<![CDATA[Artificial intelligence is transforming music education across lesson planning, performance assessment, personalized learning, creative music-making, and learning analytics, yet successful implementation depends critically on teachers' preparedness to integrate AI responsibly. This integrative literature review examines AI in music teacher education through research published primarily between 2020–2025, identifying adopted technologies, required competencies, pedagogical and ethical challenges, and proposing a conceptual framework for responsible integration. Synthesizing heterogeneous evidence — empirical studies, conceptual papers, systematic reviews, and policy documents — via PRISMA-guided screening and Whittemore and Knafl's integrative approach with theory-oriented narrative synthesis, findings reveal that generative AI, LLMs, intelligent tutoring systems, learning analytics, computer vision, and automated assessment increasingly support instructional planning and creative composition. Effectiveness, however, depends on teachers critically evaluating outputs while preserving musical creativity. AI literacy, music-specific TPACK, ethical awareness, and institutional readiness emerge as interdependent success factors, amid persistent challenges of algorithmic bias, privacy, and limited empirical evidence. The review proposes the Responsible AI Readiness Framework for Music Teacher Education, positioning AI as a pedagogical and creative partner to guide curriculum design, policy development, and future research.]]>
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