# Modeling music student teachers’ behavioral intention of using artificial intelligence in China

## TL;DR

This study explores factors influencing pre-service music teachers in China to use AI in teaching, finding that social influence and performance expectancy are key.

## Contribution

The study extends the UTAUT model to examine AI adoption in music education, revealing novel dual effects of education policy.

## Key findings

- Social influence, performance expectancy, and effort expectancy positively predict AI usage intentions.
- Education policy has negative direct but positive indirect effects through effort expectancy and social influence.
- AI usage habit does not significantly affect behavioral intention to use AI.

## Abstract

The integration of artificial intelligence (AI) into education is rapidly increasing worldwide and governments actively promote teachers’ positive attitudes toward AI and its use in instructional practices. Although prior research has highlighted the potential of AI in music education, limited studies have examined the factors influencing pre-service music teachers’ intentions to use AI in teaching.

This study employed an online questionnaire based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of 370 pre-service music teachers participated in the survey, and structural equation modeling was used to examine the determinants of their intentions to integrate AI into teaching.

The proposed UTAUT model explained 62.4% of the variance in pre-service music teachers’ intentions to use AI. The results indicated that social influence, performance expectancy, and effort expectancy positively predicted intentions to use AI, whereas education policy and facilitating conditions had negative direct effects. AI usage habit showed no significant effect. Notably, education policy demonstrated positive indirect effects through effort expectancy and social influence, indicating a dual mechanism of policy influence.

The findings of this study provide insights into how individual, institutional, and policy-related factors jointly shape pre-service music teachers’ intentions to adopt AI in education. This study then discussed implications for AI in music teacher training programs.