Artificial Intelligence-Enabled Reform of Teaching Models in Tourism Management Education: Practice Based on the “Smart Cultural Tourism Learning” Teaching System at Beijing Union University

Abstract

The deep integration of artificial intelligence (AI) and the tourism industry is driving profound changes in the talent cultivation model of tourism higher education. At present, the traditional teaching of tourism management programs faces four major challenges: lagging knowledge updating, insufficient competency cultivation, difficulties in teachers’ transformation, and fragmentation of the talent cultivation system, making it difficult to effectively respond to the new demands for interdisciplinary talents brought about by the intelligent transformation of the cultural tourism industry. Taking the first tourism AI disciplinary knowledge engine and the “Smart Cultural Tourism Learning” teaching system developed by the School of Tourism of Beijing Union University as an example, this paper systematically reviews its innovative practice of constructing an intelligent teaching ecosystem covering the entire process of teaching, learning, practice, and application, based on core modules including a discipline-specific knowledge base, a knowledge fragment repository, a fine-grained knowledge graph, dual intelligent partners, a professional prompt library, scenario-based intelligent agents, and personalized learning path planning. The study shows that the system significantly reconstructs the dynamic generation mechanism of teaching content, breaks through the temporal and spatial limitations of learning, reshapes highly simulated practical training scenarios, and establishes a multidimensional data-driven teaching evaluation system. It effectively achieves the dual goals of reducing teachers’ workload while enhancing their teaching capacity and improving students’ learning quality and efficiency, thereby effectively addressing the challenges faced by traditional tourism education.