{"resourceId":"notre-dame-learning-student-faculty-ai-summit-qualitative-findings-20261028","versions":[{"version":"external-ec59d49726ba1ed639b6ee01033933e8f34be4b2d77549b010495e21279f0ee2","resource":{"id":"notre-dame-learning-student-faculty-ai-summit-qualitative-findings-20261028","title":"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","organization":"University of Notre Dame (Office of the Provost; Notre Dame Learning Lab for AI in Teaching and Learning)","sector":"Higher education teaching, assessment design and student AI guidance","geography":"Notre Dame, Indiana, United States","publishedAt":"Notre Dame Learning news, 28 October 2026","publicationDate":"2026-10-28","eventDate":"2025-11-14","sourceName":"Notre Dame Learning, University of Notre Dame","sourceLabel":"University teaching-center report summary","sourceUrl":"https://learning.nd.edu/news/what-faculty-and-students-said-about-learning-in-the-age-of-ai/","evidenceClass":"academic-research","outcomeClass":"emerging","topics":["knowledge-work","accessibility-workforce","governance-procurement"],"finding":"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.","sledRelevance":"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":"Qualitative analysis of summit outputs by trained coders; summary only, no full methods.","architectureImplications":"Not a technology finding; interpretation: AI guidance should be deliverable per course and assignment within the LMS.","governanceImplications":"Calls for course- and assignment-specific AI policies and ongoing faculty-student dialogue.","securityPrivacyImplications":"Not addressed.","caveats":"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.","streamIds":["student-success"],"roles":{"sales":"Sales framing: Notre Dame's findings frame AI misuse as a symptom of workload, grade pressure and unclear purpose, not just a policing problem. That gives provosts and teaching centres a student-success angle on AI policy. A bounded engagement could turn these priorities into assignment-level AI guidance templates and a faculty development plan. Do not claim measured effects; this is a qualitative summary from self-selected participants at one institution, published nearly a year after the summit.","engineering":"Engineering framing: technology fit is modest. The practical need is a way to publish assignment-level AI guidance where students see it, inside LMS assignment pages, and to support scaffolded drafts and frequent low-stakes assessment. Check LMS templating for AI-use statements and staged submissions. Proof of value would show that assignment-level guidance is consistently visible to students across a pilot department and that staged workflows do not add unmanageable overhead for instructors.","delivery":"Delivery framing: delivery should translate summit priorities into a pilot: assignment-level AI guidance templates, faculty development time, and scaffolded assessment redesign in volunteer courses, with students as partners. Measure whether students report clearer expectations and whether workload changes. Use the planned November follow-up session and spring partnership programme as checkpoints, and publish the coding approach so other units can judge the evidence."},"roleDetails":{"sales":{"customerProblem":"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"],"discoveryQuestions":["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?"],"valueHypothesis":"Interpretation: assignment-level guidance and scaffolded assessment could reduce misuse driven by pressure and confusion.","potentialEngagement":"Assignment-level AI guidance templates and faculty development plan piloted with student partners.","unsupportedClaims":["That low-stakes assessment reduces AI misuse","That findings generalise beyond self-selected participants","Any measured learning effect"]},"engineering":{"fit":"Institutions able to template AI-use statements and staged submissions in their LMS.","architectureIntegration":"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.","proofOfValue":["Guidance visible on all pilot assignments","Student survey on clarity of expectations","Instructor overhead tracked"]},"delivery":{"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.","skillsAdoption":"Faculty AI academy sessions; student AI literacy guidance.","governanceCheckpoints":["November follow-up session","Spring partnership pilot review","Publication of methods"],"acceptanceCriteria":["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"]}},"retrievedAt":"2026-10-30T03:20:59Z","enrichedAt":"2026-10-30T03:25:23Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Faculty need time, training and discipline-specific opportunities to redesign courses; students cite workload pressure.","procurementImplications":"Not addressed.","operatingModelImplications":"Teaching center programmes (AI academy, mini-grants, student partnership pilot) are the delivery vehicle.","sourceVerification":{"openedUrl":"https://learning.nd.edu/news/what-faculty-and-students-said-about-learning-in-the-age-of-ai/","referenceExcerpt":"Their work and discussions generated more than 1,880 pieces of data, which were analyzed by a team of six trained qualitative coders.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}