Suno AI: Opportunities and Challenges of AI-Generated Music and Lyrics in Secondary Music Education

Abstract

This study examines how secondary school music teachers in Macao utilized Suno AI, a generative AI tool for music and lyrics, in regular classes to guide students in creating a Grade 12 graduation song. Five in-service teachers implemented the tool using free Suno AI accounts on iPads and laptops. Instruction followed an iterative process described as the PGRRF model: Prompt, Generate, Review, Regenerate, Finalize. Analysis of post-instruction interviews found three main results. For research question 1 (implementation patterns), teachers positioned Suno AI as a co-creative partner and preserved student agency by encouraging active refinement of prompts, critique, and selection of versions. For research question 2 (opportunities), the tool raised student motivation, lowered technical and creative entry barriers, supported differentiation by genre, language, and ability, and promoted AI literacy through class discussions on how the system, based on a large language model architecture, generates lyrics and melodies. For research question 3 (challenges and limitations), teachers identified several challenges, including limited control over musical details, the risk of student over-reliance that could reduce the practice of melody and theory skills, a lack of teacher training on prompt design, and platform constraints, as well as uncertainty about copyright and public use under the free plan. The findings support the view that generative AI can be a valuable classroom resource when paired with structured workflows, clear learning goals, and guidance on rights and limitations. By providing classroom cases and teacher perspectives, this study filled the research gap with empirical evidence on how Suno AI was implemented in secondary music education, the benefits that teachers perceived, and which constraints and policies were most important for responsible use in the domain of music and lyric creation.