for meetings that you have to attend but might not be interested in). This can be done wherever, in an email message, on paper, in a notebook, wherever you can stick your thoughts (and later find them again). There is no formatting, no structure, no sentences, just bullet points to see how the arguments flow. Carl recommends doing a maximum of 15 points that address his four questions. Once he has this outline written, he moves into Microsoft Word (in Windows) and does not return to paper or other editors after that. He then outlines various sections of the paper. The sections that he finds difficult to write “from whole cloth” are the related work section (which he iterates again that it needs a strong outline to become a reasonable part of the story of a paper) and the discussion section because one needs to find interesting topics or headings for it. He does all of this in Word and once he starts writing things into actual paragraphs, he likes to see what they look like in the [SIGCHI Proceedings format](https://chi2022.acm.org/for-authors/presenting/papers/chi-publication-formats/?ref=lennartnacke.com), because sometimes paragraphs tend to look long in the format and sometimes they look short and the format helps to keep the writing consistent for him. He likes to write on a laptop so that he can move around while he is writing. He sketches it out and then revises often. In sections that are similar to ones that he has written before (like Methods or Results) in one of his papers, he goes back to his papers and follows the model that he has successfully used before, because sometimes you forget important aspects and returning to a camera-ready paper ensures that you are using a standard that is accepted (and all the necessary information is present). So, on a paragraph-by-paragraph basis, he can make sure to write everything correctly. He revises a lot (usually around 50 different versions when a paper is written collaboratively) and saves each version as a separate file (admitting that he really could be using version control software or Dropbox’s revision system) because he likes to be able to go back to previous versions if he needs to. He also (shamefully?) admitted that he is not using a bibliographic manager (which can drive his collaborators crazy). He writes everything out with square brackets with Xs in them like \[X\] wherever he needs a reference and then adds them in last by hand. However, with CHI not having a limit for references anymore, there might be a need for him to switch over to a database-driven reference manager in the future. When he collaborates, he does it mainly via email and sends around the most recent version of the paper and as long as the “lock” on the file is communicated to everyone (i.e., someone decides to work on a paper for a certain amount of time), it has worked well for him (even when sections are merged later). He does not use Google Docs or other real-time collaboration tools. Once the paper reaches a medium draft stage, he likes to print it out on paper and read it over (where he is also able to mark it up and highlight sections). He finds it easier to read over the flow from section to section and check the style, mark spelling errors and problem spaces quickly. It is also a nice break from endlessly staring at a monitor. ## **About his Writing Environment** Carl writes his CHI papers mostly at home because writing at the office can be distracting (especially when all the colleagues and grad students are writing CHI papers as well). You learn lots of new and interesting things happening in other paper but cannot focus on your own paper. He likes to write in a space where the distractions are reduced to a minimum. Writing often takes momentum to really start (a particular level of activation) and every time something else happens to distract you, you lose that writing momentum. Whenever he sits down, he tells himself to write at least one sentence. The rationale goes that when you make it past one sentence, it can be easy to write another one and once that is happening, you are gaining momentum to write an entire section and continue to write your paper. This trick makes getting started much easier and helps you gain writing momentum. ## **About his own Favourite Papers (That he has Written)** Both of his favourite papers are [CSCW](https://cscw.acm.org/?ref=lennartnacke.com) papers. One is one from 1998 and one from 2012\. The first one came out of his Ph.D. thesis but was more speculative and argued that things that are good for individuals are often bad for groups. He likes the paper because it is an exploration of an idea rather than just a regular invention study. The other one was a project that happened while I was doing my postdoc in Saskatchewan and discusses people that are just playing games and do not want to socialize in the game. This had implications for the community and sociability perspectives of game communities. Carl liked it because it conveyed an interesting idea well and we dug into some underlying sociological theories ([from Georg Simmel](https://www.jstor.org/stable/2771136?ref=lennartnacke.com)) that Carl found really fascinating. It also won a best-paper award at the conference. The references for both papers are below: 📄 Carl Gutwin and Saul Greenberg. 1998\. Design for individuals, design for groups: tradeoffs between power and workspace awareness. In **Proceedings of the 1998 ACM conference on Computer supported cooperative work* (CSCW ’98). ACM, New York, NY, USA, 207-216\. DOI: [10.1145/289444.289495](https://dl.acm.org/doi/10.1145/289444.289495?ref=lennartnacke.com) 📄 Gregor McEwan, Carl Gutwin, Regan L. Mandryk, and Lennart Nacke. 2012\. “I’m just here to play games”: social dynamics and sociality in an online game site. In **Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work* (CSCW ’12). ACM, New York, NY, USA, 549-558\. DOI: [10.1145/2145204.2145289](https://dl.acm.org/doi/10.1145/2145204.2145289?ref=lennartnacke.com) ## **About Tables and Figures** Leaving tables aside as an opportunity to present many numbers outside of the regular text, Carl does care about the figures in a paper and whether they are clear. Do they show the differences that you want to show? Whatever you would like your reader to recognize immediately, you would put into the figures. Again referring to the tired reviewer, the visual aids are important to them and they can be a tool for storytelling and for delivering your message as well (and reinforce your story). ## **His Main Advice** What Carl was amazed at when he became a Professor, was how important writing was. He calls it the “coin of our realm.” And states that “everything that we do is going to be based on what we write.” Before that, he always thought it was all going to be about research and building systems, running studies, and proper methods. “Any idea that you ever get across to anybody else in HCI is because of what you’ve written,” he continues. Learning to love writing and improving this craft is really important for successful HCI researchers. So, try to make writing as enjoyable as you can for yourself because your career does in some sense depend on it. It was amazing speaking to Carl and I hope that I will be able to interview him again in the future. All of his advice is really applicable for HCI researchers and should help everyone to improve their writing.