[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82598-en":3,"doc-seo-82598-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82598,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Beyond Detection: Redesigning Assessment and Governance of Generative AI at Universidad Politécnica de Madrid (UPM)","Universities worldwide have adopted generative AI (GenAI) in markedly different ways, but many responses remain defensive, centering debate on detection, plagiarism, academic integrity, and presumed declines in student effort. This paper argues detection-focused policies fail because generated text is increasingly indistinguishable and detectors produce unreliable misclassifications. Instead, it proposes course-by-course GenAI rules, authentic interdisciplinary assessment redesign, and an AI literacy program positioning students as critical co-creators. At university scale, adoption must jointly address organisational, technical, operational, legal, and economic constraints. The Universidad Politécnica de Madrid (UPM) develops a six-dimension sustainable policy framework.","BEYOND DETECTION: REDESIGNING ASSESSMENT AND GOVERNANCE OF GENERATIVE AI AT THE UNIVERSIDAD POLITÉCNICA DE MADRID (UPM)  \nJ. Díaz, S. Linio, F. Pescador, D. Martin-Fabiani  \nUniversidad Politécnica de Madrid (SPAIN)  \nAbstract  \nUniversities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far, the dominant reaction has been defensive, this is, most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort, prioritizing basic training for academic staff over students. Other group of pioneering universities is doing the opposite, pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold. This paper sides with that second view. Obsessing about detection is a dead end, since generated text is increasingly hard to distinguish from human writing, and detectors still misfire too often to be trusted. What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use, redesign assessment toward authentic and interdisciplinary assessment that fosters critical thinking and learner autonomy, and build a serious AIliteracy programme that treats students as critical co-creators rather than passive users. The challenge, though, is not only pedagogical. Adoption at university scale also raises organisational, technical, operational, legal and economic questions that have to be solved together. In this context, the Universidad Politécnica de Madrid (UPM) is developing a strategic and sustainable AI policy and adoption framework structured around six dimensions, in which AI functions as an enabler of student autonomy and pedagogical innovation rather than as a threat to be policed.  \nKeywords: Generative Artificial Intelligence, Higher Education, Digital Governance, AI Literacy, Educational Innovation.  \n1 INTRODUCTION  \nThe release of ChatGPT in late 2022 was a turning point for higher education. In a matter of months, generative artificial intelligence (GenAI) tools became broadly accessible to students, faculty and administrative staff, opening an unprecedented debate about academic integrity, the future of assessment and the very nature of university nature [1], [2] . Institutional responses across the world have varied widely, from outright prohibitions to early experimentation with institutional licences, adhoc guidelines and dedicated governance bodies [3], [4] .  \nDespite this variety, a common pattern stands out. Most universities have started from a defensive position, treating GenAI primarily as a threat to academic integrity rather than as an opportunity torethink teaching and learning [5] . Effort tends to concentrate on training academic staff in basic prompt techniques, piloting AI-detection tools, and issuing rules that explicitly or in practice discourage student use. This stance is reinforced by genuine concerns that students might rely on GenAI to bypass cognitive effort, eroding skills such as writing, reasoning and problem solving [6], [7] .  \nThe detect-and-sanction approach is increasingly hard to sustain. AI-text detectors still produce high false-positive rates and tend to flag non-native English speakers and certain writing styles disproportionately as machine-generated [8] . The rapid evolution of large language models (LLMs) is also making generated text increasingly indistinguishable from human writing, so detection becomes a moving target. Restricting access does not actually eliminate use; it just pushes it underground, away from the educational conversation in which it could be most productive [9] .  \nOur argument is that universities have to move beyond detection towards a joined-up redesign of assessment practices and an institutional way of governing GenAI. We propose a strategic and sustainable AI policy and adoption framework organised around six dimensions: (1) pedagogy and curricul","cbCaisYMCo3zMNLn","https://ap.wps.com/l/cbCaisYMCo3zMNLn","pdf",512099,1,7,"English","en",105,"# Introduction\n## From defensive detection to adoption\n## Limits of detect-and-sanction\n# Methodology & Results\n## Institutional benchmarking\n## Participatory design","[{\"question\":\"Why does the paper argue that “detection” is a dead end for GenAI in universities?\",\"answer\":\"Generated text is increasingly hard to distinguish from human writing, and AI-text detectors still produce high false positives. Detectors also misfire in ways that undermine trust and fairness.\"},{\"question\":\"What alternative approach does the paper propose universities should adopt?\",\"answer\":\"It recommends coordinated, course-by-course rules for GenAI use, redesigning assessment toward authentic interdisciplinary tasks, and implementing an AI literacy program that treats students as critical co-creators rather than passive users.\"},{\"question\":\"What kinds of issues must be solved alongside pedagogical changes for GenAI adoption at scale?\",\"answer\":\"The paper highlights that adoption raises organisational, technical, operational, legal, and economic questions. These dimensions must be addressed together rather than in isolation.\"}]",1784181722,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"beyond-detection-redesigning-assessment-and-governance-of-generative-ai-at-universidad-politecnica-de-madrid-upm","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/beyond-detection-redesigning-assessment-and-governance-of-generative-ai-at-universidad-politecnica-de-madrid-upm/82598/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does the paper argue that “detection” is a dead end for GenAI in universities?","Question",{"text":75,"@type":76},"Generated text is increasingly hard to distinguish from human writing, and AI-text detectors still produce high false positives. Detectors also misfire in ways that undermine trust and fairness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What alternative approach does the paper propose universities should adopt?",{"text":80,"@type":76},"It recommends coordinated, course-by-course rules for GenAI use, redesigning assessment toward authentic interdisciplinary tasks, and implementing an AI literacy program that treats students as critical co-creators rather than passive users.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of issues must be solved alongside pedagogical changes for GenAI adoption at scale?",{"text":84,"@type":76},"The paper highlights that adoption raises organisational, technical, operational, legal, and economic questions. 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