Acceptance of Generative AI in Vocational Teaching and Learning Management

Abstract

The rapid advancement of Generative Artificial Intelligence (GenAI) has created new opportunities for teaching and learning across educational sectors. However, limited empirical evidence explains both vocational teachers’ intentions to adopt GenAI and their actual classroom use. This study examined the acceptance and use of GenAI among private vocational teachers in Thailand through the Unified Theory of Acceptance and Use of Technology (UTAUT), with particular attention to the intention-behavior gap. A quantitative cross-sectional survey design was employed. Data were collected from 400 private vocational teachers across Thailand using a structured questionnaire. The research model comprised Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Behavioral Intention (BI), and Use Behavior (UB). PE, EE, SI, FC, and BI were measured reflectively, whereas UB was specified formatively using self-reported indicators of usage frequency, duration, task breadth, and tool breadth. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the measurement and structural models. Partial Least Squares Structural Equation Modeling (PLS-SEM) showed that reflective constructs have acceptable internal consistency and convergent validity, but discriminant validity is problematic, especially for PE and BI (HTMT = 1.000), and UB indicator weights were non-significant. PE (β = 0.560, p < .001), FC (β = 0.336, p < .001), and EE (β = 0.088, p = .004) significantly predicted BI while SI did not. BI and FC were not significant predictors of UB. The model can explain 91.4% of BI but only 17.8% of UB. These findings highlight an intention-behavior gap and the need for infrastructure, pedagogical guidance, AI literacy, ethical protocols, and ongoing implementation support.