{"resourceId":"the-campus-uws-critical-ai-literacy-over-detection-five-practices-20261030","versions":[{"version":"external-cc49cff7e69d21e8625615a60431ddf66c8c339608d49b341958b3bf8ea9ab68","resource":{"id":"the-campus-uws-critical-ai-literacy-over-detection-five-practices-20261030","title":"University of the West of Scotland academics argue for teaching critical AI literacy instead of detection and bans, setting out five classroom practices","organization":"University of the West of Scotland (authors); Times Higher Education Campus","sector":"Higher education teaching, assessment and AI literacy","geography":"Scotland, United Kingdom","publishedAt":"THE Campus, 30 October 2026","publicationDate":"2026-10-30","eventDate":null,"sourceName":"Times Higher Education (THE Campus)","sourceLabel":"Practitioner feature article","sourceUrl":"https://www.timeshighereducation.com/campus/police-losing-battle-or-teach-students-think-clearly-choice-ours","evidenceClass":"independent-reporting","outcomeClass":"emerging","topics":["knowledge-work","governance-procurement","operating-model"],"finding":"Naeem Ramzan and Shahid Awan of the University of the West of Scotland (THE Campus, 30 October 2026) argue that detection software and prohibition rarely change how students learn, that detection tools are unreliable, and that the students least able to contest a false accusation bear the brunt. They propose teaching critical AI literacy through five practices: make student thinking visible through reasoning, annotated drafts and short oral defences; have students argue with and find failures in AI output; have staff model their own AI use openly; keep rules short, plain and written into each assessment brief; and protect the supervisory relationship from turning into surveillance. They acknowledge this asks more of staff.","sledRelevance":"Connects AI policy to student equity and belonging: detection-led enforcement can fall hardest on students least able to contest accusations.","evidence":"Practitioner argument and recommendations; no measured outcomes.","architectureImplications":"Interpretation: shifts investment from detection tools toward assessment design and staff development.","governanceImplications":"Recommends short, task-level AI rules in each assessment brief and open staff disclosure of AI use.","securityPrivacyImplications":"Warns that supervision tilted toward detection becomes surveillance; no data-handling detail.","caveats":"Opinion piece without evaluation data; UK context; recommendations untested in the article.","streamIds":["student-success"],"roles":{"sales":"Sales framing: this piece gives teaching and learning leaders a practical alternative to detection-led AI enforcement, framed around fairness to students who cannot contest false accusations. A bounded engagement could help a few programmes rewrite assessment briefs with plain task-level AI rules and visible-thinking tasks. Keep claims modest: the article offers practitioner recommendations, not evidence that the five practices improve learning or reduce misconduct. Do not position it as proof that detection tools should be dropped everywhere.","engineering":"Engineering framing: technical fit is light. The shift is away from detection dashboards toward workflows that capture student reasoning, such as annotated draft submissions, version history and short recorded oral defences in the LMS. Check whether existing LMS features already support draft tracking before adding tools. Privacy settings matter if recordings are stored. Proof of value should test whether redesigned briefs reduce student questions about allowed AI use and whether staff can sustain the added workload.","delivery":"Delivery framing: delivery means a staged assessment redesign owned by teaching centres with programme leads. Work includes templates for task-level AI statements, examples of argue-with-the-machine exercises, staff guidance on disclosing their own AI use, and supervisor guidance that keeps tutorials developmental. Track staff time and student clarity on rules. Risks include workload overload and inconsistent adoption across departments. Treat outcomes as hypotheses to be measured, since the piece reports no results."},"roleDetails":{"sales":{"customerProblem":"Detection and bans are not changing how students learn and may harm students least able to contest accusations.","stakeholders":["Teaching and learning center","Programme leads","Academic integrity office","Student union","Dean of students"],"discoveryQuestions":["How many misconduct cases rely mainly on detector scores?","Do assessment briefs state task-level AI rules?","What support do staff have to redesign assessments?"],"valueHypothesis":"Interpretation: clearer task-level rules and visible-thinking assessments may improve fairness and student confidence about AI use.","potentialEngagement":"Assessment brief rewrite and AI literacy task design for two or three programmes over one term.","unsupportedClaims":["Reduced misconduct","Improved learning outcomes","Detection tools have no role","Low staff workload"]},"engineering":{"fit":"Workflow support for drafts, annotations and oral defences rather than new AI tools.","architectureIntegration":"Use LMS version history, annotation and media submission features to capture student reasoning.","prerequisites":["LMS draft and media submission support","Storage rules for recordings","Assessment brief templates"],"constraints":["Opinion-based recommendations","UK context","Staff time limits"],"security":"Recorded oral defences and draft histories need retention limits and access controls.","proofOfValue":["Fewer student queries about allowed AI use","Staff time logged for redesigned tasks","Student survey on rule clarity"]},"delivery":{"work":"Staged redesign of assessment briefs and tasks around critical AI literacy and visible thinking.","dependencies":["Programme lead buy-in","Teaching center capacity","Integrity office alignment"],"ownership":"Teaching and learning center with programme leads.","skillsAdoption":"Staff need examples and time to design argue-with-the-machine tasks and to model their own AI use.","governanceCheckpoints":["Approval of task-level AI statement template","Integrity office review of detection reliance","End-of-term review of workload and clarity"],"acceptanceCriteria":["Proposed: every redesigned brief includes a plain task-level AI statement","Proposed: at least one visible-thinking component per redesigned module","Proposed: student rule-clarity rating improves on baseline"],"risks":["Staff workload overload","Inconsistent adoption","Untested assumptions about learning gains"]}},"retrievedAt":"2026-11-01T03:18:59Z","enrichedAt":"2026-11-01T03:24:15Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Redesign and dialogue-based assessment require more staff time, which the authors acknowledge.","procurementImplications":"Interpretation: questions continued spending on AI detection tools relative to curriculum design support.","operatingModelImplications":"Interpretation: teaching centres would carry the redesign work across disciplines.","peerReview":"unknown","sourceVerification":{"openedUrl":"https://www.timeshighereducation.com/campus/police-losing-battle-or-teach-students-think-clearly-choice-ours","referenceExcerpt":"Detection tools are unreliable, and the students least able to contest a false accusation tend to bear the brunt.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}