Teaching and Learning in the Age of genAI

This chapter reframes the dominant faculty questions about generative AI from defensive (“How do I AI-proof my assignments?”) and technology-first (“How do I use genAI in my classes?”) to purpose-driven pedagogy by asking the question: “What do I want my students to be able to do?” The chapter demonstrates how Backward Design principles—identifying learning outcomes first, then assessment evidence, and finally instructional methods—provide clear decision-making criteria for when genAI supports versus undermines learning. It presents three evidence-based practices that both reduce academic dishonesty and improve learning in the genAI era: transparency (modeling open disclosure of genAI use), flexibility (adapting to diverse learner needs while maintaining standards), and curiosity (exploring genAI alongside students rather than policing them). The chapter introduces practical tools for fostering honest classroom conversations about genAI use, including Watkins’ genAI Engagement Scale and disclosure checklists that shift the focus from surveillance to reflection. Through concrete examples, including a psychology observation assignment and calculus test scenarios, readers learn how to help students use genAI as a thought partner rather than a cognitive replacement. The chapter establishes that effective genAI integration requires starting with learning objectives, not technological capabilities, and provides immediately applicable strategies for creating productive dialogue about appropriate genAI use while maintaining academic integrity.