Literature review assistants

Literature review assistants take a research question and return papers, summaries and, in some tools, a table of extracted details such as methods, sample size and outcomes. They speed up the first pass of a review, when you are trying to see what the field looks like and which papers deserve a close read.

Elicit searches a large corpus of papers and lets you add columns to pull specific details from each one. SciSpace combines paper search with an assistant that explains passages and extracts findings across many papers. Scholarcy breaks single papers into summary cards with key findings and references, which helps when you have a stack of articles to triage. Keenious works the other way round: it reads the text you are writing and suggests papers related to it, inside Word or Google Docs.

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4 AI research tools in literature review assistants

Elicit

Research assistant that finds papers for a question, summarizes them and extracts details such as methods and outcomes into a table.

  • Free plan or free tier
  • Shows sources for its answers

SciSpace

Research workspace that searches papers, explains passages in plain language and extracts findings across many papers.

  • Free plan or free tier
  • Shows sources for its answers

Scholarcy

Breaks papers into summary flashcards with key findings, references and figures, for reading many articles quickly.

Keenious

Recommends papers that relate to the text you are writing, working inside Word and Google Docs as well as on the web.

  • Works in Word or Google Docs

The weak point of all of these is accuracy at the detail level. A summary can miss a key limitation, and an extracted number can come from the wrong table or subgroup. Check every value that goes into your manuscript against the paper itself. For a formal systematic review, use these tools as aids next to a documented search and a proper screening process, and describe how you used them in your methods.

These tools are most useful early, when you are scoping a question and want to know whether a review already exists, how large the evidence base is, and which outcomes studies tend to measure. Later, they help you check that you have not missed a cluster of papers. They are a poor fit for the final analysis, where you need to read and judge each study yourself.

Questions about literature review assistants

Can an AI tool write my literature review?

It can help you find and sort papers, but the review is your analysis of the field. Many universities and journals restrict AI-written text or require disclosure. Check your institution's AI policy and use these tools for finding and organizing, with the writing your own.

How accurate is data extraction in these tools?

It varies by paper and by field. Simple items such as sample size are often right; subgroup results, effect sizes and details in tables are easier to get wrong. Check each value against the paper before it goes into an evidence table.

Do these tools search every paper?

No. Each searches its own index, which may miss papers behind paywalls, in certain fields or in languages other than English. Pair them with a search of the databases your field expects.

Should I mention these tools in my methods section?

If a tool shaped which papers you found or how you extracted data, yes. Name the tool, say what you used it for, and say how you checked its output. Your journal or university may have specific disclosure wording.

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