This study examines the development of an AI-assisted personalized case teaching model within the context of MBA education in China. The goal is to explore how teachers and students accept this model and what features it needs, helping to shift classrooms from being focused on teachers to being focused on students, using a mixed-method approach based on the UTAUT2 theory. The majority acknowledged that AI systems can enhance case understanding, the quality of personalized feedback, and the level of engagement in learning. Teachers predominantly agree with the application of AI to aid in pre-class preparation, improve classroom engagement, and guide post-class reflection, while emphasizing the need for the system to be controllable, explicable, and adaptive in terms of human-machine collaboration. Both teachers and students have shown a marked preference for personalized recommendations, feedback visualization, and the modulation of teaching pace. This work offers empirical insights for the design of AI-enhanced case teaching systems, facilitating the shift of MBA education toward a student-centered, interaction-driven approach.