CASRAI Dictionary

Tag: ai in research administration

  • AI Grant Application Rules: A Compliance Checklist for Research Offices

    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or write an entire proposal is not, and that gap is where applications get rejected or, in NIH’s case, administratively withdrawn.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from using generative AI to analyse, summarise or score applications, largely to protect the confidentiality of unpublished ideas.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.

  • OECD AI Principles vs the EU AI Act: What Research Offices Need to Know

    Research offices coordinating international collaborations increasingly need to distinguish between two very different kinds of AI governance instrument. The OECD AI principles set out a shared, values-based standard that 47 governments have politically endorsed since 2019, while the European Union’s AI Act is a legally binding regulation carrying fines for non-compliance. For institutions running Horizon Europe consortia, UKRI-funded partnerships, or transatlantic data-sharing agreements, knowing which framework applies, and when, determines real compliance obligations rather than aspirational good practice.

    What Are the OECD AI Principles?

    The OECD AI Principles originate from a Recommendation of the OECD Council (OECD/LEGAL/0449), adopted in May 2019 as the first intergovernmental standard on artificial intelligence. As a Recommendation rather than a treaty, adherence is a political commitment, not a legal obligation. Despite that soft-law status, the framework has proved influential: its definitions of “AI system” and “AI system lifecycle” have been carried directly into the EU AI Act, US federal guidance, Council of Europe instruments and a 2024 UN General Assembly resolution on AI.

    The Principles were updated in May 2024 to account for generative AI and refine the underlying definitions, while keeping the same structure. There are now 47 adherents, spanning OECD members and partner economies including the UK, US, Japan and Korea.

    The Recommendation sets out five values-based principles for responsible AI stewardship:

    • Inclusive growth, sustainable development and well-being — AI should benefit people and the planet.
    • Human-centred values and fairness — AI actors must respect the rule of law, human rights, privacy and democratic values.
    • Transparency and explainability — AI actors should enable people to understand and, where appropriate, challenge AI-based outcomes.
    • Robustness, security and safety — AI systems must function reliably throughout their lifecycle, including under adverse conditions.
    • Accountability — organisations and individuals responsible for AI systems are accountable for their proper functioning.

    Alongside these values-based principles, the Recommendation sets out five policy recommendations for governments: invest in AI research and development, foster an inclusive AI ecosystem, shape an enabling governance environment, build human capacity for workforce transitions, and strengthen international co-operation. For research offices, this pairing matters: the values-based principles function as an ethical baseline for institutional AI policy, while the policy recommendations shape how national research funders design their own AI-in-research guidance.

    The EU AI Act: A Binding, Risk-Based Regime

    Formally Regulation (EU) 2024/1689, the EU AI Act entered into force on 1 August 2024 and is legally binding on anyone who places an AI system on the EU market, puts one into service in the EU, or whose AI system’s output is used within the EU — irrespective of where the provider is established. That last point is the crucial difference from the OECD’s soft-law approach: enforcement follows market and deployment triggers, not adherent status.

    The Act classifies AI systems by risk:

    • Unacceptable risk — practices such as social scoring and manipulative AI are banned; prohibitions applied from 2 February 2025.
    • High risk — systems used in areas such as education access, admissions or candidate evaluation face strict duties on data governance, technical documentation and human oversight; most obligations apply from 2 August 2026 (some product-safety-annex systems from 2 August 2027).
    • General-purpose AI models — providers face transparency and, for the most capable models, systemic-risk obligations that applied from 2 August 2025.
    • Limited and minimal risk — lighter transparency duties (e.g. disclosing AI-generated content) or none at all.

    Non-compliance carries real financial exposure: fines for prohibited practices can reach €35 million or 7% of global annual turnover, whichever is higher.

    Crucially for universities and research institutes, Article 2 of the Act exempts AI systems and models developed and used for the sole purpose of scientific research and development, provided they are not placed on the market or put into operational service. That exemption is narrower than it sounds: the moment a pilot admissions-scoring tool, a proctoring system or a research-evaluation model moves from an internal research exercise into operational use, including free publication as a usable tool, the exemption can lapse and the relevant risk-tier obligations apply.

