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Research on an AI-Driven Music Teaching System in Colleges and Universities Based on RBF Neural Network
Abstract
Artificial intelligence technology provides support for the innovation of traditional music teaching mode. This paper proposes an intelligent music teaching system, which integrates the interactive teaching concept and radial basis function neural network. The system constructs an evaluation model that includes four dimensions: talent, teaching environment, technical level, and initiative. The research compares the teaching effect of traditional teaching mode and artificial intelligence-driven teaching mode in university classrooms. The results show that the experimental group is obviously superior to the control group in comprehensive score, satisfaction, and popularity. The radial basis function neural network model constructed in this paper has good nonlinear mapping ability and generalization performance. The system supports teachers' teaching analysis and personalized feedback, which effectively improves the teaching effect. This study provides a reference for the intelligentization of music education in colleges and universities and education in other fields.
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