refine-literature-question

Refine Literature Question

Goal

Turn a broad topic into a usable question anchor before searching, reading, or writing the review. The output should be a one-sentence research question that forces selection: some papers become clearly relevant, and others become safe to skip.

Workflow

Identify whether the user's input is a topic, a phenomenon, or an actual question.
Rewrite vague nouns into observable relationships, mechanisms, conditions, or puzzles.
Generate 5-10 alternative question angles rather than one premature answer.
Evaluate each candidate against the three-anchor test.
Recommend one primary question and 1-2 backups, then explain what each would make the review include or exclude.

Three-Anchor Test

A strong literature review question should:

Contain a concrete "why", "how", "through what mechanism", or "under what conditions" focus.
Force literature selection by making some studies irrelevant to the question.
Feel worth answering if a reader could get a credible answer from the review.

If the question fails any item, revise it before proceeding to search or outline the literature.

Question Patterns

Use these patterns when generating candidates:

Mechanism: Why does [phenomenon] occur in [context], and through what mechanism?
Contrast: Why does [outcome] differ between [group/place/time A] and [group/place/time B]?
Condition: Under what conditions does [factor] influence [outcome]?
Tension: Why do existing studies disagree about [relationship/result]?
Process: How does [actor/system] move from [state A] to [state B]?

Output Format

Provide:

## Candidate Questions

1. ...
2. ...

## Recommended Anchor

[One sentence]

## Why This Works

- Selection boundary: ...
- Likely literature modules: ...
- What to skip: ...

## Revision Notes

- If the user wants broader scope: ...
- If the user wants narrower scope: ...

Guardrails

Do not accept XX research review, current situation of XX, or development of XX as the final anchor.
Do not invent a domain-specific claim when the user has not provided enough context; offer candidate angles and mark assumptions.
Treat AI as a sparring partner: generate options and pressure-test wording, but keep the user's academic judgment in charge.