extending experiential learning in marketing strategy education: the generative-ai living case approach

Marketing strategy education struggles to help students develop continuous, adaptive reasoning required in real strategic work. Traditional pedagogical approaches (e.g. cases, simulations, and client projects) improve engagement but remain temporally bounded, limiting students’ ability to experience how decisions cascade through time. This study extends Experiential Learning Theory by introducing the Generative-AI Living Case (G-LC), a pedagogical framework that embeds narrative continuity, algorithmic responsiveness, and staged complexity. Through a design-based research study in a graduate marketing course, we demonstrate how the G-LC enables recursive experiential learning, wherein students reenter learning cycles within one evolving narrative rather than starting anew with each assignment. Analysis of reflection journals, surveys, and classroom observations indicates students perceived greater continuity and consequence, described increasingly nuanced causal reasoning across the semester, and reported stronger connections between theory and strategic decisions when learning unfolded continuously rather than episodically. The G-LC positions instructors as facilitators who sustain narrative coherence while students act as strategists managing the cumulative consequences of their decisions. This framework offers marketing educators a replicable model for bridging theory and practice through continuity and demonstrates how generative AI can extend experiential learning into a dynamic, longitudinal process that mirrors the temporal complexity of real strategic work.