    Feature OECD AI Principles EU AI Act
    Legal status Non-binding Council Recommendation Legally binding Regulation (EU) 2024/1689
    Adopted 2019, updated May 2024 Entered into force 1 August 2024; phased application to 2027
    Approach Values-based principles plus policy recommendations Risk-tiered obligations (unacceptable/high/limited/minimal)
    Enforcement Peer reporting via the OECD.AI Policy Observatory Fines up to €35m or 7% of global turnover
    Research exemption No formal exemption — applies as ethical guidance to all AI activity Article 2 exempts AI developed solely for scientific R&D, until placed on the market
    Territorial trigger Adherent governments and their institutions (47 as of 2026) Anywhere an AI system is placed on the EU market or its output used in the EU

    Frequently Asked Questions

    What are OECD principles on AI?

    The OECD AI Principles are five values-based commitments — inclusive growth, human-centred values, transparency, robustness and accountability — adopted in a 2019 OECD Council Recommendation and updated in 2024. They sit alongside five policy recommendations for national AI strategy and are non-binding: adherents commit politically, not legally.

    What is the scope of the AI Act?

    The EU AI Act applies to any provider or deployer that places an AI system or general-purpose AI model on the EU market, puts it into service in the EU, or whose AI system’s output is used within the EU, regardless of where the organisation is established. A narrow exemption covers systems developed solely for scientific research.

    What are the key features of the AI Act?

    The Act classifies AI by risk tier: unacceptable-risk practices are banned, high-risk systems face strict obligations on data governance and human oversight, limited-risk systems carry transparency duties, and minimal-risk systems remain largely unregulated. Obligations phase in between February 2025 and August 2027.

    What is the main goal of the AI Act?

    The EU AI Act aims to ensure AI systems used in the EU are safe and respect fundamental rights, while still fostering innovation and a single EU market for trustworthy AI — mirroring, in binding legal form, values the OECD Principles set out voluntarily back in 2019.

    Implications for International Research Collaborations

    For a research administration office running a Horizon Europe or multi-country consortium, the practical dividing line is not nationality but where an AI system is placed on the market or put into service. The UK’s own regulatory approach remains principles-based and sector-led rather than a single statute, which sits closer to the OECD’s soft-law model than to the EU’s binding Act. That means a consortium spanning EU and non-EU institutions typically needs to apply the OECD Principles as a governance floor everywhere, while layering EU AI Act obligations only where the EU leg of the project triggers them.

    Practical steps for research offices include:

    • Map every AI touchpoint across the consortium — admissions tools, grant-scoring assistants, participant-facing chatbots, drafting tools built on general-purpose models — to check whether the Article 2 research exemption still applies once a tool moves from pilot to operational use.
    • Treat the OECD Principles as the baseline for institutional AI ethics policy and grant conditions, since 47 governments, including most funder jurisdictions, already reference them.
    • Track the EU AI Act’s phased dates in agreements with EU partners: prohibited-practice compliance from February 2025, general-purpose AI model duties from August 2025, and most high-risk obligations from August 2026.
    • Flag any AI tool used in EU-facing admissions, proctoring or research-evaluation processes as a potential high-risk use under Annex III, requiring documentation and human oversight even where the underlying research itself remains exempt.

    The two frameworks are not on a collision course. The EU AI Act’s adoption of the OECD’s own definition of an “AI system” points toward gradual convergence in vocabulary, even as legal force diverges. Research offices that build their AI governance around the stricter of the two applicable layers, rather than the more comfortable one, will find both frameworks easier to satisfy as further OECD updates and EU implementing guidance arrive.

  • EU AI Act Research Exemption: What Article 2(6) Actually Covers

    A run of academic literature published since mid-2025 — an editorial in GRUR International, a peer-reviewed analysis in Nature’s npj Digital Medicine, and a widely cited Swedish doctoral paper — has converged on the same conclusion: the EU AI Act research exemption is far narrower, and far less certain, than most research offices assume. Regulation (EU) 2024/1689 does carve scientific research and development out of scope, but that carve-out is built from two separate provisions with different wording, different triggers, and different failure points. For institutions running AI-assisted studies, clinical trials, or general-purpose model development, misreading where the exemption ends is now a live compliance risk.

