By Jeff Rients
If you have been wondering what a thoughtful use of A.I. might look like in your course, five Temple colleagues have some ideas to share with you. Their projects grew out of the Spring 2026 Faculty Learning Community on Teaching and Learning with A.I., and you can now explore their work on the CAT website. Think of the collection as a colleague opening a course folder and talking you through what’s inside. You get an activity and, in several cases, an account of the thinking behind it.
Jennifer Zaylea, the CAT’s Assistant Director of Digital and Emerging Technologies, and I had the privilege of facilitating the group, which brought together faculty from business, English, and public health. A faculty learning community gives colleagues time to investigate shared questions and develop something useful for their teaching. Our questions included how A.I. might support course goals, what students need to learn about its use, and where it might interfere with the learning we want to happen. The resulting materials give us something concrete to examine.
Gary Blau, in the Fox School of Business, used A.I. to develop role play activities for his Critical Skills for Effective Managers course. Students practice delegating work, giving performance feedback, handling conflict, and asking for a raise. In that last scenario, the employee has accomplishments to point to and the manager has a limited budget. Students must work through the conversation (preferably before an actual paycheck is involved). Observers add another perspective to the discussion afterward.
Rob Faunce, in English, combines mindmapping and A.I. to help students prepare for a First Year Writing paper. His activity brings together research, essay planning, and work with Claude. Robb’s activity employs reflection to help students examine the tool’s possibilities and limitations. His account keeps returning to students’ critical thinking and the importance of their own voice. What can the machine contribute, and what does the writer need to bring?
Ryan McKee, in public health, designed a lesson introducing A.I. as a thinking partner for community health workers. Students first plan a client visit themselves, then compare their ideas with A.I. suggestions. They identify useful contributions, cross out unrealistic or irrelevant advice, and add ideas the tool missed. The exercise also asks them to consider privacy and the limits of A.I.’s knowledge of a community. Students learn that a plausible suggestion still needs someone to judge whether it makes sense here.
Ksenia Power, also in public health, used A.I. to help with lesson planning. Her macronutrients lesson includes a “Diet Detective Challenge” in which students examine sample diets and propose changes. Her accompanying reflection describes the work of evaluating and revising generated material to fit her course goals and students. For faculty curious about A.I. in course preparation, this offers an example of how an instructor develops an initial draft into something suitable for a particular class.
Christopher Wheldon, our third public health professor, asks students to teach a chatbot to apply a theoretical framework. They first complete an analysis themselves, explain their reasoning, and turn those decisions into instructions for the chatbot. Then they test, compare, and revise. His materials include a public health example and a template that colleagues can adapt to other disciplines. How clearly do students understand a concept? Asking them to explain its application, including the ambiguous cases, forces them to examine and refine their own learning.
The faculty reflections deserve your attention, too. McKee, for example, describes finding A.I. useful for generating activity ideas while remaining cautious about its accuracy and protective of his responsibility to understand what he teaches. As intellectually serious academics, we can investigate a possibility while keeping our reservations. Indeed, those reservations may help us ask better questions about an activity’s design.
Across these projects, a common concern emerges regarding the intellectual work students and faculty still need to do. What must students understand to evaluate an answer? What should they attempt before opening the chatbot? How will we know whether the activity helped them learn? These are questions we can bring to our own classroom AI experiments. Of course, we should be asking other questions as well, not the least of which is, should I be using A.I. with my students at all?
We thank Gary, Rob, Ryan, Ksenia, and Christopher for sharing their work. Visit the collection, choose an example, and consider what you would keep or change for your students. For help thinking through an adaptation, contact the faculty member or reach out to Jennifer Zaylea or me at the CAT. Our Faculty Guide to A.I. offers further resources.
Jeff Rients is Associate Director of Teaching and Learning Innovation at Temple University’s Center for the Advancement of Teaching.
Disclosure: ChatGPT assisted in drafting this post. 🙂