CASRAI Dictionary

Tag: generative ai academic integrity policy

  • AI Regulation and Higher Education Impact Study

    A January 2026 peer-reviewed study in Higher Education Quarterly finds that AI regulation is pushing universities away from ad hoc AI adoption and toward formal governance — mapping obligations under laws such as the EU AI Act, embedding AI literacy in curricula, and rebuilding academic-integrity policy around disclosure rather than detection. This ai regulation higher education impact study synthesises global regulatory developments and institutional case studies to show where policy is converging, and where gaps remain for research administrators to close.

    AI regulation in higher education is the body of statutory law, sector guidance, and institutional policy that governs how universities may develop, procure, and deploy artificial-intelligence systems across teaching, assessment, research, and administration.

    What does the new 2026 study say about AI regulation and higher education?

    The study, “Regulations of AI Technologies and Their Impact on Higher Education: Global Perspectives, Institutional Cases and Emerging Challenges,” was published online on 28 January 2026 in Higher Education Quarterly (Wiley). It argues that the policy question has shifted from whether universities should use AI to how AI use is aligned with institutional missions under emerging law.

    The paper traces AI’s move from a research topic to a mass-adoption technology, driven by generative tools used across the largest single demographic of AI users: the higher-education age cohort. It documents institutional case studies showing that governance failures — not the technology itself — are the primary source of risk, spanning privacy, algorithmic bias, accountability, and intellectual property.

    A recurring finding is the gap between faculty and student behaviour: large shares of faculty report concern that generative AI weakens critical thinking, while comparably large shares of students already use it routinely for coursework. The study treats this gap as the central governance problem regulation is now trying to close.

    How does the EU AI Act classify AI use in universities?

    The EU AI Act (Regulation (EU) 2024/1689) is the first comprehensive, cross-sectoral binding AI law and directly affects universities that develop or deploy AI within scope. It treats education as one of the domains where AI carries elevated risk to fundamental rights.

    Under the Act’s risk tiers, AI systems used to determine admission or access to education, or to evaluate learning outcomes, fall into the high-risk category, which triggers obligations on risk management, data governance, transparency, and human oversight. Separately, the Act prohibits AI systems that infer emotions in education and workplace settings, subject to narrow safety and medical exceptions.

    Institutions operating admissions, proctoring, or automated-grading tools therefore need to establish which systems fall inside these categories before procurement, not after deployment. A related academic framework, HEAT-AI (Temper, Frontiers in Education, 2025), adapts the AI Act’s structure specifically for teachers and students navigating classroom-level AI use.

    What does AI regulation mean for academic integrity policy?

    Regulatory pressure is forcing academic-integrity policy to move from detection-based enforcement toward disclosure-based governance. This mirrors the position scholarly publishing bodies reached earlier: the Committee on Publication Ethics (COPE) holds that AI tools cannot be credited as authors, and the International Committee of Medical Journal Editors (ICMJE) requires authors to disclose AI or large-language-model use in manuscript preparation.

    In the UK, HEPI’s April 2026 Policy Note 67 sets out the same logic for teaching and research: institutions are advised to adopt frameworks such as the UK Research Integrity Office’s (UKRIO) “Embracing AI with Integrity” guidance, invest in bias-reduction and transparency in AI-assisted research, and build interdisciplinary review of AI-related integrity cases rather than relying on plagiarism-style detection tools alone.

    The practical consequence for institutional policy is definitive: integrity policy must specify what disclosure is required, by whom, and at what stage of a piece of work — a rule set, not a ban.

    Framework or body Scope Key AI requirement Status
    EU AI Act (Regulation (EU) 2024/1689) EU-based universities High-risk classification for admissions/assessment AI; ban on emotion-inference AI in education In force since August 2024, phased obligations
    UKRIO “Embracing AI with Integrity” UK research institutions Guidance for research-integrity offices on responsible AI use Active sector guidance
    COPE position on AI authorship Scholarly publishing AI tools cannot be credited as an author; use must be disclosed Established position statement
    ICMJE recommendations Manuscript submission Disclosure of AI/LLM use in preparing a manuscript Updated recommendations
    HEPI Policy Note 67 UK higher education sector Recommendations balancing AI innovation with research integrity Published April 2026

    How should institutions build an AI governance framework?

    An AI governance framework for higher education needs to combine legal compliance mapping with day-to-day academic practice, rather than treating regulation as a one-off legal exercise. The 2026 study and the frameworks above point to the same sequence of steps.

    • Inventory every AI system used in admissions, assessment, advising, and research support, and classify each against applicable risk tiers.
    • Assign clear accountability for AI-related decisions, including who signs off on procurement and who owns incident response.
    • Embed AI literacy — not just AI tool training — into curricula and staff development, covering critical evaluation of AI outputs.
    • Require disclosure of AI use in coursework, research outputs, and manuscripts, aligned with COPE and ICMJE positions.
    • Treat data governance as a prerequisite: AI systems are only as reliable as the institutional data feeding them.