    What Article 2(6) actually says

    Article 2(6) of the AI Act states that the Regulation “does not apply to AI systems or AI models, including their output, specifically developed and put into service for the sole purpose of scientific research and development.” Two conditions must both be met: the system or model must be developed for scientific research, and it must be put into service — first used for its intended purpose — exclusively for that research. Recital 25 is the only interpretive gloss the legislative text offers, and it does not define “scientific research and development” further.

    Critically, Article 2(6) exempts systems that are put into service for research, but it does not extend to systems that are placed on the market. That distinction — put into service versus placed on the market, defined respectively in Articles 3(10) and 3(9) — is where the exemption’s practical limits begin.

    Two exemptions, not one: Article 2(6) vs Article 2(8)

    Law professor Michèle Finck’s October 2025 editorial “In Search of the Lost Research Exemption” (GRUR International, Vol. 74, Issue 10) makes the point that is most often missed: the AI Act contains two distinct research exemptions, not one. Article 2(6) is narrow and limited to scientific research; Article 2(8) is broader and covers any research, testing or development activity, scientific or not, but only up to the point of market placement or service.

    Provision What it exempts Key limit
    Article 2(6) AI systems/models developed and put into service solely for scientific research and development Not limited to pre-market stage, but strictly tied to “sole purpose” of research — loses protection once put into service for any other use
    Article 2(8) Any research, testing or development activity (not limited to science) regarding AI systems or models Applies only prior to placing on the market or putting into service; explicitly excludes real-world testing

    Finck argues that this dual structure creates an “interpretative conundrum”: if Article 2(8) only ever covers activity that happens before market placement, and market placement is already the trigger for the Act’s obligations regardless of the exemption, the provision risks adding little independent legal value — precisely the ambiguity that gives the “lost” exemption its name.

    Where the research exemption stops applying

    The Nature-published analysis by Meszaros and colleagues (npj Digital Medicine, 2026) sets out a conceptual framework built around a single regulatory threshold: placement on the market or putting into service. Everything on the research side of that line can be exempt; everything on the other side is regulated. Three scenarios repeatedly cross that line.

    Commercialisation and dual-purpose systems

    A system loses its exemption the moment it is not developed for the sole purpose of research. Finck highlights that Horizon Europe-style collaborations, where a university partners with an industrial co-investigator who intends to commercialise the output once the exploratory phase ends, sit in exactly this grey zone. Whether “commercial purpose” is assessed objectively (does a commercial partner exist) or subjectively (did the researchers intend commercialisation) remains unresolved in the text itself.

    Post-market deployment and real-world testing

    Article 2(8) states plainly that “testing in real-world conditions shall not be covered by that exclusion.” A model tested only in a closed lab environment can remain exempt; the same model tested on live users, patients, or public-facing systems generally cannot, unless it proceeds through the Act’s dedicated real-world testing and regulatory sandbox framework (Articles 57–61). Colonna’s 2024 analysis for the DiVA repository similarly stresses that the exemption was never intended to cover deployment-stage activity dressed up as “ongoing research.”

    GPAI models and systemic-risk obligations

    Because Article 2(6) explicitly names “AI models” alongside “AI systems,” a general-purpose AI (GPAI) model built and used solely for research is exempt. That exemption evaporates once a provider places the model on the Union market — including releasing a checkpoint for downstream use beyond pure research. From that point, Title VIII’s GPAI obligations under Article 53 (technical documentation, copyright-compliance summaries) apply, and models presumed to carry systemic risk — those trained with cumulative compute above 10^25 FLOPs — face the additional Article 55 duties regardless of open-source licensing. A separate, unconditional exclusion exists for military, defence and national-security AI under Article 2(3); that provision is absolute and is not contingent on “sole purpose,” unlike the research exemptions.

    Frequently asked questions

    What is Article 2(6) of the EU AI Act?

