{"resourceId":"utah-orio-generative-ai-research-record-20261001","versions":[{"version":"external-852eb9ab880e2da8f7c741f634b6226072813841cea5015e51041a502264e1ae","resource":{"id":"utah-orio-generative-ai-research-record-20261001","title":"University of Utah Research Integrity Office: administrators see AI-assisted work with hallucinated citations and invented or modified data; researchers remain accountable for the record","organization":"University of Utah Office of the Research Integrity Officer (Office of the Vice President for Research)","sector":"Research administration / research integrity","geography":"United States (Utah); references federal misconduct standards and NIH policy","publishedAt":"October 1, 2026 (U Research resources post)","publicationDate":"2026-10-01","eventDate":null,"sourceName":"University of Utah Office of the Vice President for Research","sourceLabel":"Institutional research-integrity guidance","sourceUrl":"https://www.research.utah.edu/resources-opportunities/generative-ai-and-the-research-record/","evidenceClass":"standards-guidance","outcomeClass":"cautionary","topics":["governance-procurement","knowledge-work","accessibility-workforce","operating-model"],"finding":"Utah's research integrity office says research administrators see recurring issues in AI-assisted work: polished output with inaccurate or unsupported claims, hallucinated citations, real citations that do not support adjacent claims, unattributed content, and data invented or modified to improve accuracy or reach statistical significance. It reports that graduate students and postdocs are not receiving enough mentoring on AI use and that faculty find colleagues' materials cannot be blindly trusted. It maps AI concerns to fabrication, falsification and plagiarism, states that a tool does not remove a researcher's responsibility, cites NIH NOT-OD-25-132 (applications substantially developed by AI not treated as original; possible misconduct referral and award consequences), and recommends lab-specific AI-use expectations, disclosure practices and four pre-submission verification questions. No incident counts are given.","sledRelevance":"Grounds the research-integrity risk of AI tools in what a public R1's administrators report seeing in grant proposals, IRB applications, dissertations and manuscripts, and shows the institutional response is education and verification rather than prohibition.","evidence":"Opened the University of Utah U Research page via HTTPS (curl, main content extracted; 'Published: October 1, 2026'). Observations are the office's qualitative account; no data, case counts or evaluation.","architectureImplications":"Limited direct architecture content; implies research records systems and lab workflows should preserve source and context for AI-assisted results so claims can be traced to evidence.","governanceImplications":"Interpretation: research offices should require lab-level AI-use expectations, disclosure statements and pre-submission verification for high-stakes documents, aligned with funder rules such as NIH NOT-OD-25-132.","securityPrivacyImplications":"Not addressed directly beyond integrity; IRB applications and unpublished data entering AI tools also raise confidentiality questions campuses must cover in separate guidance.","caveats":"Qualitative guidance from one institution; no frequency or severity data; page contains minor typos; published 1 Oct, before the prior run, but not previously archived and included as current administrative evidence that pairs with this edition's 4 Oct evaluation studies.","streamIds":["research"],"roles":{"sales":"Sales framing: research offices are seeing AI-assisted errors reach grant proposals, IRB applications and manuscripts, and their first response is verification and mentoring, not bans. Utah's integrity office lists hallucinated and unsupportive citations and altered data as recurring issues. A bounded offer is a research-integrity enablement package: lab AI-use templates, disclosure language and a pre-submission verification checklist. Do not claim reduced misconduct; the guidance reports no incident data.","engineering":"Engineering framing: make verification easy inside existing research tools. Utah's checklist asks whether each claim can be verified against evidence, whether cited works support statements, and whether source and context are preserved. Technical support could add citation-checking against original sources, provenance capture for AI-assisted text and data, and disclosure fields in proposal and IRB systems. Treat AI checks as aids only; the guidance says they do not replace review against original evidence.","delivery":"Delivery framing: roll out AI-use expectations lab by lab with named owners. The research integrity officer owns guidance and training, department chairs and PIs own lab-specific rules, and sponsored programs owns funder alignment such as NIH NOT-OD-25-132. Measure training reach and disclosure completeness rather than claiming fewer incidents. Interpretation: Utah's page is qualitative; the acceptance criteria below are proposed and would need local baselines."