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

Generative artificial intelligence (GenAI) has demonstrated a significant impact across tourismdomains, such as planning, marketing, and product development; however, tourism education remainsconservative in GenAI adoption. Drawing on Kolb’s Experiential Learning Theory (ELT), the studyexplores the role of text-to-image GenAI in enhancing experiential learning for new urban tourismeducation. Using a constructivist, participatory action research paradigm, this mixed-methods researchemployed an abductive approach to analyze visual and textual data derived from AI-generated imagesand 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 ELTstages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. It proposes a pioneering GenAI-integrated ELT model introducing four roles for AI: AI-assistedinquiry, AI-augmented elicitation, AI-enhanced innovation, and AI-informed scaffolding. Overall, thisstudy broadens theoretical and methodological perspectives in the tourism and education domains andsuggests GenAI as a collaborative learning agent for instructional scaffolding.