Key Takeaways: Your discussion chapter is the place where you interpret your results and explain why they are important for theory and practice, not where you repeat your findings. It reviews the insights and places them in the context of your overall research. In a discussion section, you go through three essential steps: interpretation, analysis, and explanation.
Table of Contents
Introduction
Writing the discussion chapter is one of the most challenging tasks when working on your thesis. Unlike the results chapter, which presents what you have discovered, the discussion chapter requires you to interpret those findings, connect them to existing research, and explain why they are important for theory and practice. At this stage, it’s no longer just about presenting data, but about contextualising it and making an argument, an undertaking that can be demanding but is crucial for the quality of your work. In this guide, we walk you step by step through a practical framework that will help you create a compelling discussion chapter. And because we couldn’t leave AI out of the discussion, we’re also sharing a mega prompt you can use to simplify the process.
Writing the Discussion Chapter: Key Recommendations and Common Mistakes
Before you start writing your discussion chapter, think of it this way: Your results chapter answers “What did I find?” Your discussion chapter answers, “Why is it important and what do the results mean?”
A strong discussion focuses on the following key elements:
- Brief contextualization of the main results: Not a detailed repetition, but a concise introduction that makes the subsequent interpretation understandable.
- Interpretations: Explanation and contextualization of the results within the field.
- Implications: Significance of the results for theory, research, and practice.
- Limitations: Boundaries of the study and aspects not covered by the results.
- Recommendations (optional, depending on university guidelines): Suggestions for further research or more in-depth analyses.
The discussion is where you must be most original. It’s where you make new knowledge in relation to your data and analysis, while being transparent about what your study reveals and what it doesn’t. In a conversation with our CEO, Dr. Meriton Ceka, we discussed the importance of original and critical thinking in thesis writing, qualities that will define excellence in academic work for years to come. Keep this at the core of your discussion.
What to Avoid:
- Don’t simply rewrite your results as they already appear in the paper. Provide interpretation and analysis, not a simple summary.
- Don’t draw unsupported conclusions. Back up all explanations with evidence from your data or references.
- Don’t introduce new information or sources. Only examine what you have already presented. New data belong to the results section.
- Don’t cherry-pick only the convenient findings. Address unexpected or contradictory results and explain why they occurred.
- Don’t ignore limitations. Acknowledging what your study cannot answer strengthens your credibility; it does not weaken it.
A Step-by-Step Guide with Example
Writing a strong discussion does not mean following a rigid formula. It’s about following a logical sequence that guides your readers from what you found to why it matters. The best discussions follow a clear progression: briefly contextualise your main findings, interpret what they mean, discuss their implications for theory, research, and practice, acknowledge limitations, and suggest next steps (this last point may vary depending on your university context).
Step 1: Briefly contextualise your main findings
Introduce your research problem concisely and place your key results in the appropriate context. This is not about a detailed summary but about framing the main points so that the subsequent interpretation can build coherently on them.
Do’s: | Don’ts: | • Mention only the key results • Make the context clear • Create a logical bridge to the interpretation | • Repeat the entire results section • Introduce new data • Slip into interpretation or evaluation |
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Step 2: Interpret what your results mean
Explain how your results answer your research question and why they are important. Identify patterns, compare them to expectations, and connect them with existing literature.
Do’s: | Don’ts: | • Clearly identify patterns and relationships • Reference the research literature • Explain surprising or contradictory results | • Simply restate the results • Make unsupported speculations • Only repeat what is already stated in theory |
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Step 3: Discuss the Implications
Show how your results fit the broader conversation. Do they reinforce, complicate, or challenge existing theories? Make your contribution clear.
Do’s: | Don’ts: | • Make your work’s contribution visible • Highlight relevance for theory, research, and practice • Connect to overarching questions | • Make claims without literature support • Overinterpret results • Lose the reader with too many side arguments |
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Step 4: Acknowledge Limitations
Be transparent about what your study can and can’t tell us. Explain how limitations affect your findings and interpretation.
Do’s: | Don’ts: | • Clearly and openly state limitations • Explain how they could have affected your results • Demonstrate methodical reflection | • Hide or downplay limitations • Apologize excessively • Undermine the entire study |
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Step 5: Provide recommendations (optional, depending on university context)
Give concrete suggestions for future research. Be specific and avoid vague statements like “further research is needed.”
Do’s: | Don’ts: | • Formulate clear, realistic recommendations • Show how future studies could build on your work • Relate recommendations to your results | • Use generic phrases without added value • Make recommendations unrelated to your study • Suggest actions that are too broad or impractical |
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Ready to see this in action? Below is an illustrative example of a discussion that naturally flows through all five steps (strongly condensed for demonstration purposes):
[Step 1: Contextualising the Results]
The results show that students who use interactive quizzes perform 12% better on final exams than those who only read course materials. This supports the assumption that active learning is more effective than passive reading. At the same time, it challenges the notion that online courses are inherently less effective than in-person classes.
[Step 2: Interpretation]
Furthermore, it becomes clear that students who repeat quizzes multiple times achieve better results than those who complete them only once. Even when accounting for prior performance levels, there is a clear advantage to regular repetition. These patterns suggest that repeated active engagement with learning content enhances retention and retrieval of knowledge.
[Step 3: Implication]
The findings indicate that digital learning environments are particularly effective when they include interactive elements that allow for active repetition. This supports theoretical approaches to active learning and suggests that online formats do not have to be less effective, provided they offer appropriate structures. In practice, this means that schools, universities, and continuing education providers should intentionally design digital learning offerings to incorporate regular repetition, interactive exercises, and immediate feedback to measurably improve learning outcomes.
