The integration of artificial intelligence into music education represents a structural transformation that transcends the mere incorporation of technological tools to become central to the construction of knowledge. This paradigm shift moves pedagogy away from purely formalistic approaches toward an ecology of learning where technique, perception, and culture are dynamically intertwined. In this scenario, technology acts as a cognitive artifact capable of redistributing the student’s mental load, making abstract concepts such as harmonic structures or rhythmic hierarchies intelligible without the need for prior and exhaustive mastery of musical notation or advanced instrumental technique. By freeing up cognitive resources, it becomes easier for students to focus on higher-order processes such as critical listening, reflective interpretation, and informed aesthetic decision-making. This technological mediation intervenes directly in the continuum that links creation, the sound object, and the attribution of meaning, allowing the learning process to be transparent and decision-making to always be conscious. To this end, it is essential that these systems do not operate as opaque entities but are interpretable and put human agency at the center, ensuring that students maintain control over their own creative trajectory. By observing the decisions and strategies employed during sound experimentation, it is possible to map students’ cognitive development, valuing the learning process over the final result. Ultimately, creativity manifests itself as an essential dimension of understanding, where interaction with intelligent systems allows for the broadening of expressive horizons. However, this integration requires a critical and algorithmic perspective that avoids algorithmic biases and promotes learning that reconciles datacy with expressive sensitivity and the sociocultural context of each individual.
Sánchez-Jara et al. (Wed,) studied this question.