This study explores the incorporation of AI technology into case-based teaching in Chinese MBA education, emphasizing teacher and student perspectives and requirements with a  mix methods based on the UTAUT2 framework, aiming to promote the transformation of classrooms from “teacher-centered” to “student-centered” and interaction-oriented.  Findings demonstrates that both teachers and students acknowledge the capacity of AI to improve case understanding, deliver tailored feedback, and augment learning engagement. Teachers particularly appreciate AI's contribution to pre-class preparation, enhancing active classroom engagement, and facilitating post-class reflection, while emphasizing the necessity of system transparency, adaptability, and human-AI collaboration. Both groups demonstrated pronounced preferences for features like tailored content recommendations, feedback visualization, and adaptable management of instructional pacing. The study offers empirical insights for the development of AI-enhanced case-based teaching systems, facilitating the transition of Chinese MBA education towards an interactive pedagogical paradigm that more effectively addresses student demands.

Keywords: AI, Case-based Teaching, MBA.