From the Student Success edition of October 29, 2026
Notre Dame publishes qualitative findings from its student-faculty AI summit: 88 participants, 1,880+ data points, priorities on motivation, assignment design and course-specific AI guidance
University of Notre Dame (Office of the Provost; Notre Dame Learning Lab for AI in Teaching and Learning) · Higher education teaching, assessment design and student AI guidance · Notre Dame, Indiana, United States
- Publisher
- Notre Dame Learning, University of Notre Dame
- Original publication
- Notre Dame Learning news, 28 October 2026
- Source retrieved
- 2026-10-30
- Event date
- 2025-11-14
What happened
Notre Dame's summit on 14 November 2025 brought together 43 faculty from 36 units and 45 students from 19 majors; more than 1,880 pieces of data were analysed by six trained qualitative coders. Participants wanted to move from preventing inappropriate AI use toward motivating learning, agreeing AI should complement rather than substitute for student thinking. Students named busywork, overwhelming workload, deadlines and grade pressure as reasons for AI use that undermines learning, and participants suggested frequent low-stakes assessments, scaffolded drafts and revision, and in-class work. Students asked for course- and assignment-specific AI guidance rather than one syllabus statement. Faculty asked for time, training and discipline-specific support. Notre Dame will pilot an AI Student Pedagogical Partnership Program this spring.
Why it matters
Institution-level evidence of what students say drives harmful AI use (workload and grade pressure) and what guidance they want, relevant to student-success policy design.
Evidence and measured results
Qualitative analysis of summit outputs by trained coders; summary only, no full methods.
Limitations and uncertainty
Self-selected participants at one private university; event nearly a year before publication; summary page without full data; published now because findings are being reported ahead of a November follow-up session.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-30; 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 treat AI mainly as an integrity threat while students cite workload, pressure and unclear guidance as drivers of misuse.
- Stakeholders
- Provost
- Teaching and learning center
- Faculty
- Student government
- Academic integrity office
- Discovery questions
- Do your AI policies operate at course or assignment level?
- What do students say drives AI misuse?
- What time and training do faculty get to redesign courses?
- Value hypothesis
- Interpretation: assignment-level guidance and scaffolded assessment could reduce misuse driven by pressure and confusion.
- Potential engagement
- Assignment-level AI guidance templates and faculty development plan piloted with student partners.
- Unsupported claims
- That low-stakes assessment reduces AI misuse
- That findings generalise beyond self-selected participants
- Any measured learning effect
Pre-sales engineering
Role takeaway
- Fit
- Institutions able to template AI-use statements and staged submissions in their LMS.
- Architecture and integration
- LMS assignment templates carrying AI-use guidance; staged submission workflows.
- Prerequisites
- LMS template capability
- Agreed guidance taxonomy
- Faculty development capacity
- Constraints
- Qualitative evidence only
- Single private university
- Event predates publication by nearly a year
- Security
- Minimal; guidance publication only.
- Proof of value
- Guidance visible on all pilot assignments
- Student survey on clarity of expectations
- Instructor overhead tracked
Delivery
Role takeaway
- Work
- Create guidance templates, run faculty development, redesign assessments in volunteer courses with student partners, and gather feedback.
- Dependencies
- Faculty time
- Student partner recruitment
- Teaching center staff
- Ownership
- Teaching and learning center with the provost office.
- Skills and adoption
- Faculty AI academy sessions; student AI literacy guidance.
- Governance checkpoints
- November follow-up session
- Spring partnership pilot review
- Publication of methods
- Acceptance criteria
- Proposed: guidance templates adopted in pilot courses
- Proposed: student clarity ratings collected
- Proposed: workload impact reported
- Risks
- Faculty capacity
- Self-selection bias in pilots
- Guidance fatigue
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?
Not a technology finding; interpretation: AI guidance should be deliverable per course and assignment within the LMS.
Governance
Who approves, reviews and stays accountable for outcomes?
Calls for course- and assignment-specific AI policies and ongoing faculty-student dialogue.
Security and privacy
What data, permissions and controls need testing?
Not addressed.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Faculty need time, training and discipline-specific opportunities to redesign courses; students cite workload pressure.
Procurement
What should contracts, pricing and exit terms secure?
Not addressed.
Operating model
Which teams own the service once it runs?
Teaching center programmes (AI academy, mini-grants, student partnership pilot) are the delivery vehicle.
Publication history
- 2026-10-29Student Success · Issue 525 resources
Stable resource ID: notre-dame-learning-student-faculty-ai-summit-qualitative-findings-20261028