Preconference Workshops
The Teaching Professor Conference offers a selection of 2.5 hour preconference workshops to further enrich your conference experience. The cost is $319 for each preconference workshop. The workshops are held Friday, June 5 in the morning and afternoon before the conference begins.
Enrollment is offered during conference registration.
If you have already registered for the conference and would like to add a workshop to your registration, call 608-246-3590 to enroll.
Check back soon to see who will be taking the stage!
Students Have AI—Now What? Designing for How Learning Actually Works
Friday, June 5 | 9:00 - 11:30 am
The rise of AI tools has amplified a longstanding challenge: students often rely on strategies that feel productive but don’t actually promote lasting learning. This workshop reframes the conversation around what we know works from learning science—approaches that make content stick while reducing busywork for you and your students.
When courses are designed around evidence-based learning principles, students develop cognitive skills that AI cannot replicate: deep understanding, critical thinking, and knowledge transfer. These same principles create more efficient teaching by focusing student effort where it matters most…on learning! In this workshop, we’ll explore research-backed strategies that help students move from surface-level engagement to deeper learning. We’ll model these approaches so you experience them as a learner before implementing them as an instructor. Attendees will leave with practical, adaptable course design elements requiring no full overhaul—just concrete activities, scaffolded assignments, and transparency tools. The result: Students who learn more deeply, retain content longer, and develop metacognitive skills to become expert learners in your field and beyond.
Michelle Blank
Jeremy Rentz
AI Didn't Just Change Student Work; It Changed Ours, Too: What Faculty Can Do Next
Friday, June 5 | 9:00 - 11:30 am
Generative AI has made it possible for students to produce fluent, polished work with minimal apparent effort. In response, instructors are asking how to stop AI use or, alternatively, how to integrate it responsibly. This workshop argues that both approaches miss a central problem: What the existence of AI is doing to faculty work itself.
Teaching feels harder, more uncertain, and more exhausting than it did just a few years ago. We now find ourselves continually redesigning courses to try to grab students by their brains, constantly rebuilding assignments and assessments that no longer work, and monitoring authorship to determine whether what we receive is human work or a machine artifact. We often must do this without clear guidance or institutional support. The result is not just confusion but instead sustained fatigue. This workshop reframes AI as not a student problem but instead as a work redesign problem. Attendees will identify the specific ways that AI is reshaping our instructional labor, why common responses to AI increase workload, and which practical strategies we might use to reclaim teaching. To accomplish these goals, we will workshop a framework for analyzing the primary new challenges we face in our work and determining strategies for addressing them, with or without incorporating AI into our classes. The focus of the workshop is on making strategic decisions that will protect the core of our instructional work, without creating an unsustainable workload.
Claire Major
Scaling Up with Universal Design for Learning…and How to Get Colleagues to Join You
Friday, June 5 | 1:00 - 3:30 pm
To help make educational materials and practices inclusive and useful for all learners, this interactive workshop radically reflects on how instructors and designers can adopt Universal Design for Learning (UDL) in order to create learning interactions that provide students with more time for study and practice in their busy days: broaden our focus beyond learners with disabilities and toward a larger ease-of-use/general-inclusion framework.
Our workshop will contain three scaffolded elements: an overview of UDL, how to scale up UDL efforts beyond individual actions, and how to talk with colleagues in order to establish UDL communities of practice. Together, we’ll work through beginner, practitioner, and advanced-level UDL applications.
Thomas J. Tobin
Between Harm Reduction and Hope: Analog Inspiration and Human-Centered AI
Friday, June 5 | 1:00 - 3:30 pm
This interactive workshop reframes generative AI’s impact on education around the human values, skills, and concerns that matter most to us as educators.
Guided by Analog Inspiration—a card deck featuring 47 concepts ranging from Accessibility to Wonder—we’ll engage in both analog play and digital experimentation to meet this morally- and logistically-complicated moment. After introducing a framework for human-centered AI pedagogy, we’ll use the cards to reflect on our own values, discuss AI’s ethical implications, approach difficult AI-related teaching scenarios, and develop assessment strategies to proactively disincentivize “cheating”—we’ll also interrogate what we mean by that term. We’ll then move into guided individual AI experimentation, where you will have a chance to apply card concepts to your own specific educational contexts. Together, we’ll break and remake our assignments, audit our educational materials to add more care and build intrinsic motivation, and develop practical strategies that we can take with us after the conference.
Carter Moulton
The Science of Learning and the Humanity of Teaching
Friday, June 5 | 1:00 - 3:30 pm
In the last couple of decades, faculty have been invited and sometimes urged to “follow the science” of learning. But what does it actually mean to turn to science in the work of teaching? What can the neurobiology and cognitive science of learning help us see more clearly, and what can they never fully capture?
In this pre-conference workshop, we will explore the productive tension between two truths: learning science offers powerful, practical insights, and yet every scientific claim is situated—bounded by time, methods, models, and the particular participants a study did (and did not) include. Our classrooms are not laboratories; they are lived environments shaped by relationships, identity, attention, emotion, culture, and the unpredictable texture of human experience.
If we treat research findings as prescriptions, we risk teaching to an “average” learner who may not exist, or we may miss how neurodivergence and context reshape what is possible in the moment. We will examine a small set of core learning principles that have shown durability across decades of research while also interrogating what we still don’t know, what remains contested, and where our own teaching experiences serve as essential data.
Mays Imad, PhD
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