Decision Matrix for Prioritizing Generative AI Risks in Higher Education

Decision Matrix for Prioritizing Generative AI Risks in Higher Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decision Matrix for Prioritizing Generative AI Risks in Higher Education Timothée Lino Adolphe Trinché This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7494968/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Generative AI is rapidly reshaping higher education, yet institutions lack a transparent, replicable way to prioritise risks and allocate mitigation effort. Objective. We propose an Analytic Hierarchy Process (AHP) framework to rank 24 AI-related risks across six criteria relevant to universities. Methods. Following PRISMA 2020 reporting, we systematically screened ~200 records, assessed 42 full texts, and included 29 sources informing the criteria, sub-risks, and severity scales. Pairwise comparisons yielded normalised weights; consistency was checked (CR ≤ 0.10). We release replication files (pairwise matrices, computed weights, PRISMA flow). Results. The top priorities are academic integrity (C1, 29%) and data protection/compliance (C5, 19%). Misinformation-related risks (C3, 12%) and student disengagement/critical thinking (C4, 12%) form a second tier; bias/discrimination (C2, 19%) remains structurally important across equity-sensitive contexts, while transparency/dependency (C6, 8%) completes the profile. Implications. The framework converts strategic concerns into actionable governance: (i) Responsible-Use Charter and exam integrity controls; (ii) by-design DPIAs and data-minimisation; (iii) model cards/datasheets and incident logging aligned with recognised AI risk-management practices; (iv) annual recalibration by discipline. Conclusion. The AHP approach offers a transparent, auditable basis for prioritising AI risks in higher education and for steering policy, investment, and assurance activities. We provide materials to facilitate adoption and adaptation to non-EU contexts. generative artificial intelligence higher education Analytic Hierarchy Process risk governance decision matrix academic integrity data privacy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7494968","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509936129,"identity":"bf73f8f1-863f-44aa-a9a2-1d9585071543","order_by":0,"name":"Timothée Lino Adolphe Trinché","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACZjApAUKMDxgYLGQY2GEiDIwN2HTwgLQcgGhhNgCSPAhDcGlhAGsBq2GTQNKCG9izcycwf9xjkcc/u/lYNU8FSAvzwc+VOyzy5B2YGx9gdRjvBoYDzySKJe4cS7vNcwakhS1Z8uwZiWLDA4zNBji1HJBI3CCRY3Y7t02Cx/4wj4FkY5tE4sYGxjYJ/FryvxXn/gPZwv/5J5FactiYcxtAWnjYwLbMZ8Ch5TDvhgNngFpm3Egzlv5zDOwXM0uQlg3M2P3C3n9244OKA3WJ/TOSH36cUWMjx8De/PhmY1td4vz29ofYQgwEDmAXNjiMQz1uIN9AspZRMApGwSgYngAAGwpaW5KX2MsAAAAASUVORK5CYII=","orcid":"","institution":"Pragma Learning Institute","correspondingAuthor":true,"prefix":"","firstName":"Timothée","middleName":"Lino Adolphe","lastName":"Trinché","suffix":""}],"badges":[],"createdAt":"2025-08-30 11:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7494968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7494968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91988358,"identity":"4a1f9170-6ec9-4aa8-ad53-c4a5746002fe","added_by":"auto","created_at":"2025-09-23 12:20:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":715655,"visible":true,"origin":"","legend":"","description":"","filename":"DecisionMatrixforPrioritizingGenerativeAIRisksinHigherEducation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7494968/v1_covered_30c37bf5-a015-4df0-b724-95671f02b892.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decision Matrix for Prioritizing Generative AI Risks in Higher Education","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"generative artificial intelligence, higher education, Analytic Hierarchy Process, risk governance, decision matrix, academic integrity, data privacy","lastPublishedDoi":"10.21203/rs.3.rs-7494968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7494968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eGenerative AI is rapidly reshaping higher education, yet institutions lack a transparent, replicable way to prioritise risks and allocate mitigation effort. Objective. We propose an Analytic Hierarchy Process (AHP) framework to rank 24 AI-related risks across six criteria relevant to universities. Methods. Following PRISMA 2020 reporting, we systematically screened ~200 records, assessed 42 full texts, and included 29 sources informing the criteria, sub-risks, and severity scales. Pairwise comparisons yielded normalised weights; consistency was checked (CR ≤ 0.10). We release replication files (pairwise matrices, computed weights, PRISMA flow). Results. The top priorities are academic integrity (C1, 29%) and data protection/compliance (C5, 19%). Misinformation-related risks (C3, 12%) and student disengagement/critical thinking (C4, 12%) form a second tier; bias/discrimination (C2, 19%) remains structurally important across equity-sensitive contexts, while transparency/dependency (C6, 8%) completes the profile. Implications. The framework converts strategic concerns into actionable governance: (i) Responsible-Use Charter and exam integrity controls; (ii) by-design DPIAs and data-minimisation; (iii) model cards/datasheets and incident logging aligned with recognised AI risk-management practices; (iv) annual recalibration by discipline. Conclusion. The AHP approach offers a transparent, auditable basis for prioritising AI risks in higher education and for steering policy, investment, and assurance activities. 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