- AI-Assisted Editing vs AI-Generated Drafting
- Funder Rules Compared: NIH, UKRI, ERC, NHMRC and NSF
- The Compliance Checklist for Research Offices
- Common Questions on AI Use in Grant Applications
- Template Disclosure Language for Applicants
- Implications for Research Offices
- What Comes Next
Research offices are fielding the same question from every principal investigator this cycle: what counts as acceptable AI grant application assistance, and what will get a proposal flagged? The honest answer is that funders have converged on a rough principle — AI can edit, but it cannot originate — while diverging sharply on enforcement, disclosure and consequences. Grammar-checking and language polishing with a large language model is now explicitly permitted almost everywhere. Using AI to draft the scientific argument, generate specific aims, or write an entire proposal is not, and that gap is where applications get rejected or, in NIH’s case, administratively withdrawn.
This checklist reconciles the current rules from NIH, UKRI, the European Research Council (ERC) and NHMRC (with a note on NSF, since both funders publish closely watched AI guidance), and gives research administrators ready-to-adapt disclosure wording for applicants.
AI-Assisted Editing vs AI-Generated Drafting
Funder policies converge on a distinction between two categories of AI use, even where the exact wording differs.
- AI-assisted editing: grammar and spelling correction, clarity and readability improvements to text the applicant has already written, translation, and administrative formatting. This is broadly permitted.
- AI-generated drafting: producing the scientific rationale, specific aims, hypotheses, study design or an entire section without substantive human authorship. This is broadly prohibited, and in NIH’s case carries the risk of administrative withdrawal.
Evaluation is treated as a separate, stricter category again. Every funder examined for this analysis — NIH, UKRI, ERC and NSF — bars peer reviewers from using generative AI to analyse, summarise or score applications, largely to protect the confidentiality of unpublished ideas.
Funder Rules Compared: NIH, UKRI, ERC, NHMRC and NSF
The table below summarises publicly stated positions as of mid-2026. Research offices should always check the current version of the cited policy, as several funders note their guidance will evolve.
| Funder | Applicant drafting/editing use | Full AI-generated content | Disclosure required | Peer reviewer AI use |
|---|---|---|---|---|
| NIH | Permitted for grammar, spelling and clarity only | Prohibited; applications “substantially developed by AI” may be administratively withdrawn under NOT-OD-25-132 (effective 25 Sept 2025) | No formal disclosure field; NIH uses AI-detection screening and caps most PIs at six applications/year | Prohibited from using AI to analyse or critique applications |
| UKRI | Permitted for drafting, editing, idea generation and literature comparison | Prohibited: applicants “must not use generative AI tools to generate an entire application, or sections of an application, without human involvement” | Encouraged, not mandatory; disclosure does not affect assessment | Prohibited from using generative AI in assessment |
| ERC | Permitted for brainstorming, literature searches, revising, translating and summarising | Prohibited in substance: applicants retain “full and sole authorship responsibility”; text-similarity detection is used | Not a separate mandatory statement | Strict non-delegation policy: no AI summarising, assessing or draft-evaluation writing |
| NHMRC | Permitted for drafting, editing and organising ideas | Applicant must verify accuracy against the Australian Code for the Responsible Conduct of Research; sensitive data must not enter public AI systems | Not a separate mandatory statement | Reviewers may use AI only to refine the wording of their own comments, not to evaluate or score |
| NSF | Permitted for proposal preparation assistance | Proposers are responsible for accuracy of all content regardless of AI involvement | Required: proposers must indicate the extent of generative AI use in the project description | Reviewers barred from uploading proposal content to public AI tools (confidentiality breach) |
The Compliance Checklist for Research Offices
Institutional research offices can use the following checklist when advising applicants ahead of submission.
- Confirm which category the intended AI use falls into — editing/formatting versus content generation — before the applicant starts drafting.
- Check the specific funder’s current AI policy page rather than relying on last year’s guidance; NIH, UKRI and NSF have all updated their positions since 2023.
- Where disclosure is required (NSF) or encouraged (UKRI), draft the disclosure statement early and route it through the same sign-off as conflict-of-interest and human-subjects certifications.
