The incorporation of artificial intelligence into the piano instruction may result in a more individualized approach to learning. With the use of AI technology to assist intelligently, students will be able to learn piano more efficiently, increase the quality of teaching, inspire their interest in music, and finally enhance their musical abilities and performance. The paper provides the guidelines to integrating AI technology into the piano instruction and suggests a model of piano performance that relies on human-machine collaboration with the help of deep learning. Based on the theoretical framework of deep learning sequence processing and combined with the Onsets and Frames model, a model of piano music transcription is built. This study is using a binary cross-entropy objective in order to guide the parameter tuning through step by step. A framework of piano evaluation based on MIDI was subsequently created to confirm its usefulness in practice. The results of the experiment gave an average F-Measure of 96.43% when tested on several piano pieces, indicating highly effective performance and consistent testing results among piano students. When students completed a platform-supported learning experience, they were assessed on applied learning, knowledge extension, and innovative thinking using p-values of 0.052, 0.054, and 0.05 compared to traditional teaching methods. These results indicate that the online teaching strategy presented in the current paper would provide higher educational results as compared to the ones reached under conventional teaching conditions.
