Artificial Intelligence Applications in Piano Education : An Informatics-Based Literature Analysis
DOI:
https://doi.org/10.62383/polygon.v3i4.700Keywords:
Adaptive Learning, Artificial Intelligence, Human–Computer Interaction, Hybrid Instruction, Music TechnologyAbstract
This study examines the role of Artificial Intelligence (AI) in piano education through a qualitative review of six recent academic sources. AI technology has brought about significant transformations in music learning methods, particularly for the piano instrument. Various AI applications such as automated performance feedback systems, musical accompaniment generators, technical error detection devices, and adaptive learning platforms have enabled new approaches to teaching and learning. AI provides instant feedback, tailored exercises to individual abilities, and creates more interactive and flexible learning environments. These innovations are considered to support the development of students' technical skills more effectively, while increasing learning motivation through personalization and ease of access. Furthermore, this study examines the information systems that support these AI applications, including human-computer interaction, audio signal processing, and the use of machine learning models to recognize playing patterns and technical errors. While AI offers significant benefits, concerns arise regarding its limitations in understanding and responding to the emotional aspects of music. AI is not yet capable of fully supporting the development of subjective and complex musical expression. Over-reliance on this technology is also feared to undermine students' critical thinking, artistic sensitivity, and creativity. Therefore, this study emphasizes the importance of a balanced integration between AI technology and human pedagogical roles, with the teacher remaining the primary facilitator in fostering expression, interpretation, and artistic values in piano learning. The study recommends further research on emotionally responsive AI, blended learning models, and long-term evaluation of AI's impact on students' artistic and musical development.
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