},"roleDetails":{"sales":{"customerProblem":"AI-assisted content with unsupported claims, bad citations or altered data is entering grant proposals, IRB applications, dissertations and manuscripts, while trainees lack mentoring on appropriate use.","stakeholders":["Research integrity officer","Vice president for research","Sponsored programs office","Graduate school and postdoctoral affairs","Department chairs and PIs"],"discoveryQuestions":["Do labs have written AI-use expectations and disclosure practices?","How are proposals checked against funder AI rules such as NIH NOT-OD-25-132?","What mentoring on AI use do graduate students and postdocs receive?"],"valueHypothesis":"Shared templates, training and verification checklists can reduce the chance that unsupported AI-assisted content enters the research record; the source provides no measured effect.","potentialEngagement":"Five-week research-integrity enablement: lab AI-use policy template, disclosure language, verification checklist and train-the-trainer sessions for two pilot departments.","unsupportedClaims":["Reduced research-misconduct rates","Reliable automated detection of AI misuse","That AI reference checks replace human verification"]},"engineering":{"fit":"Universities with electronic research administration, IRB and thesis systems where AI-assisted content is submitted.","architectureIntegration":"Disclosure fields in proposal, IRB and thesis workflows; citation-verification tools that check references against original sources; provenance capture for AI-assisted analyses in lab records.","prerequisites":["Institutional AI guidelines (ai.utah.edu equivalent)","Research administration system configurability","Library or integrity staff for citation checks","Funder and publisher rule inventory"],"constraints":["Requirements differ by discipline, funder, sponsor and journal","AI checks are aids, not proof","No incident baseline","Faculty autonomy over lab practices"],"security":"Ensure AI tools used for checking do not expose unpublished data or IRB materials, and restrict confidential review content per funder rules.","proofOfValue":["Audit a sample of submissions for citation support before and after checklist adoption","Measure disclosure completion in proposal workflows","Track trainee completion of AI-use mentoring","Test citation tools on known-bad references"]},"delivery":{"work":"Publish lab AI-use templates, add disclosure prompts to submission workflows, run mentoring sessions for trainees and faculty, and set up a consult channel with the integrity office.","dependencies":["Research integrity office capacity","Sponsored programs funder-rule mapping","Graduate school training slots","Department adoption"],"ownership":"Research integrity officer owns guidance and education; PIs own lab expectations; sponsored programs owns funder compliance.","skillsAdoption":"Mentor graduate students and postdocs on verification, attribution and disclosure, and coach faculty to review collaborators' AI-assisted materials.","governanceCheckpoints":["Lab AI-use expectations approved by department","Pre-submission verification for proposals and IRB applications","Funder-rule check for NIH and others","Periodic review of reported AI-related integrity issues"],"acceptanceCriteria":["Proposed: written AI-use expectations in pilot labs","Proposed: disclosure field completed on all pilot-department proposals","Proposed: trainee mentoring completion tracked each semester","Proposed: citation-support audit run on a sample each term"],"risks":["Fabricated or altered data entering the record","Misconduct referrals from undisclosed AI use","Uneven adoption across labs","Over-reliance on AI checking tools"]}},"retrievedAt":"2026-10-05T22:08:39-05:00","enrichedAt":"2026-10-05T22:12:00-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Identifies a mentoring gap for graduate students and postdocs; training must reach trainees and faculty across career stages.","procurementImplications":"Campus AI tools used in research should support disclosure and source preservation; citation-checking tools should be evaluated against original sources.","operatingModelImplications":"Research integrity offices shift toward proactive AI-use education and consultation alongside misconduct review.","sourceVerification":{"openedUrl":"https://www.research.utah.edu/resources-opportunities/generative-ai-and-the-research-record/","referenceExcerpt":"A tool does not remove a researcher’s responsibility for a statement, a citation, or a result.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}