    Article 2(6) excludes AI systems and AI models — including their output — from the AI Act when they are specifically developed and put into service for the sole purpose of scientific research and development. It does not, however, exempt systems that have been placed on the market.

    Does the AI Act research exemption cover real-world testing?

    No. Article 2(8) states explicitly that testing in real-world conditions is not covered by the research exclusion. Researchers deploying systems outside a controlled setting generally need to use the Act’s regulatory sandbox and real-world testing framework instead.

    Are GPAI models exempt from the AI Act during research?

    Yes, while a general-purpose AI model is developed and used solely for research it falls outside scope. Once placed on the market, Title VIII obligations attach, with stricter Article 55 duties for models presumed to carry systemic risk above the 10^25 FLOPs training-compute threshold.

    Can university-industry collaborations rely on the research exemption?

    Only where the sole purpose remains scientific research. Per Finck’s 2025 analysis, Horizon Europe-style projects involving a commercial partner intending future exploitation risk losing Article 2(6)/2(8) protection once a profit-oriented purpose is established.

    What this means for research institutions and publishers

    Research administration offices — the ARMA, EARMA and INORMS community that oversees institutional compliance — now have a practical due-diligence question to add to AI-enabled research proposals: at what point does this project’s AI system move from “developed for research” to “put into service” or “placed on the market”? That question matters most for:

    • Clinical and biomedical AI tools that progress from retrospective lab validation to prospective real-world testing on patients.
    • Multi-partner Horizon Europe consortia where an industrial partner holds commercialisation rights from the outset.
    • Open-source model releases on code and model-sharing platforms, which several commentators — including the arXiv paper “Beware! The AI Act Can Also Apply to Your AI Research” — flag as a possible trigger for “placing on the market.”
    • Foundational research (for example, in AI explainability or causal reasoning) whose downstream applications are not yet known at the outset, which Finck notes may struggle to meet the “sole purpose” test even where no commercial partner is currently involved.

    Institutions with dedicated research administration functions are best placed to build this threshold assessment into ethics review and grant-agreement workflows now, rather than retrofitting compliance once a system reaches deployment.

    Looking ahead

    The AI Act’s general provisions, including Article 2’s scope rules, have applied since 2 February 2025; GPAI obligations followed on 2 August 2025; most remaining obligations, including high-risk system requirements under Annex III, become applicable from 2 August 2026. Every commentator reviewed here — Finck, Meszaros et al., and Colonna — reaches the same practical conclusion: the European Commission’s promised guidance on the research exemptions has not yet resolved the “sole purpose,” commercial-intent, and real-world-testing ambiguities in the text. Until that guidance lands, institutions should treat the exemption as a narrow, conditional safe harbour rather than a blanket shield, and document the specific research purpose, funding structure, and deployment plan for every AI system that currently relies on it.

  • AI in Grant Peer Review: How ERC, NIH, UKRI and NHMRC Draw the Line

    Four major funders have now published, or are actively revising, formal rules on AI in grant peer review, and the details differ enough that a reviewer moving between panels could unknowingly breach one funder’s terms while complying with another’s. In March 2026 the European Research Council (ERC) issued new guidelines on AI use in evaluation; the US National Institutes of Health (NIH) tightened its stance on AI-drafted applications from September 2025; UK Research and Innovation (UKRI) maintains a stricter blanket ban that peers expect to loosen; and Australia’s National Health and Medical Research Council (NHMRC) introduces a revised generative-AI policy from 28 April 2026. Research offices drafting or updating reviewer agreements need to track all four.

    How ERC, NIH, UKRI and NHMRC draw the line

    Each funder separates permitted “AI-assisted” support from prohibited “AI-generated” evaluation, but the exact boundary — and the effective date — varies.

