Self-regulated learning (SRL) plays a crucial role in enhancing musical performance by fostering autonomy, metacognitive awareness, and strategic practice habits. In higher music education, students must develop the ability to assess their progress, set learning goals, and adapt their practice strategies to achieve both technical proficiency and artistic expressiveness. This article explores key SRL strategies, including self-assessment, deliberate practice, reflective journaling, and peer feedback, analyzing their impact on students' interpretative skills and cognitive resilience. Additionally, the article examines the integration of artificial intelligence assisted learning tools to support metacognitive development in music education. While AI-based platforms provide real-time feedback on aspects such as timing, dynamics, and accuracy, their potential in fostering higher-order cognitive skills, such as musical reasoning and structural awareness, remains underexplored. By incorporating Schenkerian analysis—a method for understanding hierarchical tonal structures—this research investigates whether AI-generated feedback can support students in making informed interpretative decisions and improving their learning efficiency. Through a mixed-method approach, including surveys, performance evaluations, and reflective journal analysis, this article evaluates the effectiveness of combining SRL strategies with AI-assisted learning in higher music education.

Findings suggest that structured self-regulated learning fosters greater artistic autonomy and cognitive flexibility, while AI feedback, when used critically and in conjunction with analytical methodologies, can complement traditional pedagogical methods. The results contribute to the ongoing discourse on optimizing music education through metacognition and technology-enhanced learning, offering insights into innovative methodologies that balance traditional and digital pedagogies.

Keywords: Self-regulated learning, music performance, higher education, metacognition, artificial intelligence, Schenkerian analysis, deliberate practice, cognitive engagement, interpretative decision-making, technology-enhanced learning.