Thesis topics fail at two stages: too broad to finish, or too narrow to matter. Both leave you stuck three months in with no real question to answer. These prompts force calibration before you commit — scope, novelty, methodology, and the conversation with your advisor. For a structured run through brainstorm, narrow, and pressure-test in one sitting, see the thesis topic brainstorm workflow.
Best for
- Undergrad final project
- Masters thesis
- PhD proposal
- Independent research
1. Brainstorm from interests
My interests: {3-5 interests}. My field: {field}. My constraints: {timeline, resources, advisor expertise}. Brainstorm 10 candidate thesis topics. For each output: (a) 1-line topic, (b) 1-line why it's interesting beyond me, (c) 1-line biggest risk. No vague "AI in X" topics.2. Narrow a broad topic
My current topic is too broad: "{topic}". Suggest 5 narrower versions. For each: scope (population + intervention + outcome), research question, why it's defensible at {undergrad / masters / PhD} level. Reject any that still need further narrowing.3. Pressure-test feasibility
My thesis topic: {topic}. My timeline: {months}. My resources: {data access, funding, advisor support, lab equipment}. Pressure-test on three axes: feasibility, scope, novelty. Output the single most likely failure mode and one mitigation.4. Find adjacent gaps
I want to work in {field}. Below are 10 recent paper abstracts. Identify 3 underexplored gaps adjacent to these papers — not the same, not a trivial extension. For each gap: why no one has done it, what new method or dataset would unlock it.
{paste abstracts}5. Defend novelty
My thesis: {topic + central claim}. Articulate the novelty in three buckets: genuinely new, refinement of existing work, overlap with prior work. Then write 3 reviewer concerns I'd face and a 2-line response to each.6. Topic from a personal problem
I noticed this problem in my own life or work: {observation}. Could this be a thesis topic at {level}? If yes, give 2 candidate research angles, each with a concrete method. If no, name the academic gap or methodological reason it fails.7. Reverse from advisor’s interests
My advisor's recent papers (paste abstracts). Suggest 4 candidate thesis topics that align with their interests but extend in new directions. For each: which paper it builds from, what's the new direction, why my advisor would say yes.
{paste}8. Topic-to-proposal scaffolding
My topic: {topic}. Scaffold a 2-page proposal with sections: problem statement (1 paragraph), prior work (3 key references), my contribution (3 bullets), methodology (concrete steps), timeline (by month), expected outcomes (artifact + finding). Mark anything I haven't decided yet.9. Methodology fit check
My research question: {question}. Candidate methodologies: {qualitative interview, quantitative survey, computational modeling, archival analysis, etc.}. For each: what it would let me claim, what it would NOT let me claim, time and resource cost. Recommend one and explain why.10. Topic cross-disciplinary stress test
My topic: {topic} sits between {field A} and {field B}. From {field A}'s perspective: is this a real contribution or a recycled idea? From {field B}'s: same question. Which department's committee would defend this most aggressively?11. Pre-mortem the thesis
Imagine it's 18 months from now and my thesis on {topic} failed. Write 5 plausible failure stories: data didn't exist, advisor moved on, scope collapsed, method didn't work, finding was null. For each: what early signal I'd ignore, what I'd do at month 3 to prevent it.12. Elevator pitch to a non-specialist
Pitch my thesis on {topic} in 60 seconds to a smart non-specialist. 3 sentences: (1) the problem in their language, (2) what I'm doing that's new, (3) what changes if I'm right. No jargon. If a sentence needs a definition, rewrite it.Common mistakes
- Too-broad topics (“AI in healthcare”) with no population, intervention, or outcome
- Methodology that doesn’t fit the question (qualitative claim, quantitative method)
- No conversation with advisor before committing — and no plan B if they say no
- Confusing topic novelty with literature gap (no one studied X often means X isn’t worth studying)
- Optimizing for impressive-sounding instead of finishable