Personalized Learning Path Planning and Effect Verification of Music Education Information System Empowered By Artificial Intelligence

This study developed and deployed a music education information system based on multimodal fusion and reinforcement learning, aiming to directly improve the teaching quality and effect of music education through computer technology. The system dynamically recommends personalized learning paths and resources by collecting and analyzing multimodal data such as students' music foundation, learning preferences and emotional state in real time. In a one-year practical application test, 100 students with different music foundations and learning styles participated in the test. The results showed that students had significant improvements in instrument playing skills, music theory knowledge and music creation ability, and the speed of skill improvement increased by an average of 30%. At the same time, students' learning interest was significantly enhanced, the learning time increased by an average of 35%, and the number of active participation in learning activities increased by 40%. In addition, students' learning efficiency was also significantly improved, the time required to complete the same learning task was reduced by an average of 25%, and the speed of knowledge mastery increased by 20%. Teacher feedback showed that the system provided strong support for teaching and helped teachers provide more accurate personalized guidance. Students gave high praise to the experience of using the system, believing that the learning paths and resources recommended by the system met their learning needs and could maintain a positive learning attitude. This study provides strong support for the intelligent development of music education and demonstrates the great potential of computer technology in improving teaching quality.