AI-Enabled Learning at Scale: Integrating Theory, Implementation, and Impact

This tutorial explores how to design, implement, and evaluate AI-enabled learning systems at scale by aligning theory, implementation, and impact analysis. It addresses challenges in real-world settings, such as variability in usage, context, and system features, and introduces logic models to connect learning theory with measurable outcomes. Through practical strategies and case-based activities, participants learn to manage implementation, measure fidelity, and incorporate variation into analytic models to produce credible and scalable research.