Optimising piano duet pedagogy through real-time AI feedback and deep learning-based sound recognition

This work investigates the changing dynamics of piano instruction at the university level through the implementation of the double piano teaching method. Comparing data from institutions that practice this technique with conventional piano pedagogy, the study measures its effect on student engagement, performance, and satisfaction. The method enhances live feedback, peer learning, and musical expression, making it a highly effective pedagogical approach. Moreover, the research combines engineering technology, specifically deep learning-based sound analysis and sequence detection, to maximise music teaching and quantitatively monitor performance. The results benefit both music education and educational engineering by providing an example of data-driven, interactive pedagogies in higher education. By bridging pedagogy and AI-driven performance analysis, this study offers a novel interdisciplinary model for intelligent music education.