BackgroundAs music education evolves with technological advancements, frameworks such as Technological Pedagogical Content Knowledge (TPACK) (Mishra & Koehler, 2006) and the RAT model (Hughes, Thomas & Scharber, 2006) - Replacement, Amplification and Transformation - have become central to integrating technology into pedagogy. Research demonstrates how technology supports music learning in creating, performing and responding to music (Bauer, 2020), lower engagement barriers (Juntunen, 2017), and enables mobile and informal learning (Goyal & Jain, 2024). With artificial intelligence (AI) emergence, new dimensions opened for personalised learning experiences (Knapp et al., 2023), with GenAI serving as a creative partner (Väkevä & Partti, 2025). Despite growing interest, systematic reviews reveal limited research on AI technology in music pedagogy (Zhang et al., 2024).In Singapore, teachers have experimented with technology-enabled approaches through critical inquiry projects which were published in Sounding the Teaching series that were distributed to schools. Such work demonstrated a growing interest in how music technology and AI can extend pedagogical creativity and transform classroom practices.AimThe panel presents findings from a pioneering study on AI use in Singapore mainstream music education. It shares teacher narratives as case studies illustrating pedagogical creativity with technology, seeking to spark a professional dialogue bridging research and practice, whilst contributing to debates on creativity, equity, and music education’s future.ApproachThe presentation shares AI guidelines and practices in Singapore’s context, reports findings from a national questionnaire study on AI use for music teaching, and presents case studies of technology integration in schools. The case studies employed mixed methods including questionnaires capturing student perceptions, classroom observations documenting learning experiences, and teacher narratives providing insight into pedagogical choices, rationales, and challenges.ResultsThe national questionnaire revealed mixed teacher views. AI tools were predominantly used for creative tasks such as composition, arranging, and song-writing rather than theory, listening or performance. Most respondents believed AI integration positively impacted music classrooms, though some were concerned about potential negative effects on ethical values, creativity, musical skills, student autonomy, and digital literacy. AI was perceived as both aid and hindrance, its value dependent on implementation methods. All respondents expressed interest in professional learning opportunities to support AI integration.The case studies highlighted five secondary-level classroom projects:a) Songwriting with AI and Music TechnologyStudents used AI as a springboard for lyrical and musical ideas, working through workflows that mirrored those of professional music producers, to nurture musicianship, creativity and identity.b) AI Vibe Coding to Transform Music ClassroomAI vibe coding enables teachers to build engagement ecosystems, generating online badges, rankings, digital archives, thus supporting portfolio generation and reflection. One-stop portals create interactive learning journeys while removing cost and technical expertise, making recognition, motivation, and growth accessible to all students. c) Repurposing iPads as Orchestral InstrumentsPersonal learning devices are repurposed through a school’s iPad Orchestra module. Every student assumed orchestral roles, enabling whole classes to explore harmony, texture, and collaboration without traditional instrument costs. By layering sounds and arranging parts collectively, students produce sophisticated and complex music, beyond what a typical classroom ensemble could achieve. The process democratise orchestral learning and fostered inclusivity, teamwork, and creativity. d) Gen music AI in music tasksGen music AI (SUNO) reduced barriers to music creation, enabling students to engage with expressive and conceptual dimensions of creation without requiring advanced instrumental or production skills. This supported differentiation, personalised learning, and assessment, while prompting critical reflection on authorship, originality and ethical use.e) Electronic music pedagogy with informal learning approachesIncorporating principles from electronic music pedagogy (Musical Futures International, 2025) and informal learning (Green, 2008), teachers engaged students in collaborative, student-led music-making experiences that reflected the ways they naturally engage with music outside of school. Students explored digital tools to compose, remix, and produce original works, fostering motivation and sustained engagement.Collectively, these projects illustrate gen AI and music technology’s potential and limitations in lowering entry barriers and fostering creativity, raising important questions about teacher roles, student agency, and ethical considerations.Conclusions and implications for music educationThese images of practices showed that GenAI can serve as a creative partner in the music classroom, with technology integration replacing, amplifying and transforming teaching approaches. Yet, they also highlight teachers’ indispensable role in fostering student agency and identity. Teachers remain crucial as facilitators guiding ethical deliberation, nurture expressive and social dimensions of music-making, and supporting students’ holistic personal growth. There is clear need for professional development, ethical and creative balance, ensuring music pedagogy evolves to harness emerging tools while promoting student agency, inclusion, and expression in an AI-shaped era.