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Exploring pre-service music teachers' acceptance of generative artificial intelligence: a PLS-SEM-ANN approach
1Communication University of China, Beijing, China.
Frontiers in Psychology
|July 14, 2025
Summary
Pre-service music teachers
Area of Science:
- Educational Technology
- Artificial Intelligence in Music Education
Background:
- The integration of Generative Artificial Intelligence (Generative AI) into higher education pedagogy is rapidly evolving.
- Understanding user acceptance is crucial for effective technology adoption in music teacher training.
Purpose of the Study:
- To investigate the factors influencing pre-service music teachers' intention to accept and use Generative AI in their teaching practices.
- To apply the extended Unified Theory of Acceptance and Use of Technology Model 2 (UTAUT2) to the context of Generative AI in music education.
Main Methods:
- Quantitative research methodology was employed.
- Data was collected from pre-service music teachers in higher education settings.
Main Results:
- Perceived Risk, Social Influence, and Habit were significant predictors of Behavioral Intention to use Generative AI.
- Behavioral Intention and Perceived Risk were key determinants of actual Generative AI use behavior.
- Sensitivity analysis validated the central role of Behavioral Intention and the negative impact of Perceived Risk.
Conclusions:
- The study offers theoretical insights into technology acceptance in music education.
- Practical recommendations are provided to facilitate the adoption and effective utilization of Generative AI among future music educators.
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