Artificial Intelligence in the Music Classroom

Abstract

This study examines the integration of artificial intelligence (AI) technologies into primary and secondary school music classrooms using a cyclical action research approach. Through reflective implementation cycles, two practitioner-researchers investigated how AI tools influence student engagement, instructional planning, and teacher workflows. A review of literature positions AI within the broader context of critical pedagogy, relational pedagogy, pedagogical content knowledge (PCK), and constructivist learning theories. Classroom examples illustrate both the promises and limitations of AI for music learning, while theoretical framing underscores issues of equity, creativity, and teacher-student relationships. Findings indicate that AI functions most effectively when guided by teachers, acting as a collaborative partner rather than a substitute for human instruction. The study offers implications for teacher education, policy, and practice, along with recommendations for responsible and ethical AI implementation in music education.