Build an AI-assisted literature review you can verify

Use AI to organize a defined set of papers into an evidence matrix, then check every entry against the original source. The useful output is a traceable comparison of studies that you can revise as your reading develops.

Download the research workflow templates, a ZIP containing editable CSV logs for evidence extraction, citation checking, qualitative coding review, and analysis checks. Open the files in your spreadsheet or research workspace.

This beginner workflow suits an exploratory review, a thesis chapter, or the early stages of a research project. You need a research question, access to the papers, a reference manager, and a spreadsheet. Allow a first working session to build and check a small pilot. A systematic review additionally needs a documented protocol, suitable database searches, explicit screening rules, and reporting appropriate to its design.

Start with a question that controls the search

Write a question specific enough to decide which studies belong in your review. For example: “How do doctoral researchers verify references suggested by generative AI?” Identify the population, phenomenon, setting, and kinds of evidence you need.

Ask an AI assistant to suggest synonyms and adjacent concepts. Treat these suggestions as search vocabulary. Try them in the scholarly databases used in your discipline and inspect the results yourself. Record each database, search string, search date, and any filters. Keep citation chasing as a separately recorded route into the collection.

Assemble a reference library

Save the bibliographic record and the version of each paper you actually read. Add a DOI or another stable identifier where available. Keep a note of why each paper was included. Review possible duplicates before extraction; Zotero provides a Duplicate Items view and a merge operation that preserves collection membership and tags. See the Zotero duplicate-detection guide.

Use a small, deliberately varied pilot set: for example, an interview study, a survey, and a conceptual article. This exposes fields that fit one method poorly before you process a larger collection. The pilot size is a practical starting choice, not a claim about sufficient review coverage.

Define the evidence matrix before using AI

Create columns for: reference ID, research question, setting and sample, design, operational definitions, relevant finding, qualification, supporting passage, page or section, and verification status. Add an “analyst interpretation” column for your own reading.

Keep the reported result separate from your interpretation. A qualitative paper may need a narrative account of a finding; an experimental paper may need an effect estimate, uncertainty interval, and comparison condition. Avoid forcing different forms of evidence into one numerical ranking.

Extract one paper at a time

Use a tool that can work with the paper you provide and expose its supporting passages. Elicit's current workflow guide distinguishes flexible Research Agent sessions, overview reports, and its structured Systematic Review workflow. Choose the mode that fits your task and confirm current access conditions before relying on a feature.

Copy and adapt this instruction:

“Use only the attached paper. Extract its research question, setting and sample, design, operational definitions, and the finding relevant to [my question]. For each field, provide a short supporting passage and a page or section locator. Write ‘not reported’ when the paper does not supply the information. Separate the authors' claims from any interpretation. Flag ambiguous sample sizes and results reported only in supplementary material.”

PDF page numbers can differ from printed page numbers. Specify which locator convention you use. If the tool cannot read a table, supplement, or scanned page, record that limitation and inspect the original.

Verify the pilot and revise the instructions

For each cell, open the cited location. Check whether the wording supports the extracted entry, whether the sample refers to the relevant study, and whether the reported outcome is a primary or secondary result. Mark cells as verified, corrected, unresolved, or not reported.

Keep a short error log. If the tool confuses participants with observations, revise the sample field to request both explicitly. If it turns associations into causal effects, require the design and the authors' causal language beside each result. Re-run the pilot after changing the instructions. Preserve the original extraction so you can see what improved.

Write from the checked matrix

Group studies by question, method, or mechanism. Ask AI to identify candidate agreements and disagreements using only verified rows and reference IDs. Then examine whether apparent disagreement comes from different measures, settings, samples, or designs.

A useful synthesis paragraph makes a claim, identifies the supporting studies, and explains its boundary. An empty part of a small matrix is a search lead; investigate it before calling it a research gap.

Finished output: a reference library, a search log, a checked evidence matrix, and a short account of how AI assisted extraction and how you verified it. Save the tool name, available model/version, date, instructions, source set, and corrections.

Continue with checking AI-generated citations or return to Start here.

Editorial workflow, published 6 November 2026. Product documentation checked on that date. The prompts and worked workflow are original guidance from the Generative AI for Research Initiative.