AI-mediated music education paradigms: A cross-cultural analysis in the Greater Bay area

This study examines artificial intelligence integration in music education within the Greater Bay Area, developing collaborative pedagogical frameworks and evaluating AI-enhanced teaching effectiveness within unique cultural contexts. The research employs a comprehensive framework combining mixed methods, computational analysis, and multi-method data collection to analyze sociocultural aspects of AI integration across educational institutions. Results reveal significant performance disparities between institutions (technical institutes: +27.3%, community colleges: +26.9%), with implementation outcomes influenced by institutional characteristics and faculty adaptation (57.4%-83.2%). The study demonstrates enhanced learner engagement (+35.6%), knowledge transfer (+22.5%), and problem-solving (+21.7%) with faster mastery acquisition (-31.9%). Cultural adaptability analysis shows East Asian systems' superior cultural sensitivity (0.85), while temporal analysis identifies four distinct competency acquisition phases across 24 months. Successful AI integration in music education requires balanced attention to technological configuration, institutional preparedness, cultural appropriateness, and contextual considerations. Policies should leverage technological tools aligned with learning objectives, support faculty development, preserve regional musical heritage while developing contemporary skills, and provide tailored implementation assistance, particularly in multicultural contexts, balancing innovation with cultural preservation.