Multimodal Music Teaching Based on Artificial Intelligence

With the rapid development of artificial intelligence and multimodal information processing technologies, personalized learning has become an important research direction in intelligent educational systems. This study proposes an AI-enhanced framework for personalized multimodal music teaching based on data-driven learner profiling. Multimodal learning interaction data, including visual, auditory, textual, interactive, social, and temporal behavioral features, are analyzed using the K-means++ clustering algorithm to identify heterogeneous learning patterns. Three learner profiles, namely Audiovisual-Interactive, Text-Structure Dependent, and Auditory-Rhythm Sensitive, are extracted and used to guide the design of differentiated instructional strategies. To evaluate the effectiveness of the proposed framework, simulation experiments are conducted using virtual learner agents with distinct behavioral characteristics. Results demonstrate that personalized multimodal instruction significantly improves learning efficiency and engagement compared with conventional non-personalized approaches. The proposed framework provides a data-driven methodology for adaptive multimodal learning and highlights the potential of artificial intelligence in multimodal information perception, behavioral pattern analysis, and intelligent human–machine interaction. These findings offer valuable insights for the development of intelligent educational environments and multimodal signal-driven learning systems.