This interactive session reframes course design around teaching transferable AI skills rather than constructing end-to-end agents. Participants will learn a skills-first framework that decomposes agent-building into teachable competencies (prompt engineering, data curation, evaluation, interpretability, ethics, iteration). We’ll demonstrate modular assignments, scaffolding strategies, and assessment rubrics that map to real-world tasks without requiring students to build complex systems. Attendees will co-design sample activities for their disciplines and leave with templates adaptable to undergraduate and graduate courses. The session emphasizes equity, reproducibility, and instructional scalability so faculty can integrate meaningful AI skill development immediately into their curricula.