    Institutions with active research administration functions are best placed to run this inventory, since compliance mapping, integrity policy, and procurement oversight already sit within that remit.

    Answer-first Q&A: AI regulation and higher education

    Does the EU AI Act apply to universities?

    Yes. The EU AI Act applies to universities that develop, procure, or deploy AI systems within its scope, including tools used for admissions, assessment, or learning-outcome evaluation. These uses fall into the Act’s high-risk category, triggering obligations on risk management, transparency, and human oversight before deployment.

    Which AI uses in education are classified high-risk?

    The Act classifies AI systems used to determine access or admission to education, and those used to evaluate learning outcomes, as high-risk. Separately, it bans AI systems that infer emotions in education settings, with narrow exceptions for safety or medical purposes.

    What should a 2026 academic integrity AI policy include?

    A current academic-integrity policy should require disclosure of AI use rather than rely on detection tools, define acceptable use by assessment type, and align with established positions from COPE and ICMJE on authorship and manuscript disclosure, plus UK-specific guidance such as UKRIO’s framework.

    What are the first steps in an AI governance framework?

    The first steps are an inventory of every AI system in use across admissions, teaching, and research, risk-tier classification against applicable law, and named accountability for procurement and incident response, followed by staff and student AI literacy training.

    Implications for research administrators and institutional leaders

    For research administrators, the 2026 study’s core message is that AI regulation is now a compliance and integrity function, not solely an IT or teaching-and-learning issue. Procurement, data governance, research-integrity offices, and academic-integrity committees need a shared inventory of AI systems and a single accountability map, or institutions risk inconsistent responses to the same underlying regulatory requirement.

    Institutions that map AI systems against EU AI Act risk tiers, adopt disclosure-based integrity policy consistent with COPE and ICMJE, and follow UKRIO-style guidance are better positioned to demonstrate compliance to regulators, funders, and accreditors alike.

    What comes next for AI regulation in higher education?

    The direction of travel identified by the study is toward more, not less, formal AI regulation reaching higher education, extending beyond the EU AI Act as other jurisdictions develop comparable frameworks. Institutions that build governance capacity now — inventory, accountability, disclosure, literacy — will face lower compliance and integrity risk as further rules arrive, rather than retrofitting policy after the fact.

  • AI-Generated Content Code of Practice: What It Means for Journals and Preprint Servers

    The AI-Generated Content Code of Practice is the European Commission’s voluntary framework, published 10 June 2026, that helps providers and deployers of generative AI systems meet the labelling and disclosure duties in Article 50 of the EU AI Act. For journals and preprint servers, the Code’s “editorial responsibility” carve-out is the single most consequential clause: it determines whether peer-reviewed articles, preprints, and AI-assisted manuscript text trigger a public AI-disclosure requirement.

    The Code of Practice on Transparency of AI-Generated Content is a non-binding compliance instrument: it is a voluntary set of practical measures that signatories can use as evidence of compliance with the legally binding transparency obligations set out in Article 50 of Regulation (EU) 2024/1689, the EU AI Act.

    What is the AI-Generated Content Code of Practice?

    The Code of Practice on Transparency of AI-Generated Content was closed out at a plenary session on 10 June 2026, following a drafting process that ran from November 2025 through three drafting rounds, the last concluding on 8 May 2026. It was produced by the European Commission’s AI Office through two working groups: one covering obligations for providers of generative AI systems, the other covering obligations for deployers — the organisations that actually publish AI-generated or AI-assisted output.

    Providers must ensure that generated audio, image, video, and text outputs are marked in a machine-readable format detectable as artificial, using layered technical measures such as metadata and watermarking. Deployers must clearly label deepfakes and must disclose AI-generated text on matters of public interest unless that text has undergone human review and is subject to editorial responsibility. That single exemption clause is what makes the Code directly relevant to scholarly publishing.

    Article 50 vs Article 56: two different codes, not one

    Publishers should not confuse this Code with the earlier General-Purpose AI Code of Practice, finalised on 10 July 2025 under Article 56 of the AI Act. That code addresses safety, security, and copyright compliance for developers of foundation models such as GPT- and Gemini-class systems — it is not about labelling published content.

    The June 2026 Code sits under Article 50 instead, and governs transparency obligations that apply from 2 August 2026, when the wider AI Act’s transparency provisions take effect. Confusing the two codes is the most common error in early legal commentary on this development, and it matters for publishers: it is Article 50 — not Article 56 — that determines whether an AI-assisted peer-review report, cover letter, or manuscript summary requires a visible “AI” label.

    What this means for journal editorial workflows

    Peer-reviewed journal articles are the clearest case for the editorial-responsibility exemption. A manuscript that has passed through peer review, editorial decision-making, and copyediting has, by definition, undergone the “human review… subject to editorial responsibility” that Article 50(4) requires to avoid the public-disclosure trigger for AI-generated text.

