Machine learning in education could tune up the way in which music is taught and learned. Such tools might offer instant, precise feedback and tailored support to students. They do, however, raise some interesting ethical questions about pedagogy, accessibility, and cultural integrity.
Historically, music instruction has always been about live teaching, often one-on-one, where students receive guidance based on their teacher’s observations and interpretations of their abilities. Such direct mentoring is inherently limited by time, subjectivity, and the delays between performance and feedback. In contrast, machine learning (ML) systems now being developed promise to provide real-time, data-driven responses to a student’s playing. Moreover, work in the International Journal of Information and Communication Technology, suggests that such tools might even detect issues that even the most attentive human teacher might miss and so provide important advice to the student.
These intelligent systems combine audio signal processing with predictive modelling rooted in deep learning. Deep learning is a type of artificial intelligence (AI) inspired by how the human brain processes information. It allows software to detect patterns in large datasets, such as musical performances, and make judgements or predictions based on those patterns. Once trained, these models can assess technical elements such as pitch accuracy, rhythm and tempo precision, dynamics, and even expressive phrasing.
For music students, such tools can offer feedback instantaneously, perhaps between lessons with human teachers. A student practising a piece can be alerted mid-performance about inconsistent tempo or missed notes and can see suggestions for improvement immediately, and so be ahead of the score when it comes to playing for their tutor or in an examination setting.
The systems also offer personalisation at a level difficult to achieve in traditional teaching. By monitoring a student’s progress over time, machine learning tools can detect persistent challenges, such as uneven articulation or imprecise rhythm, and adapt future exercises accordingly. This responsive, data-informed support is especially valuable in contexts where access to expert instruction is limited, allowing high-quality feedback to reach learners beyond the conservatoire or music school.
Lu, L. (2025) ‘AI-powered intelligent music education systems for real-time feedback and performance assessment’, Int. J. Information and Communication Technology, Vol. 26, No. 18, pp.33–47.
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