Research and Practice Conception of AI-Driven Precise Diagnosis of Historical Styles in Piano Teaching in Colleges and Universities

Abstract

In piano teaching in colleges and universities, the accurate grasp of historical styles is a core link to improve students’ performance literacy and inherit music culture. Relying on teachers’ professional experience, the traditional teaching model plays an irreplaceable role in style inheritance and skill guidance, and there is room for improvement in the accuracy of diagnosis, feedback efficiency, and coverage of personalized guidance. The rapid development of artificial intelligence technology provides a new path for improving the quality and efficiency of piano teaching in colleges and universities. Its advantages in audio recognition, data modeling, and intelligent analysis can complement traditional teaching, realizing the accurate capture, quantitative analysis, and scientific diagnosis of historical styles in piano performance. Based on the actual needs of piano teaching in colleges and universities and the characteristics of artificial intelligence technology, this paper clarifies the core objectives and principles of the research and development of “precise diagnosis of historical styles”, systematically constructs a research and development framework covering key links such as data resource construction, algorithm model research and development, diagnosis module design, and teaching adaptation optimization, and puts forward specific practical paths and guarantee measures. It focuses on emphasizing the two-way efforts of technological empowerment and the improvement of teaching subject capabilities, cultivating college piano teachers and students who can use artificial intelligence, promoting the upgrading of piano teaching towards precision, personalization, and intelligence, helping students accurately grasp the essence of styles of piano works in different historical periods, and improving their musical performance and aesthetic abilities.