AI in Research Writing
Library shelves with a glowing network of connected research papers

Part 1

Framing & Literature

Key question: Can AI help me find the gap faster without inventing the evidence for it?

Where AI genuinely helps in this stage

The bottleneck in a literature review is not reading speed — it is synthesis: seeing how twenty studies disagree and where your work sits. That is a reasoning task, and it is where AI adds real value.

  1. Interrogate and narrow your research question until it is answerable.
  2. Build systematic search strings for Scopus, WoS or Google Scholar.
  3. Screen abstracts you retrieved yourself against explicit criteria.
  4. Turn your own downloaded PDFs into a synthesis matrix and a gap statement.

Non-negotiable: the AI never supplies a citation. You supply the papers; the AI helps you think about them.

Prompts to copy and try

1. Stress-test your research question

Best in Claude or ChatGPT

Act as a critical reviewer for a [FIELD] journal indexed in Scopus Q1.

My draft research question is:
"[PASTE YOUR QUESTION]"

Do the following, in order:
1. Tell me what is vague, unmeasurable, or already well-established in this question.
2. Identify the implicit assumptions I am making.
3. Rewrite it as 3 sharper alternatives: one descriptive, one explanatory, one comparative.
4. For each alternative, state the design and data needed to answer it, and the likely "so what?" objection a reviewer would raise.

Be blunt. Do not compliment the question.

Forcing the model into a reviewer role produces critique instead of agreement. The 'do not compliment' line removes the default flattery.

2. Build a systematic search string

Best in Gemini or ChatGPT

I am running a literature search on: [TOPIC].

Build a Boolean search strategy I can paste into Scopus and Web of Science.
- Break my topic into 3-4 concept blocks.
- For each block, list synonyms, British/American spellings, acronyms and related terms.
- Combine with AND between blocks, OR within blocks, using truncation (*) and quoted phrases.
- Give me the final string in Scopus TITLE-ABS-KEY() syntax and a plain-text version.
- Then suggest 3 terms that are likely to create false positives and how to exclude them.

Search strings are pure syntax and vocabulary work — a task with no fabrication risk and a huge time saving.

3. Screen abstracts against your criteria

Best in Claude (long context) or Gemini

Below are [N] abstracts I retrieved from my database search.

My inclusion criteria: [LIST]
My exclusion criteria: [LIST]

For each abstract, output a table row: ID | Include / Exclude / Unclear | one-line reason | which criterion it maps to.
Mark "Unclear" whenever the abstract does not contain enough information — do not guess.
At the end, list the IDs you marked Unclear and tell me exactly what I need to check in the full text.

ABSTRACTS:
[PASTE]

You supply the abstracts, so nothing can be invented. 'Unclear' is an allowed answer, which stops the model from forcing a decision.

4. Build a synthesis matrix from your own PDFs

Best in NotebookLM (Gemini)

Using only the sources I uploaded, build a synthesis matrix as a table with these columns:
Author & year | Context/sample | Theoretical lens | Method & data | Key finding | Stated limitation.

Then, still using only these sources:
1. Group the studies into 3-5 thematic clusters and name each cluster.
2. Identify where findings conflict, and quote the specific sentences that conflict.
3. List what none of these sources address.
Cite the source for every row and every claim.

NotebookLM is grounded in your uploaded documents and cites back to them, so every line is checkable. This is the single highest-value AI use in a literature review.

5. Draft a defensible gap statement

Best in Claude

Here are my synthesis notes: [PASTE MATRIX OR SUMMARY].

Write 3 versions of a research gap statement of 80-120 words each:
(a) an empirical gap, (b) a methodological gap, (c) a contextual/population gap.

For each version: state what is already known, what is missing, and why filling it matters to [FIELD].
Then critique your own three versions and tell me which is the weakest and why.

Asking the model to critique its own output surfaces the weak framing before a reviewer does.

Tool spotlight for this stage

NotebookLM

Your reading room. Upload 20-50 PDFs and ask grounded, cited questions across all of them.

Tip: Ask 'which sources disagree with each other and where?' — then click the citations to verify.

Claude

Long-document critique and synthesis. Best at nuanced argument and honest pushback.

Tip: Give it a reviewer persona and forbid compliments to get usable critique.

Gemini

Search strategy, Deep Research reports, and quick landscape scans with live links.

Tip: Always open the links it returns — a live link is still not a read paper.

ChatGPT

Fast iteration on question framing, keyword expansion, and concept definitions.

Tip: Turn on web/search mode for anything time-sensitive, and still verify.

Two more tools worth knowing

  • Elicit / ConsensusSearch engines over real indexed papers. They extract findings, sample sizes and outcomes into a table, with links to the actual articles — far safer than asking a chatbot for references.
  • Connected Papers / LitmapsVisual citation maps from one seed paper. Excellent for spotting the classic works and the recent cluster you have missed.

Integrity boundary for Part 1

Do this

  • Retrieve papers from Scopus, WoS, PubMed or Scholar — then feed them to AI.
  • Verify every DOI you cite by opening it.
  • Keep the AI's synthesis as notes, and write the review in your own words.

Never do this

  • Ask any chatbot to 'list references' or 'cite sources' from memory.
  • Cite a paper based on an AI summary you have not read.
  • Paste a colleague's unpublished manuscript into a public tool.

Your notes are saved in this browser only.