AI Technology-Enabled Personalized Teaching of British Vocal Music: Practical Pathways and Research

Abstract

The integration of artificial intelligence (AI) into vocal pedagogy presents significant opportunities for personalized instruction, yet its systematic application to the specialized domain of British vocal music remains underexplored. This study investigates practical pathways for AI-enabled personalized teaching, focusing on real-time vocal assessment, adaptive repertoire selection, and individualized performance coaching. Employing a mixed-methods approach encompassing qualitative needs analysis, prototype development, and quantitative quasi-experimental evaluation, the research evaluates the effectiveness of an AI tool designed for British vocal training. Findings indicate that AI can provide valuable, data-driven feedback that significantly improves students' technical skill, stylistic competence, and self-efficacy compared to traditional instruction alone. However, the study also identifies key limitations, particularly in AI's capacity to analyze expressive delivery and adapt to nuanced cultural-stylistic contexts. The research concludes that the most effective pathway involves a hybrid pedagogical model where AI augments the teacher by handling foundational technical reinforcement and progress monitoring, thereby freeing the instructor to focus on higher-order artistic mentorship. This study contributes a practical framework for integrating AI into tradition-rich arts education, emphasizing technology's role as a supportive assistant rather than a replacement for human expertise.