In November\u2019s Active Teaching Lab, Stuart Moulthrop shared his experience using a custom GPT \u201cagent\u201d to support reading-based discussion and scholarly experimentation in a graduate-level theory course. What began for Stuart as a practical test – \u201cWhat could AI do with a semester\u2019s worth of dense theoretical readings?\u201d – quickly evolved into a broader reflection on what it means to be a scholar, teacher, and creator alongside AI. His experiment showed that building a course-specific chatbot is surprisingly feasible and opens genuinely interesting pedagogical possibilities, from supporting discussion to modeling scholarly dialogue.<\/p>\n\n\n\n
At the same time, the session underscored a crucial constraint: student attitudes toward AI ultimately determine what uses of AI are pedagogically appropriate. Stuart\u2019s students raised thoughtful objections to the use of AI, even while engaging critically with it. The Lab discussion highlighted that while custom AI agents can support learning, their effectiveness depends on careful framing, transparency, and voluntary use that respects student concerns.<\/p>\n\n\n\n
To continue exploring this month\u2019s topic, start by watching the November 5th Active Teaching Lab recording below. Then, take a look at the key takeaways from our discussion and a few classroom experiments you can adapt right away. <\/p>\n\n\n\n