Tag: AI in grant peer review

  • Scientific Preprints: What the 2026 Survey Shows

    Scientific preprints are now a routine part of research life, but new 2026 survey evidence shows researchers remain deeply divided on whether posting one helps or hurts a career. A scientific preprint is a complete draft of a research manuscript shared publicly before formal peer review, most commonly via a discipline-specific server such as bioRxiv, medRxiv, arXiv or Preprints.org. Across three separate studies published in 2025-2026, most researchers say preprints speed up dissemination and widen visibility, yet a majority still believe evaluators reward peer-reviewed journal articles over preprints when it comes to hiring, promotion and funding decisions.

    What do the 2026 preprint surveys actually show?

    The most detailed new evidence comes from a survey of nearly 1,800 biomedical researchers in the United States and Canada, fielded in early 2025 and posted to bioRxiv in March 2026 under the title “Faster science, penalties in evaluation, and concerns on quality and impact: Researchers’ use and perceptions of preprints”. Its findings, also reported by Science/AAAS, describe engagement driven mainly by speed rather than any deeper commitment to open science.

    Two smaller but complementary studies add discipline and geography to the picture: a 2026 cross-sectional survey of 103 medical faculty at Marmara University in Türkiye, published in JMIRx Med, and a 2025 nationwide survey of 170 early-career researchers in India, run by the Indian National Young Academy of Science with the International Science Council and reported by Research Matters. Together they show that global attitudes to preprints are not converging; they are fragmenting by discipline, career stage and national research-assessment culture.

    How many researchers read, cite and post preprints?

    In the US/Canada biomedical survey, two-thirds of respondents had read at least one preprint in the previous two years, roughly half had submitted one themselves, and only about one-third had cited one in their own published work. Nearly half of respondents said they worried preprints could spread shoddy research or misinformation before it has been checked.

    Survey Population and date Sample size Headline finding
    Researchers’ Use and Perceptions of Preprints (bioRxiv, 2026) US and Canadian biomedical researchers, fielded early 2025 ~1,800 Two-thirds had read a preprint in two years; most did not believe posting one improved career prospects
    Awareness, Experiences and Attitudes Toward Preprints Among Medical Academics (JMIRx Med, 2026) Medical faculty, Marmara University, Türkiye 103 Awareness was inconsistent and clinical adoption remained cautious
    INYAS/International Science Council nationwide survey (2025) Early-career researchers, India 170 52.3% cited fear of being “scooped” as the top barrier to posting a preprint

    The gap between reading, citing and posting matters. Researchers evidently trust preprints enough to consult them for current findings, but a much smaller group is willing to stake a citation, and fewer still are willing to post their own unreviewed work under their name.

    Do preprints help or hurt career advancement?

    The bioRxiv survey is unambiguous on this point: researchers on average do not believe publishing preprints enhances their career advancement. More than 60% of respondents who sit on funding, hiring or tenure committees said they give more credit to peer-reviewed papers than to preprints, and fewer than 12% said they credit both equally. Only around 16% strongly agreed that preprints reduce the weight evaluators place on articles in subscription, peer-reviewed journals.

    Yet the same respondents were not dismissive of preprints as a practice. Two-thirds of hiring-committee members said they viewed preprints favourably as evidence of productivity and momentum, even while acknowledging that a preprint carries less formal weight than a peer-reviewed publication. Researchers also credited preprints with two concrete, non-evaluative benefits: they spread findings faster than peer-reviewed journals do, and they help authors find collaborators. Credibility judgements, meanwhile, still lean heavily on author reputation rather than any formal quality signal attached to the preprint itself.

    • Career-advancement belief: low, on average, across all three 2025-2026 surveys.
    • Speed-to-dissemination benefit: consistently rated the strongest advantage.
    • Credibility heuristic: author and institutional reputation, not peer review status.
    • Formal recognition in tenure and promotion frameworks: still rare or absent.

    Why does adoption vary so much by country and discipline?

    Adoption is shaped less by technology access than by how national research-assessment systems treat unreviewed work. In India, the top-cited barrier to posting a preprint was fear of being “scooped” (52.3% of respondents in the INYAS/International Science Council survey), closely followed by a lack of institutional recognition. A genuine, if narrow, policy shift is under way there: India’s University Grants Commission guidelines now permit preprints to be considered as part of doctoral-degree assessment, a concrete recognition step most other jurisdictions have not yet matched.

    In Türkiye, the JMIRx Med survey of clinical academics found inconsistent awareness of what a preprint even is, alongside caution rooted in traditional publishing norms in medicine. That pattern echoes wider findings from Digital Science’s 2025 State of Open Data report, the longest-running annual survey of open-research behaviour, which found that two-thirds of over 43,000 researcher respondents still feel they receive insufficient credit for open practices generally — a “credit gap” that maps directly onto preprint hesitancy.

    Answer-first: common preprint questions

    What is a scientific preprint?

    A scientific preprint is a complete manuscript version shared publicly, typically via a dedicated server, before it has completed formal peer review or journal publication. It lets researchers establish priority and gather early feedback while the manuscript is still moving through review at a journal.

    Are preprints credible?

    Preprints carry no guarantee of quality because they bypass formal peer review, but many undergo basic screening by the hosting server. Survey evidence shows readers judge credibility mainly by author reputation and institutional affiliation rather than any formal quality mark on the preprint itself.

    Can preprints be cited in academic work?

    Yes. Major style guides, including APA, provide explicit reference formats for preprints, and ICMJE recommendations permit citing them provided the version is clearly identified as unreviewed. Reviewers should still prefer the peer-reviewed version once one exists.

    Do preprints help or hurt a researcher’s career?

    2026 survey data show a split effect: preprints speed up dissemination and help researchers find collaborators, but most respondents do not believe posting one improves career advancement, and over 60% of evaluators still credit peer-reviewed papers more heavily in hiring and tenure decisions.

    What this means for institutions and publishers

    The consistent finding across all three 2025-2026 surveys is a mismatch between researcher behaviour and institutional recognition. Researchers are reading, and increasingly posting, preprints for pragmatic reasons — speed, visibility, collaboration — while formal evaluation frameworks lag behind. India’s UGC decision to count preprints toward doctoral assessment is a rare example of policy catching up with practice; most institutions have not made an equivalent move.

    For research administrators and publishers, the practical implication is that preprint policy cannot be treated as a peer-review question alone. It touches authorship and contribution standards, since credit for early-shared work still needs to be attributed accurately, and it belongs firmly within research administration policy on how non-traditional outputs are counted in assessment. Clear institutional guidance — not just server-level screening — is what closes the credit gap these surveys describe.

    Where preprint culture goes next

    None of the 2025-2026 evidence suggests preprint use will slow. Reading and posting rates are already high across biomedical fields, and funders are steadily normalising rapid, open sharing of results. What has not caught up is formal recognition: until hiring, promotion and funding committees credit preprints on comparable terms to peer-reviewed work, the gap between researcher enthusiasm and institutional reward will persist. The 2026 surveys make that gap measurable for the first time at this scale — the next test is whether assessment frameworks respond.

  • 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.