Responsible Use of AI in Research and Grant Applications
There is no single rulebook for AI use in research. Requirements differ by funder and by journal, and they continue to change. But the major guidance has converged on a consistent set of expectations, and several firm lines now carry real consequences. This page is for Reynolda Campus faculty preparing a proposal, reviewing one, or writing up results.
Four Principles
Whatever the funder or publisher, these four expectations are constant:
- You are the author. AI cannot be named an author or carry responsibility. Accountability for the accuracy, originality, and integrity of what you submit stays entirely with you.
- Verify everything. If you use AI, treat AI output as draft material to check, not facts to paste. Fabricated citations and invented references are the most common and most damaging problem.
- You own the errors. Any inaccuracy or plagiarism an AI tool introduces becomes yours the moment you submit, regardless of how it got there.
- Disclose your use. Be transparent that you used AI. Where the disclosure goes, and how much detail it needs, depends on the sponsor or journal.
Using AI to write or edit a proposal
Expand for specific guidance related to NIH and NSF proposals.
NIH
NIH will not treat an application, or any section of one, that is substantially developed by AI as the applicant’s original idea, and will not consider it. This policy took effect for due dates on or after September 25, 2025, and is now reflected here and in the NIH Grants Policy Statement. NIH has deliberately not set a numeric threshold for what counts as “substantially developed,” so your own judgment is what governs. Think of it as a spectrum.
Lower-risk uses:
- Grammar and clarity edits
- Formatting and readability checks
- Feedback on text you wrote yourself
- Summarizing background literature you then verify
Higher-Risk Uses:
- Generating the scientific argument or novelty claim
- Drafting Specific Aims, rationale, or study design
- Producing the methodology or the reviewer-facing narrative
- Inserting AI-generated citations or evidence
The closer AI moves toward authoring the science, the greater the originality risk.
NSF
NSF asks proposers to indicate in the project description whether and how generative AI was used to develop the proposal, and holds you responsible for the accuracy and authenticity of everything you submit, including AI-assisted content.
If you submit to both agencies, the safe default is the same: keep the scientific substance your own, and disclose any AI assistance.
If you serve as a peer reviewer
Never upload a proposal or manuscript you are reviewing into a public AI tool.
NSF treats anything entered into AI tools outside its firewall as having entered the public domain, which breaks the confidentiality of merit review, can create legal liability, and violates the reviewer confidentiality pledge. NIH likewise prohibits using generative AI to analyze or formulate critiques of applications under review. Journal peer review carries the same near-universal prohibition.
The harm is concrete: uploading surrenders another researcher’s unpublished ideas, and your control over them, with no way to get them back.
Writing up and publishing results
Most journals follow the International Committee of Medical Journal Editors (ICMJE), and the pattern is consistent across major publishers:
- No AI authorship. An AI tool cannot be a named author, because it cannot take responsibility for the work.
- Acknowledge writing assistance. Disclose AI used to help with writing in the Acknowledgments.
- Document analytical use in Methods. If AI was used in data collection or analysis, describe it in the Methods section.
A useful rule of thumb: assistive use (polishing your own words) usually needs little or no disclosure; generative use (creating new content) must be disclosed; and AI image generation is frequently prohibited outright. Always confirm your target journal’s specific policy before submitting.
Protecting sensitive and confidential information
This is where funder AI guidance, IRB obligations, and University data-security policy all meet.
Whatever a funder permits in general, do not enter into any AI tool material that carries an obligation to someone else. That includes:
- data governed by sponsor confidentiality or non-disclosure terms
- export-controlled or otherwise regulated technical data
- identifiable human-subjects or other regulated data
- invention disclosures or results ahead of a patent filing
- collaborators’ unpublished data.
Note that your own in-progress work can fall into these categories — for instance, a draft that contains human-subjects data or results headed for disclosure.
For your own writing and analysis that carries no such obligation, you may use the AI tool of your choice. If you’re not certain whether your material is free of these obligations — a draft can contain regulated data without your thinking about it — treat it as protected, or use a tool whose data-handling terms the University has cleared.
Primary sources
These are the authoritative notices. Funder policy changes frequently — confirm against the live notice before relying on any detail here.
- NIH NOT-OD-25-132 — Supporting Fairness and Originality in NIH Research Applications (2025). https://grants.nih.gov/grants/guide/notice-files/NOT-OD-25-132.html
- NIH NOT-OD-23-149 — The Use of Generative AI Technologies is Prohibited for the NIH Peer Review Process (2023). https://grants.nih.gov/grants/guide/notice-files/NOT-OD-23-149.html
- NSF — Notice on the use of generative AI technology in the NSF merit review process. https://www.nsf.gov/policies/ai/merit-review
- ICMJE — Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work (role of authors; AI guidance). https://www.icmje.org/recommendations/
Prepared June 2026. Funder and publisher AI policies are evolving rapidly; verify each section against current primary sources before each submission cycle.