INTEGRATION OF ARTIFICIAL INTELLIGENCE IN PRIMARY SCHOOL MUSIC EDUCATION IN CHINA: A REVIEW BASED ON THE AI-TPACK-AK FRAMEWORK
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Abstract
The integration of Artificial Intelligence (AI) into primary school music education represents a significant transformation with important pedagogical implications. This study reviews and synthesizes global research on the historical development of AI, its current applications in music education, and teachers’ perceptions and attitudes toward AI within the Technological Pedagogical Content Knowledge–Arts Knowledge (TPACK-AK) framework. Using a comprehensive literature analysis approach, the study examines the current status of AI adoption in primary school music education and identifies the key factors influencing teachers’ perceptions and instructional adaptations. The findings indicate that AI-based tools can enhance personalized learning, provide real-time feedback, and support students’ musical creativity and engagement. However, several challenges remain, including algorithmic cultural bias, teachers’ technological anxiety, and disparities in digital infrastructure between urban and rural schools. Analysis based on the AI-TPACK-AK framework suggests that effective AI integration requires teachers to balance technological competence, pedagogical innovation, artistic sensitivity, and disciplinary knowledge. To address these challenges, the study proposes three strategies: developing culturally responsive adaptive algorithms, establishing collaborative teacher-AI lesson planning mechanisms, and promoting digital equity initiatives to reduce resource disparities. A limitation of this study is that most of the existing evidence is derived from developed countries; therefore, further research is needed to examine the applicability of these findings within the contexts of developing economies. This study provides theoretical insights and practical implications for the sustainable and equitable integration of AI in primary school music education.
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