The Auxiliary Function and Realization Mechanism of Artificial Intelligence in Cross-Cultural Traditional Music Education

Under the background of global cultural mutual learning, traditional music education is facing new challenges. This study explores the auxiliary potential of artificial intelligence in cross-cultural traditional music education, constructs a semantic mapping framework of multimodal classroom situations, designs an intelligent interaction mechanism, and enhances learning motivation and understanding depth. Through experiments, this paper finds that the framework can effectively improve students' interest in learning, significantly improve their academic performance, and reduce the frequency of error patterns such as “beat slip”. In addition, this study also reveals some problems such as data scarcity, inaccurate identification of emotional symbols, and delay of real-time feedback, and puts forward some improvement ideas including enhancing the transparency of the algorithm, protecting cultural diversity, and reshaping the role of teachers. These findings provide an important reference for protecting educational equity and cultural diversity, and lay a foundation for future research.