From the Student Success edition of October 28, 2026
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
University of Michigan Senate Assembly and Center for Research on Learning and Teaching (CRLT) · Higher education teaching, assessment and faculty governance · Ann Arbor and Dearborn, Michigan, United States
- Publisher
- The Michigan Daily
- Original publication
- The Michigan Daily, 28 October 2026
- Source retrieved
- 2026-10-29
What happened
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.
Why it matters
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 and measured results
Reported statements at a faculty governance meeting.
Limitations and uncertainty
Opinions of individual faculty; the reporter paraphrases some statements; no information on the content of the finalised guidelines.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-29; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- 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
- Discovery questions
- 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?
- Value hypothesis
- Interpretation: a consent and disclosure framework agreed with faculty governance could make AI feedback pilots possible without eroding trust.
- Potential engagement
- Facilitated policy drafting on AI-assisted grading consent, disclosure and instructor review.
- Unsupported claims
- That AI grading is accurate or fair
- That faculty broadly oppose or support AI grading
- Any time savings
Pre-sales engineering
Role takeaway
- Fit
- Institutions evaluating AI feedback or grading features in LMS or third-party tools.
- Architecture and integration
- 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.
- Proof of value
- Faculty-reviewed data-flow diagram
- Working opt-out or consent flag
- Audit log of AI-processed submissions
Delivery
Role takeaway
- 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.
- Skills and adoption
- Faculty guidance on reviewing AI-generated feedback; support for faculty uneasy with monitoring roles.
- Governance checkpoints
- Faculty governance review
- Privacy and IP review
- Post-pilot report to the governance body
- Acceptance criteria
- 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
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
AI grading tools must show where student work is processed and whether it leaves institutional control.
Governance
Who approves, reviews and stays accountable for outcomes?
Shared governance may require consent or disclosure before student work is processed by AI for grading.
Security and privacy
What data, permissions and controls need testing?
Concern that submitting student work to AI exposes intellectual property; the article does not say which tools were meant.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Faculty describe workload and role strain from monitoring student AI use.
Procurement
What should contracts, pricing and exit terms secure?
Procurement of AI feedback or grading tools should include data-use terms that can be shown to faculty and students.
Operating model
Which teams own the service once it runs?
AI grading pilots need faculty governance review, not only IT approval.
Publication history
- 2026-10-28Student Success · Issue 514 resources
Stable resource ID: michigan-daily-senate-assembly-faculty-concerns-ai-grading-consent-20261028