A Document-based Comparative Analysis of AI Voice Feedback Systems in University Vocal Music Education between China and Korea

초록

Artificial intelligence (AI) is increasingly used in higher education. However, its application in performance-based vocal music education continues to reflect national educational ecologies. This study uses a document-based comparative analysis of AI voice feedback systems used in undergraduate vocal education in China and Korea. A corpus of [N=40] documents (peer-reviewed studies, policy texts, institutional teaching materials, and technical reports) published between 2019 and 2024 in Chinese, Korean, and English was screened using predefined inclusion/exclusion criteria. Sources were retrieved from CNKI, KCI, RISS, DBpia, and major publisher/organization websites, and qualitative coding was conducted following a multi-round procedure. Documents were coded using four dimensions—classroom organization, instructional interaction, evaluation logic, and technological deployment—and synthesized through a cross-national comparison matrix. Across the reviewed corpus, the analysis identifies two dominant implementation orientations: scalability-oriented formative assessment in China and precision-oriented studio refinement in Korea. There is also within-country variation. Rather than treating AI feedback as a fixed technology, the findings suggest that its pedagogical function depends on curricular structures, assessment regimes, teacher authority, and data governance. These factors shape how algorithmic outputs are interpreted and used. The study discusses implications for teachers, developers, and policymakers. Topics include balanced use of acoustic metrics, protection of vocal biometric data, and safeguards for artistic diversity.

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