The application of artificial intelligence in music education management: Opportunities and challenges

Abstract

With the increasing maturity of artificial intelligence (AI) technology, its application in the field of educational management has become an important way to improve the quality and efficiency of education. In particular, in music education management, the introduction of AI technology has brought revolutionary changes to the traditional education model. This paper first discusses the application background of AI in music education management and the opportunities and challenges it brings, especially the potential impacts on teaching quality assessment, resource allocation, and educational policy formulation. However, current research shows that existing AI application methods have deficiencies in processing complex data, algorithm generalization capabilities, and adaptability to specific music education scenarios. In response to these deficiencies, this paper proposes two research contents: one is an intelligent decision support system based on an improved actor-critic framework, aimed at improving the efficiency and accuracy of music education management by simulating complex decision-making processes; the other is using a time series prediction model based on convolutional neural network (CNN) to predict the technology trends in music education, which can capture educational dynamics and provide data support for future educational policies and resource allocation. The research in this paper not only provides new theoretical and practical perspectives for the application of AI in the field of music education management but also provides a scientific basis for the realization of personalized and precise management in music education.