Lesson 353 of 2116
Hypothesis Generation With AI: Divergence Before Convergence
LLMs are remarkable divergent thinkers — they can propose 50 hypotheses in a minute. Your job is the convergent part: testability, novelty, risk.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The move: divergence with AI, convergence with humans
- 2hypothesis generation
- 3divergent thinking
- 4falsifiability
Concept cluster
Terms to connect while reading
Section 1
The move: divergence with AI, convergence with humans
Researchers tend to over-commit to the first hypothesis they think of. AI can break that habit by producing a dozen alternatives you would never have considered. But the AI cannot tell you which hypothesis is publishable, fundable, or true — only which hypotheses sound plausible.
The hypothesis-generation prompt
- 1Force cross-disciplinary framings — novelty often hides at disciplinary borders
- 2Require a distinguishing prediction — otherwise all hypotheses look alike
- 3Require a falsification plan — untestable hypotheses are not science
- 4Rank by novelty, not plausibility — you want options your advisor wouldn't already have
The convergence phase
- Human judgment picks which hypothesis is WORTH testing
- Cost, ethics, feasibility, and novelty are all human calls
- Discuss the top 3 with a trusted colleague before committing
- Document why you rejected the other 12 — reviewers may ask
Key terms in this lesson
The big idea: AI expands the search space; human judgment collapses it. Use each for what it is actually good at.
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