AI and Pedagogy Brownbag Series
Offered as a partnership between the Center for Integrated Professional Development’s Scholarly Teaching unit and the Adaptive Edge Institute, the AI & Pedagogy Brownbag Series is a relaxed, lunchtime take on our “Gladly We Learn, Teach, and Research with AI” workshops. Each of the six sessions is anchored to a dimension of the Framework for Inclusive Teaching Excellence (FITE) and invites an honest look at how generative AI could be used, where it can't be used, and when it must not be used. Sessions run over the lunch hour, so bring your lunch and a question or two. Each session is self-contained—come to the ones that interest you or attend all six. Registration is required per session.
Eligible Participants: Tenure-Track Faculty, Non-Tenure Track Faculty and Course Instructors, Graduate Students, and Staff (Civil Service and AP).
Session 1
AI and How Students Learn
Register for this session by Thursday, September 3
Generative AI can clarify complex ideas, model expert thinking, and break big tasks into manageable steps—but it can just as easily short-circuit the productive struggle that learning requires. This session looks at AI through the lens of the science of learning: working memory and cognitive load, attention, metacognition, and the difference between deep and shallow processing. Together we'll consider when handing work to AI supports understanding and when it quietly replaces it, and you'll leave with concrete ways to use AI to scaffold—rather than skip—the thinking you want students to do. Registration is required.
Session 2
Designing Courses Around AI Fluency and Literacy
Register for this session by Thursday, September 17
If students use AI, they must do so thoughtfully and well. This session helps you design a course toward that goal, starting with clear, usable definitions of AI “fluency” and “literacy” and the important distinction between the two: literacy is the critical understanding every student needs, while fluency involves hands-on use you may or may not choose to permit. From there, we'll turn to course design—how learning outcomes, activities, and the overall arc of a course can build students’ judgment about when to rely on AI, when to question it, and when to set it aside. Because students meet these tools everywhere, this work matters even in classrooms where AI use isn't allowed, so skeptics are genuinely welcome. You'll leave with a working definition you can adapt and a set of design questions for building fluency and literacy into your course from the outcomes down. Registration is required.
Session 3
Evidence-Based Teaching in the Age of AI
Register for this session by Thursday, October 1
What does the evidence actually say about teaching well when AI is in the room? This session surveys a range of evidence-based strategies—problem-based and project-based learning, “naked teaching,” slow teaching, and other approaches—and considers how each can be used either to bring AI meaningfully into student work or to keep it out when the learning depends on unassisted effort. We'll also take a brief tour of the current state of the scholarship in SoTL and educational research, separating what the evidence supports from what remains speculative. You'll leave with a menu of research-informed techniques and a clearer sense of where the field currently stands. Registration is required.
Session 4
Human-Centered AI and Inclusive Teaching
Register for this session by Thursday, October 15
AI is changing higher education quickly, but students still need inclusive, relational learning spaces where their voices and identities matter. This session explores how to bring AI into a course without eroding the human dimensions of teaching—trust, belonging, and shared meaning-making. We'll discuss transparent ways to communicate AI expectations, approaches that keep students’ lived experiences at the center, and simple “AI use norms” you can adapt for your own classroom to support academic integrity and belonging at once. Registration is required.
Session 5
AI, Feedback, and Assessment
Register for this session by Thursday, October 29
This session examines where AI can responsibly support the feedback and assessment loop—drafting and refining rubrics, generating practice questions, and helping students act on feedback—and where it should stay out of the process entirely. We'll weigh the ethical and data-security considerations of putting student work into AI tools, clarify what proper disclosure looks like, and identify a few low-risk, high-value ways AI can lighten the load without outsourcing your professional judgment. Registration is required.
Session 6
Reflecting on Teaching with AI
Register for this session by Thursday, November 12
In our closing session, we'll look at how AI tools can support instructors’ own reflective practice—surfacing patterns in student work, summarizing discussion themes, and prompting questions that lead to iterative course improvement. We'll consider how AI-supported reflection can help identify equity gaps while respecting student privacy, and you'll leave with a light-touch routine for using AI to reflect on your teaching across the semester. Registration is required.
