The rapid emergence of generative artificial intelligence (GenAI) has profound implications for music education, particularly in assessment practices. Building on historical precedents of innovations like the internet and blockchain that reshaped industries (Carr, 2008; Swan, 2015), GenAI—defined as AI that creates original content using large language models (LLMs) and generative pre-trained transformers (GPTs)—enables educators to generate customized assessments. However, this promise is tempered by perils, including facilitated cheating, ethical misuse, and threats to sustainable AI engagement at work and in education (Shao, et al., 2024; Shaw, 2024).We will present a facets model that aligns AI usage with established assessment and ethical principles in music education. Drawing on the World Alliance for Arts Education (WAAE) Guiding Principles for the Assessment of Arts Learning (Brophy et al., 2021) and the ethical tenets of the Rome Call for AI Ethics (RenAIssance Foundation, 2020), the model frames six key facets to guide AI use for assessment: (1) prompt design and context, (2) social justice in output, (3) human-centered interpretation, (4) fidelity in implementation, (5) transparency of the algorithmic process, and (6) ongoing reliability and security checks. In practice, music educators can use the facets as a decision protocol to evaluate whether an AI-generated rubric, formative task, or feedback artifact aligns with artistic integrity, equity, and pedagogical context.We will share how music educators can use the facets model to integrate GenAI while grounding it in established principles and ethics. Drawing from the WAAE principles, assessments must embody social justice (bias-free, culturally responsive, authentic, and accessible), fidelity (valuing artistry, process, intentionality, trustworthiness, and viability), and sustainability (fostering shared language, curricular connections, and instructional value) (Brophy et al., 2021). Participants will learn techniques for developing prompts and apply them in the session to create sample assessments.Ethical dimensions will be addressed through the Rome Call for AI Ethics, which advocates transparency, inclusion, responsibility, impartiality, reliability, and security/privacy (RenAIssance Foundation, 2020). A sample high school music theory assignment—requiring an original chorale with secondary dominants—will highlight dilemmas of undetected AI-generated submissions.Through interactive discussions and demonstrations, we aim to amplify the complementary strengths of AI and music assessment by equipping participants with strategies for human-centered, ethically sound GenAI applications, ensuring music assessment remains innovative yet principled. This session aligns with ISME’s theme Unity in Music Education: Building Bridges for All by seeking to bridge the gap between technological innovation, sustainability, and humanistic values.