Research Ideation Generator
Generate research questions, hypotheses, and empirical strategies. Brainstorm ideas systematically with structured prompts that push beyond obvious approaches.
You're staring at a blank page trying to come up with your next research question and everything feels either too incremental or too ambitious. Systematic ideation — structured brainstorming that pushes past obvious approaches — generates research directions you wouldn't reach through unstructured thinking alone.
Who it's for: PhD students developing dissertation proposals and research agendas, principal investigators identifying new research directions for grant proposals, postdocs pivoting into new research areas, research teams running structured brainstorming sessions, interdisciplinary researchers exploring connections between fields
Example
"Generate research directions at the intersection of NLP and healthcare" → Ideation package: 10 research questions ranked by novelty and feasibility, testable hypotheses for the top 5 questions, suggested empirical strategies with data requirements, literature gap analysis showing underexplored areas, and a research agenda framework for the next 2 years
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Research Ideation System
## Command
`/research-ideation [topic]` — Generate research ideas for a topic
## Ideation Framework
### Phase 1: Problem Space Exploration
Before generating ideas, understand the space:
1. What is the core phenomenon?
2. Why does it matter?
3. What's the current state of knowledge?
4. What are the key debates/tensions?
### Phase 2: Research Question Generation
Generate questions across dimensions:
**Descriptive Questions** (What is?)
- What is the prevalence/distribution of X?
- How does X vary across contexts?
- What are the components/dimensions of X?
**Causal Questions** (What causes?)
- Does X cause Y?
- What mediates the X→Y relationship?
- What moderates the X→Y relationship?
**Mechanism Questions** (How?)
- How does X produce Y?
- What is the process by which X operates?
- Why does X work in some contexts but not others?
**Normative Questions** (What should?)
- What is the optimal level of X?
- How should we design X?
- What interventions would improve X?
### Phase 3: Hypothesis Generation
For each research question, generate:
1. **Conventional hypothesis**: What most people would predict
2. **Contrarian hypothesis**: Opposite of conventional
3. **Contingent hypothesis**: "It depends on Z"
4. **Novel hypothesis**: Non-obvious prediction
### Phase 4: Empirical Strategy Brainstorm
For promising hypotheses:
- What data would test this?
- What's the ideal research design?
- What's a feasible alternative design?
- What are the main identification threats?
## Ideation Techniques
### Inversion
What if the opposite of conventional wisdom is true?
### Analogy Transfer
What works in field Y that hasn't been applied to field X?
### Boundary Exploration
What happens at the extremes? What's the smallest unit of analysis?
### Mechanism Deep Dive
Pick any relationship and ask "but how, exactly?"
### Counterfactual Thinking
What would the world look like if X didn't exist?
### Combination
What happens when A and B interact?
## Output Format
```
## Research Ideation: [Topic]
### Problem Space
[Brief characterization]
### Research Questions
1. [Question 1] — [Type: Descriptive/Causal/Mechanism/Normative]
2. [Question 2] — [Type]
3. [Question 3] — [Type]
...
### Most Promising Hypotheses
**Hypothesis 1**: [Statement]
- Rationale: [Why this might be true]
- Test: [How to test it]
**Hypothesis 2**: [Statement]
- Rationale: [Why]
- Test: [How]
### Non-Obvious Ideas
- [Idea that isn't immediately obvious]
- [Contrarian take]
- [Cross-domain insight]
### Gaps Identified
- [What hasn't been studied]
- [What's understudied]
```
## Quality Checks
Good research ideas should be:
- [ ] **Interesting**: Would people care about the answer?
- [ ] **Novel**: Not already answered definitively
- [ ] **Testable**: Can be empirically investigated
- [ ] **Meaningful**: Results would change how we think or act
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What This Does
This playbook helps generate research ideas systematically. Instead of staring at a blank page, use structured prompts to generate research questions, hypotheses, and empirical strategies. Claude pushes beyond obvious approaches to surface non-obvious ideas.