    This does not remove the underlying disclosure obligation that scholarly publishing already imposes through its own ethics infrastructure. ICMJE’s Recommendations state that AI tools cannot be credited as authors because they cannot take responsibility for the submitted work, and that any generative AI use in manuscript preparation must be disclosed to editors and readers. COPE’s position statement on AI tools reaches the same conclusion: AI cannot be an author, and authors remain fully accountable for content it helped produce. The EU Code’s editorial-responsibility test and the ICMJE/COPE disclosure rule are therefore complementary, not duplicative — a journal that already enforces ICMJE-COPE disclosure norms is well placed to document compliance with the EU Code if it chooses to sign.

    • Editorial policy: confirm the AI-use disclosure clause in author guidelines references generative AI text, not only images or data.
    • Peer review reports: reviewers using AI drafting tools should disclose this to editors, mirroring the deployer disclosure logic in the Code.
    • Editorial metadata: retain records evidencing human review, since this is the documentation that supports the Article 50(4) exemption claim.

    Preprint servers: a narrower exemption path

    Preprints are structurally different. A preprint is, by design, posted before formal peer review and before an editorial board takes responsibility for its content. That means the “editorial responsibility” exemption that shelters a published journal article is much harder for a preprint server to claim at the point of posting.

    Preprint servers such as arXiv, bioRxiv, and medRxiv already run moderation screening, but screening for scope and plagiarism is not the same as the substantive editorial review Article 50(4) contemplates. Where a preprint contains AI-generated text on a matter of public interest — a policy-relevant synthesis, a public-health claim — a strict reading of the Code suggests deployer-side disclosure obligations may apply at the preprint stage, even though the same text would likely be exempt once it clears peer review and is published in a journal. Preprint operators serving EU users should treat this as a genuine compliance gap to close, not an afterthought.

    Content type Human review / editorial responsibility present? Likely Article 50 disclosure trigger
    Peer-reviewed journal article Yes — editorial board, peer review, copyediting Exempt (if AI use is disclosed per ICMJE/COPE norms)
    Preprint (pre-review) Limited — screening only, no substantive editorial review Disclosure obligation more likely to apply
    AI-generated figure or image (deepfake-style) Not applicable — separate deployer rule Labelling required regardless of review stage
    AI-assisted literature-review drafting Depends on subsequent editorial handling Case-by-case; disclose per journal policy

    Answer-first Q&A

    Is the AI-Generated Content Code of Practice mandatory?

    No. The Code of Practice is voluntary; signing it is optional. What is legally binding is Article 50 of the EU AI Act itself, which applies from 2 August 2026. Signing the Code simply gives providers and deployers, including publishers, a recognised route to demonstrate compliance with those binding obligations.

    Does the Code of Practice apply to preprints?

    The Code applies to any deployer publishing AI-generated text on matters of public interest to EU audiences, which can include preprint servers. Because preprints have not undergone substantive editorial review at posting, the editorial-responsibility exemption is harder to claim than for peer-reviewed journal articles, making preprint-stage disclosure more likely to be required.

    Can AI-generated text be listed as an author contribution?

    No. ICMJE and COPE both hold that generative AI tools cannot qualify as authors because they cannot be held accountable for the work or approve the final version. Human authors must disclose AI use and retain full responsibility for accuracy, originality, and integrity of the resulting manuscript text.

    How does this Code differ from the GPAI Code of Practice?

    The GPAI Code of Practice (Article 56, July 2025) governs foundation-model developers’ safety, security, and copyright duties. The AI-Generated Content Code of Practice (Article 50, June 2026) instead governs labelling and disclosure of AI-generated outputs by the organisations that publish them — the code directly relevant to journals and preprint servers.

    Implications and a compliance checklist

    For the publisher segment of CASRAI’s audience, the practical task is narrow but time-sensitive: journals should audit whether their existing ICMJE/COPE-aligned AI-disclosure clauses reference the Code’s editorial-responsibility test, and preprint operators serving EU readers should assess whether pre-review screening is sufficient to avoid a deployer-side disclosure obligation once Article 50 takes effect on 2 August 2026.

    • Map current author-guideline AI-disclosure language against Article 50(4)’s “human review and editorial responsibility” wording.
    • Confirm peer review and editorial sign-off records are retained as exemption evidence.
    • Assess whether preprint-stage moderation constitutes “editorial responsibility” under a plain reading of the Code, or whether additional review is needed.
    • Track AI Office guidance and signatory lists, since the Code’s practical measures may evolve as more publishers sign.

    Institutions coordinating research-integrity policy across editorial offices and research administration functions should treat this as a live compliance item for the second half of 2026, and align it with existing authorship disclosure norms rather than treating it as a separate, parallel rulebook.