Integrating Text-to-Image Generative Artificial Intelligence Into New Urban Tourism Education: Experiential Learning Theory Perspective

Abstract

Generative artificial intelligence (GenAI) has demonstrated a significant impact across tourism domains, such as planning, marketing, and product development; however, tourism education remains conservative in GenAI adoption. Drawing on Kolb’s Experiential Learning Theory (ELT), the study explores the role of text-to-image GenAI in enhancing experiential learning for new urban tourism education. Using a constructivist, participatory action research paradigm, this mixed-methods research employed an abductive approach to analyze visual and textual data derived from AI-generated images and essays written by 70 undergraduate tourism students. The results demonstrate how students articulated the new urban tourism concepts—the extraordinary mundane, encounters and contact zones, and urban coproduction. The study further analyzed the distinct roles GenAI assumes within the ELT stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. It proposes a pioneering GenAI-integrated ELT model introducing four roles for AI: AI-assisted inquiry, AI-augmented elicitation, AI-enhanced innovation, and AI-informed scaffolding. Overall, this study broadens theoretical and methodological perspectives in the tourism and education domains and suggests GenAI as a collaborative learning agent for instructional scaffolding.

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