How to write a literature review with AI

The literature review is where AI helps most and where it fails worst. Used naively, a chatbot summarises papers it has never read and cites papers that don't exist. Used well, an AI literature review tool compresses months of reading into weeks, while every claim stays traceable to a real source. The difference is the workflow. Here's the one CiteDash AI is built around.

1. Search wide, save deliberately

Start in Literature Finder: one search reaches 250M+ works across OpenAlex, PubMed, Semantic Scholar, and arXiv plus the indexed corpus, matched by meaning rather than exact words. Use the citation map to walk outward from key papers, and let 'Write with this' recommendations flag what the AI would actually cite for your question. Save what matters. Open-access PDFs fetch automatically, and saved searches keep feeding new matches to your review queue.

2. Read with receipts

In the Thesis Library, interrogate each saved paper. Every answer cites the exact passage it came from, and highlights land in your quote bank bound to their source spans. This is the raw material for synthesis: full text, not abstracts, and never the model's memory of a paper.

3. Build the evidence matrix

The Synthesis Lab turns the pile into structure: one row per paper, one column per question you're asking the literature. Every filled cell is extracted from full text, carries its source span, and is fact-checked before you see it. A cell the AI can't ground stays empty rather than being guessed. Apply ROB2, GRADE, or CASP appraisal so weak studies can't quietly carry strong conclusions.

4. Synthesise themes, not summaries

A literature review is an argument, not an annotated bibliography. Theme synthesis clusters the matrix into themes, agreements, tensions, and gaps, and drafts a narrative in which every claim links back to matrix cells with live verification verdicts. The gaps it surfaces are candidate research questions you can explore directly in the Question Builder.

5. Draft the chapter, grounded

Hand the synthesis to the Thesis Editor and draft the literature review chapter framed on your research question. Every AI-drafted claim carries a citation that resolves to a real paper; anything unsupported is flagged in place, never smoothed over. Per-paragraph indicators show grounding at a glance, and one click re-verifies the whole chapter.

Doing a formal systematic review?

If your field expects PRISMA-grade rigour (explicit criteria, recorded screening decisions, a flow diagram) the same Synthesis Lab runs that full workflow with your saved searches doubling as the documented search strategy. Read "Run a PRISMA systematic review with AI" for that path; this guide covers the narrative review every thesis needs.