Xu Yikun, Xue Lizhu*

School of Culture and Management, Chengdu Vocational University of the Arts

Abstract：

Amidst significant advances in AI and large language models, establishing personalized adaptive learning systems is a key goal of educational digital transformation. This study addresses the research gap in deeply integrating cutting-edge technology, specific course logic, and ideological-political education to enable genuine adaptive learning path generation. Taking the vocational undergraduate course *Internal Control and Risk Management* as a case, it explores an intelligent course reform pathway synthesizing theoretical innovation, ideological-political education, and technological application. The paper elaborates a knowledge graph-driven adaptive teaching model featuring a "dual closed-loop and self-evolving" architecture. It details methods for structurally integrating ideological-political elements into the course knowledge system and explores using generative AI for personalized path generation. This research aims to provide a theoretical and practical paradigm for digitally transforming new liberal arts courses in vocational undergraduate education.

Key Words：

knowledge graph; adaptive learning; vocational undergraduate education