    Funder Rule effective AI-assisted (permitted) AI-generated (prohibited)
    ERC 24 March 2026 Language polishing of a reviewer’s own report; general (non-proposal) information searches Summarising proposals, assessing scientific merit, drafting evaluations, uploading any proposal content to external AI systems
    NIH Applications submitted from 25 September 2025 Limited administrative tasks in application preparation Reviewers using generative AI to analyse applications or formulate critiques; applications “substantially developed by AI” are treated as non-original and not reviewed
    UKRI Current policy; Research Funding Policy Group review pending None yet formally sanctioned for reviewers — even AI-assisted grammar checks are currently disallowed Any generative AI use by reviewers or panellists in assessing applications
    NHMRC 28 April 2026 Generative AI to refine clarity or grammar of a reviewer’s own comments Using AI to evaluate, critique or score applications

    A fifth data point is worth noting: the US-based Foundation for Food & Agriculture Research (FFAR) went further still in November 2025, prohibiting reviewers from using AI tools in any capacity during peer review — including refinement of their own comments — on confidentiality grounds. That makes FFAR the strictest outlier against which UKRI’s current position, and NHMRC’s narrower allowance, can be benchmarked.

    • Confidentiality is the universal red line. Every policy reviewed prohibits uploading proposal text, applicant data or reviewer notes into public or third-party AI tools.
    • Non-delegation is the second constant. Scientific merit assessment must remain a human judgement in all four jurisdictions, regardless of how permissive the language-polishing allowance is.
    • UKRI is currently the most conservative of the four, with a sector-wide Research Funding Policy Group review expected to permit limited generative AI use in processing (not scoring) applications while keeping final decisions human-made.

    AI-assisted vs AI-generated: common questions

    Research offices repeatedly ask the same handful of questions when briefing reviewers. The answers below are grounded in the funder documents referenced above.

    What is the difference between AI-assisted and AI-generated peer review?

    AI-assisted review means a human reviewer uses a tool only for mechanical tasks — grammar, clarity, formatting of their own text — while retaining full intellectual authorship of the assessment. AI-generated review means the AI performs part of the evaluative task itself, such as summarising a proposal, scoring merit, or drafting critique content, which every funder surveyed here prohibits.

    Has NIH banned AI in grant peer review?

    Yes. NIH prohibits scientific peer reviewers from using generative AI tools to analyse applications or formulate critiques, a position it has held since June 2023. From 25 September 2025, NIH also treats applications substantially developed by AI as non-original, removing them from review rather than scoring them on merit.

    Can UKRI reviewers use AI to check grammar in their assessments?

    Not currently. UKRI’s existing policy forbids reviewers and panellists from using generative AI for any part of assessment, including language or grammar correction — a stricter line than ERC or NHMRC. A sector-wide funder policy group is expected to revisit this, but any change would still require human-made final decisions.

    When does the NHMRC generative AI policy take effect?

    NHMRC’s revised Policy on Use of Generative Artificial Intelligence in Grant Review takes effect from 28 April 2026. It permits peer reviewers to use generative AI to refine the clarity or grammar of their own comments, but explicitly prohibits using AI to evaluate, critique or score applications.

    Practical reviewer-agreement language for research offices

    Research offices administering panels — whether for an internal seed-fund competition, a co-funded international call, or as a delegated peer-review manager for an external funder — need reviewer agreements that anticipate divergence between funder rules. Three drafting principles reduce risk:

    • Name the prohibited actions explicitly, not just the tool category. A clause banning “AI tools” is weaker than one banning “uploading proposal content, applicant identifiers, or draft scores to any AI system, whether or not the funder’s own policy names that system.”
    • State the confidentiality obligation independently of the AI-use clause. General-purpose AI (GPAI) providers regulated under the EU AI Act’s GPAI obligations, in force since August 2025, may process submitted inputs for model improvement unless expressly excluded, so agreements should require reviewers to confirm no proposal content has been shared with any third-party system, GPAI-regulated or not.
    • Require disclosure, not just prohibition. A short attestation line — “I have not used generative AI to draft, summarise or score any part of this review, and any AI assistance used was limited to language editing of my own original text” — gives research integrity offices an auditable record if a dispute arises.

    Where a funder (such as NHMRC from April 2026) permits limited AI-assisted editing, research offices should still require reviewers to disclose which tool was used and confirm no proposal content was entered into it. This keeps institutional practice defensible even where funder rules differ from one call to the next.

