Investigating GenAI Teaching and Learning Mini‑Grants Program

Sponsored by the Center for Research on Learning and Teaching (CRLT) and the Life Changing Education (LCE) Initiative

Program Overview

The Investigating GenAI Teaching & Learning Mini‑Grants Program supports faculty ranging from the enthusiastic to the curious, cautious, and/or conflicted about the impacts of generative AI on their courses, and who want to learn more. This program provides structured support and funding to investigate how generative AI (or intentional limits on its use) shapes student learning in a specific course or learning experience. Applicants should bring a baseline familiarity with generative AI, but do not need advanced technical expertise or formal AI training. The emphasis is on contributing to emerging, evidence‑informed best practices for GenAI in teaching and learning. Each funded project will receive a $1,500 stipend.

How to Apply

Eligibility and Fit

The competition is open, on the Ann Arbor campus of the University, to all tenured and tenure-track faculty; clinical instructional faculty; lecturers who have continuing appointments and course development responsibilities (i.e., an assignment from the dean, chair, or designee to develop a new course or significantly revise an existing course).

This program is a good fit for faculty who:

  • Are already engaging (or planning to engage) with GenAI-related decisions in their teaching
  • Want structured support to study GenAI's pedagogical implications in their own context
  • Are interested in questions of learning, equity, educational purpose, and student experience

You do not need prior experience with educational research or advanced experience with AI tools. 

Research Questions

Funded projects will typically focus on one primary teaching and learning question, such as:

  • How does generative AI shape student learning, engagement, or skill development in this course?
  • What do equitable, transparent, and pedagogically grounded approaches to GenAI look like in practice?
  • When does intentionally limiting or prohibiting AI use support learning goals?
  • How do AI‑supported and AI‑free approaches compare in a particular assignment, unit, or learning experience?
  • See below for specific examples.

This program is not a technical training program or a tool pilot. CRLT will not provide instruction on how to use specific AI platforms.

Faculty Support

Funded faculty will receive:

  • A $1,500 stipend
  • Access to CRLT resources and support 
  • Access to a cohort of grant recipients to share ideas and offer feedback

What we hope to learn (collectively)

Across projects, CRLT and YLCE aim to develop a clearer understanding of:

  • How different AI approaches affect learning, engagement, and equity across instructional contexts
  • Which practices appear to support students in formative, life‑changing educational experiences
  • Emerging best practices for the pedagogical use (and intentional non‑use) of generative AI

Project reports will be synthesized to inform future CRLT resources and campus guidance.

Participation Requirements

Funded projects are intentionally scoped and supported, typically involving:

  • One course or learning experience
  • Implementation in Fall 2026-Winter 2027 academic year
  • At least two forms of evidence drawn from authentic course activities (e.g., student work, reflections, brief surveys)
  • A short reflective project report (due Spring 2027)

Required milestones include:

  • Attend orientation: May 19, 2026 (12:00–2:00 p.m. in person)
  • Participate in project planning: Spring/Summer 2026
  • Implement project: Fall 2026-Winter 2027
  • Submit final report: May 31, 2027
Example Project Ideas

CRLT looks forward to supporting a wide range of thoughtful, practical‑grounded projects that will help the university better understand how generative AI affects student learning educational experience. The following examples were generated as inspiration for the types of research questions, interventions and data collection methods that could be explored during this fellows program.

AI-Assisted Peer Review in a Writing-Intensive Course

  • Course: Writing in the Humanities
  • Intervention:Students use GenAI to generate initial peer-review comments on draft essays, then refine or critique the AI feedback.
  • Research Question: How does AI-assisted peer review influence the quality of student revisions and final writing outcomes?
  • Data Sources: First drafts, revised drafts, AI feedback logs, student surveys.

Comparing AI-Free vs. AI-Supported Problem Solving

  • Course: Thermodynamics
  • Intervention: Half the problem sets explicitly prohibit AI tools; the other half incorporate structured AI use (e.g., having students critique AI-generated solutions).
  • Research Question: How do AI-free vs. AI-structured problem-solving tasks impact conceptual understanding and error patterns?
  • Data Sources: Homework artifacts, Canvas analytics, error analysis, short reflections.

Transparent AI Use in Large Intro Courses

  • Course: Introduction to Psych
  • Intervention: Students complete weekly “AI Use Logs” where they document if/how AI supported studying, summarizing, or practice testing.
  • Research Question: How does requiring transparency about AI use shape students’ study behaviors and exam performance?
  • Data Sources: AI-use logs, survey data, exam scores, focus groups.

AI-Free Ethnography in a Methods Course

  • Course: Ethnographic Methods
  • Intervention: The instructor institutes an “AI-Free Fieldwork Notebook” requirement and provides structured alternatives to AI (e.g., observation guides, scaffolds).
  • Research Question: What is the impact of intentional AI abstention on students’ qualitative observation and analytic skills?
  • Data Sources: Field notes, coded artifacts, reflective essays, instructor observations.

AI-Generated Drafts vs. Human-Only Drafting in a Capstone

  • Course: School of Information Senior Capstone
  • Intervention:In early project phases, some student teams use GenAI to generate initial problem statements or personas; other teams draft manually.
  • Research Question: How does beginning with AI-generated drafts affect team creativity, depth of inquiry, and final project quality?
  • Data Sources: Draft artifacts, final projects, peer evaluations, team interviews.

Impact of AI on Preparation for PhD Comprehensive Exams

  • Course: School of Education Comprehensive Qualifying Exam
  • Intervention: In preparation for comprehensive exams, students have the option to use GenAI to create initial drafts. 
  • Research Question: How does beginning with AI-generated drafts affect team creativity, depth of inquiry, and final project quality?
  • Data Sources: Draft artifacts, final projects, peer evaluations, team interviews.
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