Automatic generation of music education content based on deep learning algorithms

Abstract

This study proposes an innovative approach to improve the quality and efficiency of music education using artificial intelligence technology. By constructing a personalized music education assistance system integrating gated recurrent unit (GRU) and reinforcement learning (RL), the system is capable of generating high-quality teaching resources according to learners’ individual needs. The experimental results show that the experimental group using this system outperforms the control group using traditional pedagogy in terms of skill progress, learning engagement, learning resource usage, motivation and attitude changes, and learning outcomes in specific dimensions of music learning. Nevertheless, the system still needs to be further improved and refined in terms of algorithm optimization, teaching content richness, and interactivity enhancement to better meet the needs of different learners.