Evaluating The Impact of Digital Teacher Training on Generative AI Adoption in Urban Chinese Schools

• In Event: Emerging Considerations for Designing In-Service Teacher Training in Generative AI

Abstract

This study examines how Digital Teacher Training programs (DTT) influence GenAI adoption among 1,693 K-12 teachers in Beijing. Combining the Professional Capital Framework and UTAUT model, we analyse DTT's impact on GenAI use, trust, and job satisfaction, while exploring adoption disparities across teacher subgroups. PLS-SEM results show DTT significantly enhances effort expectancy (β=0.753***), social influence (β=0.779*), GenAI use (β=0.555***) and job satisfaction (β=0.185***). Early-career teachers adopted GenAI more readily (β=0.682***) than mid-career peers (β=0.507***), while postgraduates outperformed bachelor-level teachers. Local tools like DeepSeek, Kimi and Baidi dominated the DTT. Findings suggest that career-stage-specific training, localised tool integration, and peer learning communities are critical for adoption. The study validates UTAUT in GenAI contexts and offers a framework for AI-ready teacher development.

Authors

• William Edusei-Mensah, Beijing Normal University

• Yushun Li, Beijing Normal University

• Jiannan Bai, Beijing Normal University