The emergence of generative artificial intelligence has brought transformative possibilities to all areas of human endeavor, including music education. Since its widespread adoption in the early 2020s, scholars have begun to examine the impact of these technologies on their research and practice (Jain & Raghuram, 2024; Singh & Hiran, 2022). The Oxford Handbook of Artificial Intelligence in Music Education represents one such scholarly response, bringing together interdisciplinary perspectives on the roles of AI in music teaching and learning. In this panel, the three co-editors and selected chapter authors of the handbook will present its unique scholarly contributions and discuss the implications of generative AI for music education research and practice in an era of rapid technological change.Titled “From Code to Community: AI’s Role in Inclusive and Globally Connected Music Education,” this panel positions generative AI not merely as an advanced tool but as a transformative medium for fostering inclusive, globally connected communities (Hasas, 2024). This perspective resonates with the ISME World Conference theme, “Unity in Music Education: Building Bridges for All,” by highlighting how AI can connect diverse musical voices and learners across cultural and geographic boundaries. Featuring six panelists with a wide range of expertise and international backgrounds, the session will investigate the possibilities and challenges AI presents in reshaping music education. Anchored in Theme 1, “Transforming Music Education: Policy, Media, and Social Change,” the panel envisions the kinds of social transformation that generative AI might catalyze within educational and cultural contexts. In doing so, it will address implications for educational practices and inform future policy development and implementation.As the outline of our panel discussion, the panelists will first address the following areas at the intersection of generative AI and music education: (a) philosophical and ethical considerations, (b) creative and generative practices, (c) teacher education, (d) K-12 education, (e) motivation and agency, and (f) international relationships. In a time of information overload, it is crucial to examine the values that generative AI may bring to music education. When approached responsibly, AI technologies hold the potential to enhance access, equity, inclusion, and democratic participation (Cheng, 2025; Holster, 2024). However, these promises come with legal and ethical implications, including issues of ownership, copyright, authorship, and agency, particularly in both formal and informal learning spaces (McQuillan, 2022; Michel-Villarreal, 2023). The use of large language models (LLMs) shapes not only the process but also the products of musical creativity (Miller, 2019). While generative AI can support creative musicianship, it also risks reinforcing dominant narratives and marginalizing diverse musical voices due to its reliance on aggregated data (Noble, 2018). These tensions raise broader concerns about the impact of AI on creativity, identity formation, critical thinking, and power relations among musics and musicians from diverse cultures in music education. By confronting these challenges and opportunities, our panel will also discuss pedagogical approaches for integrating AI in K-12 classrooms, higher education programs, and community-based music initiatives.Following these panel presentations, we will open a discussion forum with the audience. This discussion aims to explore thoughtful approaches that music teachers might take toward generative AI across various initiatives. Current practices will be critiqued by examining the intersections of knowledge, power, and human relationships. Panelists will connect the conversation to concrete examples that illustrate the use of generative AI in formal education, community music learning and teaching settings, as well as in music business and entrepreneurship.