Exploring Pre-Service Music Teachers' Acceptance of Generative Artificial Intelligence: A PLS-SEM-ANN Approach

Sirui He¹Yuhong Ren^2*

• ¹Communication University of China, Beijing, China
• ²Hebei Normal University, Shijiazhuang, Hebei Province, China

This study, 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. Given the rapid development of generative AI, how to effectively apply this technology in education has become an urgent issue. However, research specifically focusing on pre-service music teachers, particularly empirical studies in the Chinese context, remains scarce. To better adapt to the research background, the model retains the original seven factors of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit, and adds two new variables: Perceived Compatibility (PC) and Perceived Risk (PR). The study combines Partial Least Squares Structural Equation Modeling (PLS-SEM) with Artificial Neural Networks (ANN) to both validate the theoretical model and enhance the accuracy of the predictive model. 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. The findings provide theoretical and practical guidance for promoting the application of generative AI in music education.