[Step 4: Limitation]
These results must, however, be interpreted with caution. First, it cannot be ruled out that more motivated or higher-performing students are more likely to repeat quizzes frequently, which could influence the observed performance differences. Second, the analysis is based on self-selection rather than random assignment, so causal claims about the effectiveness of interactive quizzes are limited. Third, it remains unclear whether the observed effect is long-lasting or mainly reflects short-term exam preparation. Overall, the results provide indications of relationships, but the exact causes cannot be fully determined.
[Step 5: Recommendation for Future Research]
To clarify this question more precisely, future research could randomly assign students to different quiz frequencies. This would allow the actual impact of repeated practice on learning outcomes to be determined more reliably.
Using AI to Accelerate Your Discussion Writing
Now that you understand what makes a strong discussion, let's talk about how AI can help you. The key is to use AI as a structured partner: you provide the raw material (your research, results, and ideas), and AI helps you shape potential interpretations from it. Here is a ready-to-use mega prompt (if your university allows the use of AI):
I am currently writing the discussion section for my [thesis/paper] in [your field]. Below, I will give you my introduction, methods, results, and a bullet-point outline of my main discussion ideas. Use this material to develop ideas for my discussion section that reflect my actual findings and my own thinking.
Instructions:
- Read my introduction, methods, and results carefully to understand the study’s context and research question/s.
- Use my bullet points as the foundation for each paragraph. Expand them into clear, coherent academic prose.
- Structure each paragraph with a strong topic sentence linking back to my research question, interpretation of results and connections to existing research or theory.
- Acknowledge limitations only if I've mentioned them in my outline or results.
- Use clear, precise academic language, formal but not overly complex.
Here is my material:
- Paste your introduction
- Paste your methods briefly
- Paste your results
- Paste the 4-6 main points or arguments you want to make in your discussion section
You have options. Claude, ChatGPT, Gemini, and Perplexity all handle this mega prompt effectively, each with different strengths. The prompt gives you a starting point and some ideas to build on, but remember: AI drafts are just drafts. Your job is to reshape paragraphs to match your voice and your findings, and to make sure the discussion truly reflects your analysis.
When using AI in academic writing, make sure your practice aligns with the ethical expectations of your field. Start by checking your institution’s or publisher’s policy, as rules on AI use and disclosure vary widely. If disclosure is required, clearly state that AI assisted in drafting and explain its role. Maintain full intellectual ownership by ensuring that all analysis, interpretation, and final claims reflect your own critical judgment rather than unverified AI output.
Conclusion
Writing a discussion chapter is an act of interpretation, your chance to move beyond reporting data and step into the role of a researcher who reasons, argues, and contributes something new. AI can help you brainstorm ideas, organise thoughts, draft clearer paragraphs, and maintain structure. But it can’t replace the intellectual work that gives your discussion substance. The insight, judgment, and disciplinary understanding that shape a strong discussion must come from you.
Use AI to streamline the mechanics, but stay in control of the analysis. Question the output, verify every claim, and reshape the text to reflect your academic voice. This is where you prove you understand your field well enough to join the conversation, not just summarise it.
Frequently Asked Questions (FAQs)
The length of the discussion chapter depends on the type of document and the academic field, but as a rule of thumb, about 15–25% of the entire paper should be devoted to the discussion. More important than the exact length is the quality of the content; every section should offer clear interpretive value rather than simply adding bulk.
If your results don’t support the hypotheses, that’s completely fine, and often especially valuable. Analyze why the unexpected findings may have occurred, what they mean, and what contribution they still make to the field. Unexpected results often lead to some of the most interesting and insightful discussions.
No. Your discussion should only examine the data and findings you have already presented. New data belongs in the results section. If you discover something new while writing, reorganize your chapters accordingly.
Not recommended. AI can help you organise your thoughts and draft paragraphs, but it cannot replace your intellectual work. Use AI as a tool to streamline the writing process and then revise thoroughly to ensure it reflects your actual analysis and your own voice.
Ask yourself: Do I explain why my results matter? Do I link them to existing research? What do my results mean for theory, research, or practice, and what new contributions do they make? Do I identify limitations? And do I point to possible next steps? If you meet all these points, you’re on the right track.
Disclosure: This article was prepared by human contributors. Generative AI tools were used to support brainstorming, language refinement, and structural editing. All final decisions regarding content, recommendations, and academic insights reflect human judgment and expertise.
References
Dietrichs, I. (2018). Academic writing in a Swiss university context. Pressbooks Hochschule Luzern. https://ebooks.hslu.ch/academicwriting/
Omori, K. (2017). Writing a discussion section. In M. Allen (Ed.), The SAGE encyclopedia of communication research methods (pp. 1862-1864). SAGE Publications. https://doi.org/10.4135/9781483381411.n677
University of Oxford. (2025). Writing discussion sections. Academic Writing Hub. https://lifelong-learning.ox.ac.uk/about/writing-discussion-sections
Dea Elmasllari, MSc.
Dea is a senior researcher passionate about helping students navigate the world of academia. She explores the intersection of AI and scholarly work, offering insights on how technology can enhance writing, research, and learning. As the Head of Partnerships at Delta Lektorat, Dea leads collaborations with universities and student associations to promote excellence in academic writing and innovative approaches to thesis support. Her work focuses on bridging traditional academic rigour with emerging digital tools that empower students and scholars alike.
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