Prerequisites
- Claude Code installed and configured
- A research area or topic to explore
Research Ideation: [Topic]
Problem Space
[Brief characterization]
Research Questions
- [Question 1] — [Type: Descriptive/Causal/Mechanism/Normative]
- [Question 2] — [Type]
- [Question 3] — [Type] ...
Most Promising Hypotheses
Hypothesis 1: [Statement]
- Rationale: [Why this might be true]
- Test: [How to test it]
Hypothesis 2: [Statement]
- Rationale: [Why]
- Test: [How]
Non-Obvious Ideas
- [Idea that isn't immediately obvious]
- [Contrarian take]
- [Cross-domain insight]
Gaps Identified
- [What hasn't been studied]
- [What's understudied]
## Quality Checks
Good research ideas should be:
- [ ] **Interesting**: Would people care about the answer?
- [ ] **Novel**: Not already answered definitively
- [ ] **Testable**: Can be empirically investigated
- [ ] **Meaningful**: Results would change how we think or act
Step-by-Step Setup
Step 1: Add to your research CLAUDE.md
Add the template above to your research folder's CLAUDE.md.
Step 2: Test with a topic
/research-ideation [your research area]
Or simply:
Generate research ideas about [topic]. Use the ideation framework.
Example Usage
User:
"/research-ideation AI adoption in healthcare"
Claude:
Research Ideation: AI Adoption in Healthcare
Problem Space
AI tools (diagnostic, administrative, clinical decision support) are becoming available in healthcare, but adoption is uneven. Key tensions: accuracy vs explainability, efficiency vs job displacement, innovation vs regulation.
Research Questions
Descriptive
- What predicts which hospitals adopt AI early vs late? — Descriptive
- How do clinicians' mental models of AI accuracy compare to actual accuracy? — Descriptive
Causal 3. Does AI diagnostic assistance improve or harm physician decision-making? — Causal 4. Does exposure to AI errors affect future trust calibration? — Causal
Mechanism 5. How do clinicians integrate AI recommendations with their own judgment? — Mechanism 6. Why do some physicians override AI more than others? — Mechanism
Normative 7. What level of AI explanation is optimal for different clinical decisions? — Normative 8. How should liability be allocated when AI contributes to errors? — Normative
Most Promising Hypotheses
Hypothesis 1: AI assistance helps average physicians more than experts
- Rationale: Experts have strong priors; AI provides less marginal information
- Test: Compare diagnostic accuracy improvement across experience levels
Hypothesis 2: AI explanation length has an inverse-U relationship with trust
- Rationale: Too little = black box distrust; too much = cognitive overload
- Test: Experiment varying explanation detail, measuring adoption
Hypothesis 3: AI adoption is higher for "defensive" diagnoses (ruling out) than "offensive" (ruling in)
- Rationale: Liability asymmetry — missing a diagnosis is worse than over-testing
- Test: Compare adoption rates by diagnosis type
Non-Obvious Ideas
- Automation complacency as hidden cost: Physicians may stop developing skills they delegate to AI, creating fragility
- AI as scapegoat: Physicians might adopt AI not for accuracy but for blame-shifting ("the AI suggested it")
- Inverse selection: Hospitals that most need AI (under-resourced, overworked) may be least able to implement it
Gaps Identified
- Long-term effects on physician skill development
- Patient preferences for AI involvement in their care
- How AI errors affect downstream patient trust in medicine broadly
Tips
- Push for contrarian ideas: Explicitly ask "What if the opposite were true?"
- Cross-domain insights: Ask "What does [other field] know about this that healthcare hasn't applied?"
- Quantity first: Generate 10-15 questions before evaluating. Don't self-censor early.
- Test feasibility later: Ideation phase is about possibility, not practicality.
Troubleshooting
Problem: Ideas feel generic/obvious
Solution: Go deeper. For any idea, ask "Yes, but specifically how?" or "What's the second-order effect?"
Problem: Can't generate contrarian ideas
Solution: List the assumptions behind conventional wisdom. What if each assumption were wrong?
Problem: Ideas aren't testable
Solution: Add "How would you test this?" as a required component for each hypothesis. Untestable ideas get cut.