Development and Validation of a Structural Equation Model: The Impact of Generative Artificial Intelligence on Task Completion among Music Teachers in Higher Education

Generative artificial intelligence (GAI) is increasingly integrating into education, leveraging its strengths in content creation and intelligent interaction. This study is grounded in an integrated framework of the Technology Acceptance Model and Flow Theory. It constructs a novel structural equation model to examine the interrelationships among six latent variables. These variables include network externalities, perceived ease of use, perceived usefulness, perceived value, immersive experience, and continuous usage intention. A survey of 612 music teachers from higher education institutions examines the positive impact mechanism of GAI on their task completion. The results indicate that immersive experience and perceived usefulness significantly promote continuous usage intention, while perceived value exhibits a negative influence. Accordingly, optimization should focus on four dimensions. Specifically, efforts are needed in: building a policy support system, optimizing paths of product research and development, innovating teaching practices, and establishing a teacher development support system. These efforts, thus, will collectively facilitate the digital transformation of music education.