    Implications and outlook

    For institutions running multi-funder portfolios, the practical challenge is less about any single funder’s rule and more about reviewer confusion across simultaneous panels. A reviewer serving both an ERC panel and a UKRI-funded call in the same month operates under materially different AI permissions for the same underlying task. Research offices should treat funder AI policies as living documents — ERC’s and NHMRC’s 2026 updates both followed roughly a year or more after their organisations’ initial public positions on AI, suggesting further revision is likely as reviewer behaviour and AI capability both evolve.

    The direction of travel across all four funders is convergence on two non-negotiables — confidentiality of proposal content and non-delegation of scientific judgement — even as the permitted margin for administrative AI assistance slowly widens. Research offices that build reviewer agreements around those two constants, rather than around any single funder’s current wording, will need fewer rewrites as UKRI’s pending policy shift and any subsequent NIH or ERC revisions land through 2026 and beyond.

    For related terminology used across funder and publisher AI-governance documents, see the CASRAI research dictionary, and for broader institutional process guidance visit the research administration resource hub.

  • AI in Research Administration: Where It’s Actually Deployed

    Most coverage of artificial intelligence in higher education still centres on the classroom — chatbots writing essays, detectors chasing them. Less visible, but arguably more consequential for research offices, is AI in research administration: the back-office layer of proposal budgeting, compliance screening and post-award reporting that keeps federally and privately funded research compliant and auditable. That layer is where AI is quietly moving from pilot to production in 2026, and the evidence — not the marketing copy — shows a narrower, more cautious footprint than headlines suggest.

    This is not a piece about generative AI and authorship integrity, disclosure norms, or research misconduct detection in manuscripts — those questions sit in a separate, already well-documented debate. This is about the administrative machinery: proposal-budget checking, risk-based compliance review, contract redlining and financial reporting inside research offices, sponsored-programmes units and grants-management systems.

    Where AI Is Actually Being Deployed

    The clearest signal comes from a March 2026 Ithaka S+R report, funded through the National Science Foundation’s GRANTED programme (grant #2437518), which convened two workshops — one at Montclair State University (31 participants, 13 institutions) and one at Chapman University (32 participants, 13 institutions) — specifically to catalogue how research administration software and AI tools are being used inside research offices. The findings map closely onto three workflow areas:

    • Pre-award proposal and budget checking. Institutions are using AI to review draft proposals and budgets for items that will trigger downstream review — facilities requirements, human-subjects protocols, or budget lines inconsistent with a sponsor’s rules.
    • Risk-based compliance screening. AI is used as a first-pass filter that flags transactions, contract clauses, or expenditures for human review rather than replacing that review — described by workshop participants as “an extra layer” that directs attention, not a decision-maker.
    • Contract and reporting automation. Redlining of routine contract language, drafting of progress narratives, and identification of funded projects with commercialisation potential are the most cited post-award use cases.

    Two concrete examples illustrate the pattern at very different institutional scales. Southern Utah University, a smaller teaching-focused institution, built a budget-availability report that automatically flags high-risk expenditures for review — a narrow, operationally specific tool rather than a platform. At the University of California San Diego, a large research-intensive institution, the contracts and grants office is running risk-based proposal review to identify projects needing facilities or IRB attention, and has automated non-disclosure-agreement redlining in a way staff estimate cuts drafting time by roughly 70 percent.

    Workflow stage AI use case Maturity in 2026 Reported example
    Pre-award Proposal/budget risk flagging Early production UCSD risk-based proposal review
    Pre-award Funding-opportunity matching Experimental Faculty-to-grant matching pilots
    Compliance Contract clause / NDA redlining Early production UCSD NDA redlining
    Compliance Expenditure anomaly flagging Pilot Southern Utah University budget-availability tool
    Post-award Progress-report drafting Experimental Institution-reported pilots, Ithaka S+R 2026
    Institution-wide Policy Q&A chatbot for staff Early production UCSD TritonGPT; Emory ORAgpt proof-of-concept

    Evidence From the Field: What Institutions Report

    Two enterprise-level projects sit ahead of the field. TritonGPT, developed at UC San Diego and trained on institutional policy documents, has been available to the campus community since 2023 and is now offered as software-as-a-service to other institutions. At the University of Idaho, the NSF GRANTED-funded AI4RA initiative is building open-source tools for research administrators. At the system level, the California State University system ran a 94,000-response AI sentiment survey — described as the largest of its kind — to set baseline metrics before committing to further rollout.

