AI literature review policy template for research teams

Research teams need clear rules for AI-assisted literature review work. Without a policy, AI use can become inconsistent: one person uses it for search, another uses it for extraction, someone else uses it for citations, and nobody records what was checked.

An AI literature review policy does not need to be long. It needs to define allowed uses, restricted uses, source verification, human responsibility, and documentation.

This article provides a practical policy structure that teams can adapt for literature reviews, scoping reviews, thesis projects, lab reviews, and evidence-mapping work.

An AI literature review policy is a short set of rules that explains how a team may use AI tools during literature search, screening, reading, extraction, synthesis, and citation checking.

It should answer:

• Which AI tools may be used?
• What tasks may they support?
• What tasks need human review?
• How will sources be verified?
• How will AI use be documented?
• What material should not be uploaded?
• Who owns final research decisions?

The policy gives the team a shared method. It also makes AI use easier to explain to supervisors, collaborators, institutions, and journals.

A team needs a policy because AI errors are often workflow errors, not only tool errors. The same tool can be used carefully or carelessly depending on the rule set around it.

A policy helps prevent:

• Unsupported claims from AI summaries.
• Unverified citations.
• Inconsistent screening decisions.
• Hidden data extraction errors.
• Unclear responsibility for final decisions.
• Uploading sensitive or unpublished material.
• Methods sections that cannot explain tool use.

The goal is not to slow the team down. The goal is to prevent work from becoming hard to trust later.

For the broader checklist, see responsible AI automation checklist for research teams.

The policy should name tasks where AI can assist but does not make final decisions. These are usually discovery, organization, and reading-support tasks.

Allowed uses may include:

• Brainstorming search terms.
• Finding candidate papers.
• Summarizing abstracts for triage.
• Suggesting themes for human review.
• Drafting questions for deeper reading.
• Locating likely methods, findings, or limitations sections.
• Comparing saved papers at a high level.
• Helping format a search log or extraction table.

Each allowed use should include a check. For example: "AI may summarize abstracts for triage, but reviewers must inspect the original abstract before recording a decision."

The policy should restrict any AI use that could silently change the review's evidence base, conclusions, or academic integrity.

Restricted uses may include:

• Making final inclusion or exclusion decisions without human review.
• Creating citations without verification.
• Using AI summaries as evidence without checking the paper.
• Extracting high-risk fields without source-location checks.
• Uploading confidential or unpublished materials without approval.
• Generating final synthesis claims that are not checked against sources.
• Changing the review question or criteria without team approval.

The policy does not need to ban AI from touching these tasks entirely. It can allow assistance while requiring explicit human verification.

For citation concerns, see how to check whether a paper has been corrected or retracted.

Human review should be tied to specific points in the workflow. A vague promise that "a researcher will check it" is weaker than a named review step.

Define who checks:

• Search strings.
• Inclusion and exclusion criteria.
• Screening decisions.
• Data extraction fields.
• Citation lists.
• Summary claims.
• Synthesis paragraphs.
• Final references.

Also define how checking is recorded. A status column such as unchecked, checked, disputed, or excluded can be enough for many teams.

For extraction checking, see data extraction quality control for AI literature reviews.

The policy should state that any claim used in the review must be traceable to a source. AI output alone is not a source.

Require source verification for:

• Study design.
• Sample or dataset.
• Intervention, exposure, or method.
• Outcome or measure.
• Main finding.
• Limitation.
• Citation details.
• Retraction or correction status when relevant.

The policy should also require source locations when possible. Page numbers, tables, figures, sections, or exact paper locations make later review much easier.

This is the part of the policy that keeps AI-assisted writing grounded.

If AI affects screening order or screening decisions, the policy should define how that use will be recorded. This is especially important for active learning or AI-prioritized workflows.

The policy should specify:

• Whether AI only ranks records or also suggests decisions.
• Whether all records will be screened.
• If not all records are screened, what stopping rule applies.
• Whether random audit samples are required.
• How conflicts are resolved.
• How excluded records are stored.
• How the final flow of records is reported.

For AI-prioritized screening, see what is active learning screening in systematic reviews and when to stop screening in an AI-assisted review.

The policy should define what material can and cannot be uploaded to AI tools. This protects unpublished work, sensitive data, confidential collaboration material, and restricted documents.

Include rules for:

• Public papers.
• Paywalled PDFs.
• Unpublished manuscripts.
• Grant proposals.
• Human-subjects material.
• Internal lab notes.
• Proprietary datasets.
• Third-party confidential documents.

If the team is unsure whether a file can be uploaded, the policy should require approval before upload.

Privacy rules are not a side issue. They determine which tools can be used for which materials.

Documentation should be light enough to maintain but specific enough to inspect. A policy that requires impossible recordkeeping will be ignored.

Record:

• Tool name.
• Date used.
• Task performed.
• Source set used.
• Prompt or task summary when relevant.
• Output type.
• Human review status.
• Final decision or action.
• Known limitations.

For many teams, this can be a search log, screening log, extraction table, or project note.

For a more detailed record structure, see literature search log template for AI-assisted reviews.

Here is a concise policy structure teams can adapt:

Purpose: AI tools may support literature discovery, triage, organization, and source inspection. They may not replace human judgment in final research decisions.

Allowed uses: Team members may use approved AI tools to generate search ideas, find candidate papers, summarize records for triage, organize papers into themes, and ask questions against known source sets.

Human review: Inclusion decisions, exclusion decisions, extracted evidence, citation use, and synthesis claims must be checked by a researcher against the original source.

Source verification: Any claim used in the final review must be linked to a paper or source location. AI-generated summaries are not accepted as standalone evidence.

Citation checking: Citations suggested or formatted by AI must be verified before use. References should be checked for existence, metadata accuracy, claim support, and correction or retraction status where relevant.

Privacy: Confidential, unpublished, identifiable, or restricted material may not be uploaded to AI tools unless the project owner approves the use.

Documentation: AI-assisted steps should be recorded with tool name, task, date, source set, human review status, and final decision.

Accountability: The research team remains responsible for the final literature review, including methods, source selection, extracted evidence, citations, and conclusions.

WisPaper can fit into a policy-based workflow as a research assistant for paper discovery, triage, library organization, and questions against a known paper set. Deep Search, Scholar Agent, and Inspiration Discovery can help researchers move from a natural-language question to candidate papers.

Paper cards show source labels, summaries, authors, publication details, and preview images, which helps researchers make first-pass triage decisions before deeper reading. My Library lets users save or upload papers, and Library QA can answer questions based on the user's own library.

For citation checking, TrueCite, powered by WisPaper, checks BibTeX files against real academic databases to flag hallucinated references. A responsible policy should still require researchers to verify whether each source supports the claim being cited.

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