- Warn applicants against pasting unpublished proposal content, preliminary data, or collaborator information into free or public AI tools — this risks both confidentiality breaches and, in the EU/UK, data protection exposure.
- Never advise applicants to use AI for peer-review-adjacent tasks such as scoring their own proposal in a way that substitutes for genuine self-assessment.
- Keep a record of which AI tools were used and for what purpose, in case a funder requests it during a research-integrity enquiry.
Common Questions on AI Use in Grant Applications
Can I use ChatGPT to write my grant application?
Most funders allow ChatGPT and similar tools for grammar checks, clarity edits and brainstorming, but not for drafting the scientific argument or specific aims. NIH, UKRI, ERC and NHMRC all place ultimate authorship responsibility on the applicant, so a proposal substantially generated by AI risks rejection.
Does NIH allow AI-generated grant applications?
No. Under NOT-OD-25-132, effective 25 September 2025, NIH treats applications or sections “substantially developed by AI” as not reflecting the applicants’ original ideas, and such submissions may be administratively withdrawn. NIH also screens for AI use and caps most principal investigators at six new or resubmitted applications per year.
Do I need to disclose AI use in a grant application?
It depends on the funder. NSF requires applicants to state the extent of generative AI use directly in the project description. UKRI encourages disclosure without penalty at assessment. ERC and NHMRC do not mandate a disclosure statement but still hold the applicant fully accountable for all AI-assisted content submitted.
Can grant peer reviewers use AI to assess applications?
Generally no. NIH, UKRI, ERC and NSF all prohibit reviewers from using generative AI to analyse, summarise or score proposals, largely to protect confidentiality and prevent unpublished ideas reaching public tools. NHMRC allows a narrow exception: reviewers may use AI only to polish the wording of their own comments.
Template Disclosure Language for Applicants
Research offices are repeatedly asked for standard wording applicants can adapt rather than draft from scratch. Two templates cover the main scenarios.
Where disclosure is required or requested (NSF/UKRI-style):
“Generative AI (tool: [name and version]) was used to [check grammar and clarity / generate an initial outline] of Sections [X]. All scientific content, analysis and conclusions are the original work of the named investigators, who take full responsibility for the accuracy and integrity of this application.”
Where disclosure is not mandatory but institutions want a defensive record (ERC/NHMRC-style, kept on file):
“The applicants used [tool name] to assist with editing and language clarity only. No AI tool was used to generate the scientific rationale, methodology, hypotheses or original data interpretation contained in this application.”
Neither template substitutes for reading the specific solicitation text, which occasionally adds requirements beyond the funder’s general policy.
Implications for Research Offices
The practical challenge is that these policies are not converging on common language, so a one-size-fits-all institutional AI policy will misfire on at least one major funder. A UKRI-style permissive default with encouraged disclosure would not protect a PI from NIH’s administrative-withdrawal risk, and an NIH-style prohibition would leave NSF’s mandatory disclosure field unanswered.
- Build funder-specific AI guidance into pre-award checklists rather than a single institution-wide statement.
- Treat AI-use attestations the same way as financial conflict-of-interest disclosures — logged, dated and retrievable if a funder investigates later.
- Extend research-integrity training to cover AI-specific risks: fabricated citations, hallucinated preliminary data, and inadvertent disclosure of unpublished ideas to public tools.
- Coordinate with research administration leadership on how AI-use records intersect with existing misconduct and compliance processes.
What Comes Next
Evidence on outcomes is starting to complicate the compliance picture. A February 2026 Nature analysis found AI-drafted NIH proposals were more likely to be funded, but that funded proposals using AI assistance also tended to read more similarly to one another — a finding likely to sharpen funder scrutiny of homogenised language rather than loosen it. Expect NIH’s detection and application-limit measures to be tested over the next funding cycle, while UKRI, ERC and NHMRC continue to state their guidance will be revisited as the evidence base evolves. The safest institutional posture for now is documented, funder-specific caution: assume editing is safe, assume drafting is not, and keep a paper trail either way.
