EDUCATORS’ ACCEPTANCE OF AI-ENABLED TEACHING METHODS IN CHINESE HIGHER EDUCATION: EXTENDING UTAUT WITH PERCEIVED RISK AND DEMOGRAPHIC MODERATORS

Abstract

This study examines educators’ behavioral intention to adopt AI-enabled teaching methods in China using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework that incorporates perceived risk. Survey data were collected from 312 higher-education teachers and analyzed using confirmatory factor analysis and structural equation modeling, followed by multi-group comparisons across gender, age, educational background, and teaching experience. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly and positively predict educators’ intention to adopt AI-enabled teaching methods, whereas perceived risk exerts a significant negative effect. The proposed model explains 63.7% of the variance in behavioral intention. Multi-group analyses further suggest that age, educational background, and teaching experience moderate several relationships in the model, while gender does not show statistically significant moderation. These findings highlight the importance of demonstrating pedagogical value and reducing implementation barriers through institutional infrastructure, technical support, and targeted professional development, alongside transparent governance mechanisms for privacy, ethics, and data protection to mitigate risk perceptions. This study contributes to the AI-in-education adoption literature by validating an extended UTAUT model in a higher-education context and offers actionable implications for universities and policymakers aiming to promote responsible and scalable integration of AI into teaching practices.