Abstract: Case-based teaching is an essential approach in undergraduate hotel management teaching for helping students understand complex management scenarios, develop critical thinking, and enhance decision-making skills. Generative artificial intelligence (AI) introduces new pathways for improving case-based teaching. This study analyzes the current challenges of case-based teaching, including the need for more diverse teaching methods, expanded case resources, and deeper analytical engagement. It proposes practical approaches to leverage generative AI to enrich teaching methods, extend case resources, and expand analytical depth. Through the development of a multidimensional evaluation framework, the effectiveness of generative AI in teaching is explored. This research provides theoretical support and practical references for applying generative AI in hotel management teaching.

Key words: Generative artificial intelligence, Hotel management teaching, Case-based teaching, Teaching effectiveness evaluation, Teaching innovation, Case resources