{"resourceId":"michigan-daily-senate-assembly-faculty-concerns-ai-grading-consent-20261028","versions":[{"version":"external-9efe1d6239561360e186f25a6f540a261e3cbd53cb12b74abf170c93ab063d35","resource":{"id":"michigan-daily-senate-assembly-faculty-concerns-ai-grading-consent-20261028","title":"University of Michigan Senate Assembly: teaching center describes faculty shift from teaching to policing AI use, and faculty object to AI grading without student consent","organization":"University of Michigan Senate Assembly and Center for Research on Learning and Teaching (CRLT)","sector":"Higher education teaching, assessment and faculty governance","geography":"Ann Arbor and Dearborn, Michigan, United States","publishedAt":"The Michigan Daily, 28 October 2026","publicationDate":"2026-10-28","eventDate":null,"sourceName":"The Michigan Daily","sourceLabel":"Student newspaper reporting","sourceUrl":"https://www.michigandaily.com/news/academics/senate-assembly-discusses-student-faculty-policies-and-artificial-intelligence/","evidenceClass":"independent-reporting","outcomeClass":"cautionary","topics":["governance-procurement","data-security"],"finding":"At a University of Michigan Senate Assembly meeting held the Monday before publication, following the university's finalised guidelines for responsible and ethical AI use, a CRLT associate director said faculty face a deeply troubling transition from teaching and assessing students to policing and surveying their AI use. A former Senate Advisory Committee chair said faculty reliance on AI diminishes the need for specialised expertise and called it injurious to evaluating student work. A Dearborn psychology professor said grading with AI may require student consent because submitted work is students' intellectual property, and said she does not want assignments graded by AI.","sledRelevance":"Faculty governance is now raising consent and expertise objections to AI grading, a constraint on AI feedback and assessment tools aimed at student success.","evidence":"Reported statements at a faculty governance meeting.","architectureImplications":"Interpretation: AI grading tools must show where student work is processed and whether it leaves institutional control.","governanceImplications":"Shared governance may require consent or disclosure before student work is processed by AI for grading.","securityPrivacyImplications":"Concern that submitting student work to AI exposes intellectual property; the article does not say which tools were meant.","caveats":"Opinions of individual faculty; the reporter paraphrases some statements; no information on the content of the finalised guidelines.","streamIds":["student-success"],"roles":{"sales":"Sales framing: Michigan faculty governance is voicing objections to AI grading on consent, intellectual property and expertise grounds, and the teaching center describes faculty shifting from teaching to policing AI use. Anyone proposing AI feedback or grading tools should expect these questions. A bounded engagement could draft a consent and disclosure approach for AI-assisted feedback with faculty governance. Do not present AI grading as uncontroversial or claim it is accurate; the meeting produced no data.","engineering":"Engineering framing: the technical issue is data flow. Before any AI grading or feedback pilot, document where student submissions are processed, whether content is retained or used for training, and whether processing can stay in an institutional tenant. Proof of value should include a data-flow diagram faculty can review, a consent or opt-out mechanism tested in the LMS, and an audit trail showing which submissions were processed by AI.","delivery":"Delivery framing: delivery has to run through shared governance. Work includes presenting AI grading proposals to faculty bodies, agreeing when student consent or notice is required, defining instructor review of AI-generated marks, and supporting faculty who feel pushed into surveillance roles. Pilot only in courses whose instructors opt in, and report back to the governance body on accuracy, student reaction and workload before any broader use."},"roleDetails":{"sales":{"customerProblem":"Institutions considering AI feedback or grading face faculty objections over consent, intellectual property and expertise.","stakeholders":["Faculty senate leadership","Teaching and learning center","Provost office","Student government","Privacy officer"],"discoveryQuestions":["Does your AI guidance address grading or feedback on student work?","Would students be told or asked before their work is processed by AI?","Which faculty bodies must review an AI grading pilot?"],"valueHypothesis":"Interpretation: a consent and disclosure framework agreed with faculty governance could make AI feedback pilots possible without eroding trust.","potentialEngagement":"Facilitated policy drafting on AI-assisted grading consent, disclosure and instructor review.","unsupportedClaims":["That AI grading is accurate or fair","That faculty broadly oppose or support AI grading","Any time savings"]},"engineering":{"fit":"Institutions evaluating AI feedback or grading features in LMS or third-party tools.","architectureIntegration":"Documented data flow from LMS submission to the AI service, with consent flags and audit logging.","prerequisites":["Data-flow documentation from vendors","Consent or notice mechanism in the LMS","Institutional tenant or no-retention terms"],"constraints":["Faculty governance approval","Student intellectual property concerns","Single-meeting evidence"],"security":"Treat submitted student work as protected; restrict processing to approved services with no training use.","proofOfValue":["Faculty-reviewed data-flow diagram","Working opt-out or consent flag","Audit log of AI-processed submissions"]},"delivery":{"work":"Take AI grading proposals through faculty governance, define consent and instructor review, and pilot only with opt-in instructors.","dependencies":["Faculty senate agenda time","Privacy office review","Vendor data terms"],"ownership":"Provost office with faculty governance.","skillsAdoption":"Faculty guidance on reviewing AI-generated feedback; support for faculty uneasy with monitoring roles.","governanceCheckpoints":["Faculty governance review","Privacy and IP review","Post-pilot report to the governance body"],"acceptanceCriteria":["Proposed: consent or notice policy approved before any pilot","Proposed: every AI-generated mark reviewed by an instructor","Proposed: pilot report covers accuracy, student reaction and workload"],"risks":["Loss of faculty trust","Student IP complaints","Policy drift between departments"]}},"retrievedAt":"2026-10-29T03:20:51Z","enrichedAt":"2026-10-29T03:24:23Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Faculty describe workload and role strain from monitoring student AI use.","procurementImplications":"Interpretation: procurement of AI feedback or grading tools should include data-use terms that can be shown to faculty and students.","operatingModelImplications":"Interpretation: AI grading pilots need faculty governance review, not only IT approval.","sourceVerification":{"openedUrl":"https://www.michigandaily.com/news/academics/senate-assembly-discusses-student-faculty-policies-and-artificial-intelligence/","referenceExcerpt":"said faculty reliance on AI diminishes the need for specialized expertise.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}