Research guide

AI in research: verification, responsibility and disclosure

Use AI responsibly in research: verify sources and calculations, protect confidential information and disclose use under institution and journal policies.

Scientific coding and a computational simulation mesh on a monitor
Prepared by: Dr. Didgar Research Institute · Last revised: · 3 min read
Expected deliverables
  • Keep an AI-use log and source/input checks with the draft.
Decision workbook and example

Appropriate assistance

Treat brainstorming, code explanations and readability suggestions as proposals to evaluate. Independently verify generated citations, quotations and calculations at original sources or tools. A language model is not an authoritative factual database.

Protect information

Do not upload identifiable participant data, confidential peer-review text or restricted documents without authorization. Review each service’s retention and data-use policy before uploading material.

Responsibility and disclosure

Under ICMJE guidance, AI tools do not meet authorship accountability requirements. Follow institutional or journal policy for disclosure of tools, purpose and affected sections; policies differ between disciplines.

Final verification

Independently check results and references, assess possible bias and keep a record of tool use. No automated detector score alone conclusively establishes text provenance or misconduct.

Maintain a usage record

Record tool and version when available, date, task, permitted input and verification method. Identify assisted passages, code or calculations and who checked them. This record supports review of institutional and journal disclosure requirements.

Verify references and code independently

Locate suggested titles or DOIs on the publisher or repository and match authors and the actual supporting passage. Link resolution alone does not establish correct attribution. Test code on controlled inputs and known-answer cases; successful execution is not scientific validation.

Match use to sensitivity

Public permitted data, unpublished documents and identifiable data need different decisions. Use only necessary information and check actual service terms and project permissions. If permitted use is unclear, consult the responsible institution rather than assuming confidentiality.

Worked case and implementation decisions

The following is a fictional teaching case. Do not use its numbers or wording as actual study findings.

In the exercise an AI tool suggests a plausible but nonexistent reference. Check title, authors and DOI against the original record; if the document cannot be verified, do not present it as a valid source. Disclose actual assistance, scope and human checking under the outlet policy. Do not upload confidential data, restricted manuscripts or participant responses to external tools without permission. Tool output is neither specialist approval nor an accountable author. Record version and date to assess later changes.

Worked case and implementation decisions
StageTeaching exampleVerification question
TaskDefined coding or writing assistanceDoes policy permit this use?
SourceNonexistent suggested referenceHas the original been checked?
InputSynthetic instead of confidential dataIs transfer authorized?
ReviewLogic, numbers and attributionCan authors explain the result?
DisclosureTool, version, role and human checksDoes it match actual work?

Exercise output: Keep an AI-use log and source/input checks with the draft.

Sources and further reading

Official sources for verification and further reading

This guide supports research learning and planning; align implementation with the actual design and institutional requirements. Editorial policy
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