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.
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.
4. Use problem-solution organization to build toward your contribution
The problem-solution strategy follows a logical flow from problem definition through traditional approaches to recent innovations, ending with remaining challenges.
Start by clearly defining the core problems and challenges in your research area. Then discuss traditional approaches and their limitations, followed by recent innovations that have addressed some issues. Conclude by identifying remaining challenges that your work will tackle.
This organization style naturally sets up your research contribution because you’ve systematically worked through what’s been tried, what worked, what didn’t, and what’s still missing. It’s particularly effective for applied research where you’re solving specific practical problems.
The problem-solution flow makes it easy for readers to understand exactly why your research is needed and how it fits into the broader landscape.
One thing to take away here is that the best organizational strategy depends on your specific research area, the nature of existing literature, and what story you need to tell about your contribution. Choose the approach that best supports your positioning argument and helps readers understand why your work matters.
Once, you’ve picked the right organization style for your related work section, I want you to be aware of two more things regarding your citation practices in the related work section.
Get your citation density right for maximum impact
Aim for 1–3 citations per sentence in your related work section, but avoid citation-heavy sentences that sacrifice analysis for name-dropping (I call those things ‘citation bombs’ and I hate them with a passion). Instead of writing in APA style “Medical image analysis is important (Author1, 2023; Author2, 2022; Author3, 2021; Author4, 2020)”—or worse if you use ACM SIGCHI style, something like “image analysis using eye tracking [32, 53, 65, 73, 84, 89, 91, 113]”—write something like “Recent deep learning approaches have shown promise for medical image analysis (Smith, 2023; Jones, 2022), though challenges remain in interpretability and generalization.” You want to integrate citations naturally while advancing your argument. Don’t just take a citation dump and run away from the mess you’ve made. Not cool.
Use academic language patterns that signal expertise
Master the standard phrases that introduce different types of information:
- “However, these studies have not addressed…” for identifying gaps.
- “Recent studies have explored…” for introducing research areas.
- “The authors employed…” for describing methods.
- “Analysis revealed…” for reporting results.
Using conventional language lets readers navigate your section efficiently while establishing your credibility as someone who understands academic discourse in your field. Well done. And if you need more of these standard phrases, I recommend a little peek into the Manchester Academic Phrasebank.
Hope this was useful. If you liked today’s issue, let me know and I’ll actually do series on literature reviews and related work for the next couple of issues.
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 that teaches you the basics of research methods. Or the recordings of our AI research tools webinar and PhD student fast track webinar.
