Adoption of generative artificial intelligence in instruction: a mixed-methods UTAUT study of K-12 computer science teachers in China

Abstract This study examines the adoption of generative artificial intelligence (GenAI) in instructional practices among K-12 computer science teachers (CSTs) in China, addressing a gap in understanding how subject-specific teachers engage with emerging technologies. Grounded in Expectancy-Value Theory (EVT), the research extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating Innovation Expectation (IE), Cost-benefit (CB), and Perceived Risk (PR), thereby capturing both motivational drivers and potential constraints. A mixed-methods design was employed: in the quantitative phase, survey data from 338 CSTs across 20 provinces were analyzed using structural equation modeling (SEM). The results showed that Performance Expectancy (PE), Effort Expectancy (EE), and IE significantly strengthened teachers’ Attitude to Use (AU), while Social Influence (SI) was not significant. Intention to Use (IU) was shaped by IE, CB, AU, and PR, with PR exerting a negative effect. IU further served as a significant predictor of Behavioral Intention to Use (BI). In the qualitative phase, 12 CSTs participated in concept map-supported interviews. Thematic analysis highlighted barriers including perceived erosion of teacher authority, student overreliance, fragmented understanding of GenAI functions, and ethical data security concerns, while underscoring the importance of targeted professional development and discipline-specific tools. Findings illustrate the multidimensional factors shaping the adoption of GenAI and emphasize the value of theoretically grounded, context-sensitive approaches in supporting teachers’ integration of emerging technologies. These findings suggest that schools and education authorities can promote the effective use of GenAI by teachers through tailored training, tiered support resources, and clear ethical guidelines.