Exploring the Educational Potential of Generative AI in Music Teaching

Abstract

<p>This study explores the perceptions of pre-service primary and secondary education teachers regarding the use of generative artificial intelligence (AI) in music education, using Suno.ai as a case study. Adopting a mixed-methods design, the research combines quantitative data from Likert-type items with qualitative insights from open-ended responses, allowing for a nuanced understanding of students’ experiences and reflections. The findings reveal a generally positive attitude toward the integration of generative AI in music teaching. Students emphasized the tool’s ease of use, intuitive interface, and ability to quickly produce musical content, which they perceived as motivating and accessible for classroom contexts. At the same time, their responses demonstrate a critical awareness of potential drawbacks, including concerns about diminished creativity, reduced artistic effort, and limited control over the generated output. Students proposed concrete pedagogical improvements to make the tool more suitable for educational use, such as greater customization options, theoretical explanations of musical elements, and a more interactive, learner-centered design. The study suggests that while generative AI holds promise for enriching music education, its educational potential depends on thoughtful pedagogical integration. These findings highlight the importance of fostering critical, creative, and ethical engagement with technology among future teachers, positioning AI not as a substitute for human creativity but as a catalyst for musical exploration and learning.</p>

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