Exploring pre-service music teachers' acceptance of generative artificial intelligence: a PLS-SEM-ANN approach

Abstract

Introduction: Based on the extended Unified Theory of Acceptance and Use of Technology Model 2 (UTAUT2), explores the intention to accept Generative Artificial Intelligence (Generative AI) technology in teaching and its influencing factors among pre-service music teachers in higher education. Method: Quantitative research. Results: The results indicate that Perceived Risk, Social Influence, and Habit significantly influence Behavioral Intention, while Behavioral Intention and Perceived Risk are key predictors of actual use behavior. Sensitivity analysis further confirms the central role of Behavioral Intention and the inhibitory effect of Perceived Risk. Discussion: The findings provide theoretical and practical guidance for promoting the application of generative AI in music education.

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