Incorporating AI Into an In-Class Peer Instruction Polling Activity

Abstract

This resource describes an AI-enhanced peer-instruction activity developed for Physical Chemistry I (CHEM 30321) at the University of Notre Dame. The activity integrates generative AI tools into collaborative, in-class learning to help students develop conceptual understanding and connect mathematical equations with physical phenomena. Students work in small groups with AI tools that are prompted to guide their reasoning through leading questions rather than provide direct answers, exploring topics such as quantum tunneling and scanning tunneling microscopy. Chen (CHEM) – Incorporating AI.Following the AI-supported exploration, students complete a concept test without AI assistance and submit their responses through Poll Everywhere. The instructor then provides feedback and reveals that some incorrect answer choices or illustrations were generated by AI, creating an opportunity to emphasize critical evaluation and fact-checking of AI-generated content. The resource also provides suggestions for adapting the activity and gathering student feedback about their experiences learning with AI.