    These are not isolated enthusiasm projects. The Council on Governmental Relations (COGR) has documented that the U.S. federal government issued more than 200 new or revised policies affecting research administration over the preceding ten years — a compliance burden that is the actual driver behind AI adoption, not novelty. Emory University’s sponsored-programmes office built a proof-of-concept generative AI chatbot, reported by SRA International in May 2025, intended to give research administrators instant, policy-grounded answers rather than requiring them to search static guidance documents.

    Answer-First Q&A

    What is AI actually used for in research administration?

    Institutions report using AI mainly for risk-based screening: flagging proposal budgets, contract clauses, or expenditures that need human review, plus drafting routine reports and answering staff policy questions. It is deployed as a triage layer, not as an autonomous decision-maker in compliance-sensitive workflows.

    Is AI reliable enough for research compliance work?

    Not on its own. Workshop participants in the Ithaka S+R study described current tools as error-prone for high-stakes compliance decisions, so institutions keep a human reviewer in the loop and use AI outputs as a prioritisation signal rather than a final determination.

    What is electronic research administration (eRA) software?

    Electronic research administration (eRA) software centralises pre-award proposal development, post-award financial tracking, IRB/IACUC compliance management, and reporting in one system. Vendors including Cayuse, InfoEd Global and Streamlyne are now embedding AI features into these existing platforms rather than institutions building AI tools separately.

    Will AI replace research administrators?

    Current evidence points the other way. Institutions describe AI as freeing staff time for relationship-building and strategic work, while raising a genuine concern: if entry-level document review and compliance checks are automated away, the profession may lose the training ground that builds administrator judgement over time.

    What Remains Experimental — and Why

    Effort-report anomaly detection — using AI to flag inconsistencies in how research staff certify time charged to federal awards — is frequently proposed as a logical extension of risk-based screening, but publicly documented institutional deployments remain scarce as of mid-2026. This gap matters: effort reporting sits inside some of the most tightly regulated financial-compliance territory in federally sponsored research, and institutions appear to be moving deliberately rather than rushing tools into that specific workflow.

    Three barriers recur across every institution surveyed in the Ithaka S+R workshops:

    • Data governance. Fragmented, inconsistent institutional data undermines AI output quality, and grant proposals routinely contain data covered by HIPAA, export-control rules, or pre-publication intellectual property.
    • Fragmented adoption. Most institutions have not articulated an institution-wide AI strategy for research administration; use is left to individual staff discretion, producing uneven, hard-to-scale experimentation.
    • Trust. Faculty scepticism about whether proposal or compliance data will be used to train external vendor models directly affects whether research administrators can deploy AI tools without damaging working relationships they depend on.

    Implications for Institutions and the Profession

    The practical pattern for institutions considering AI in grants management and compliance workflows is narrower and more disciplined than vendor marketing implies: start with a specific, bounded use case — budget flagging, contract redlining, a policy-guidance chatbot — evaluate it against defined return-on-investment questions, and keep a human reviewer accountable for the final determination. The institutions cited above succeeded by treating AI as an attention-directing layer inside existing research administration workflows, not as a replacement for the judgement that compliance work requires.

    For the broader field of research management and administration, the open question the Ithaka S+R researchers themselves flag is workforce development: if AI absorbs the entry-level document review that has historically trained new research administrators, institutions will need to redesign how professional judgement is built, not just how workloads are reduced. Organisations such as NCURA, SRA International and NORDP are already the venues where this cross-institutional knowledge-sharing is happening, ahead of any formal standard for AI use in the field.

    CASRAI’s own coverage of research administration software categories and standards tracks how eRA platforms are evolving as AI features are absorbed into existing pre-award, post-award and compliance modules — the practical mechanism by which most institutions will encounter AI in this space, rather than through bespoke in-house builds.