Integrating AI into Faculty Development and Learning Design

This chapter reframes artificial intelligence (AI) in teaching from gadget chasing to capacity building. It argues that durable learning gains come from AI-literate course design, myth-busting of “easy wins,” and guardrails that discourage shortcut use, supported by programmatic infrastructure in centers for teaching and learning. The chapter translates these principles into practice: mapping AI to specific pedagogical aims; launching a bottom-up faculty development program that uses workshops, sandbox trials, and peer mentoring to build confidence; and running classroom pilots that emphasize fit with the syllabus, necessary approvals when student data are analyzed, small-scale rollouts, iterative feedback, and clear go/no-go decisions. An AI Strategy Scorecard (Teaching Metrics) then tracks participation, conceptual depth, instructor workload, persistence, and satisfaction, as well as longer-term effects, enabling continuous improvement rather than one-off experiments. The chapter also repositions the university’s role in knowledge transfer—using AI to personalize learning, support inclusive participation, and streamline routine tasks—while keeping human mentorship, ethics, and critical inquiry at the center. Future-looking sections consider how more capable systems might reshape instruction and why distinctly human judgment must remain decisive. A calculus course case study illustrates benefits and limits in practice. Appendixes provide reusable templates for faculty training, pilot design, and scorecard implementation.