AI for Research | Build a Literature Review You Can Trust

Turn the topic into a research question

A topic is a field of interest. A research question sets the boundary of the review. “AI in education” includes many tools, students and outcomes. “What evidence compares worked examples with retrieval practice in introductory algebra?” gives you a clearer search and a way to decide whether a paper belongs.

The example question is a planning exercise, not a claim that a particular answer has been established. Define the population, intervention or comparison, and outcome that matter. Decide whether you need a broad narrative review or a formal method with documented inclusion criteria.

An assistant can suggest terminology and alternative phrasings. Review those suggestions against the discipline’s actual vocabulary. Do not let a convenient generated outline decide the scope before you have established what you are trying to learn.

Use AI to prepare searches, then verify the papers

Ask for search concepts and synonyms rather than an instant authoritative bibliography. Use the relevant scholarly databases and original publisher records to find candidates. Keep a search log with the query, date, database and screening decisions.

For each candidate, confirm the title, authors, publication year and identifier. Open the original record and the paper when available. A working DOI that points to a different title is not a successful verification.

Walters and Wilder’s 2023 study examined bibliographic references produced by ChatGPT and documented fabricated references and errors in real ones. That finding concerns the tested systems and tasks; it is not a current error-rate estimate for every research assistant. It supports checking the references rather than assuming academic formatting establishes authenticity.

Build an evidence matrix before writing the synthesis

Attach a passage or page reference to important entries. Ask the assistant to organize the supplied information, but check that it has not filled an empty field with an inferred sample, intervention or conclusion.

Keep “not reported” available as an answer. A complete-looking matrix with invented cells is less useful than an incomplete matrix that accurately represents the papers you have.

Explain agreements and disagreements rather than stacking summaries

A literature review is more than one paragraph per paper. Organize the evidence around the question: where findings agree, where they differ and what differences in design might explain the disagreement.

For the algebra example, two papers may use different age groups or measure different outcomes. One might measure immediate performance, while another measures delayed recall. They can appear contradictory if those distinctions disappear in a generated summary.

Ask the assistant to propose a synthesis using your verified matrix, with each claim linked to the supporting entries. Review the argument yourself. A gap in evidence should remain a gap, not become a confident generalization that makes the paragraph flow more smoothly.

Audit the final review against the source record

Before submitting, open each cited source and check the claim it supports. Verify quotations and page references. Confirm that the bibliography and in-text citations match and that the paper version you read is the one you identify.

Read later corrections or updates when they affect the conclusion. Preserve research limitations in the final prose, especially when explaining an association as distinct from a causal result. Do not add a more dramatic claim during the final style pass.

Follow the applicable policy for disclosing AI assistance. The strongest use of AI in research can be organizational: sharpening a question, comparing supplied evidence and finding places where the argument needs another check. The sources and the conclusion still need a reviewer who can explain how one supports the other.