Extending UTAUT for Generative AI Adoption among Vocational Teachers: The Roles of Trust, AI Literacy, and Risk Awareness

Abstract

Generative Artificial Intelligence (GenAI) has substantial potential to support teaching, learning, and professional development, yet evidence remains limited on how vocational teachers form adoption intentions and whether those intentions translate into broader digital competency. This study examined GenAI acceptance among private vocational teachers in Thailand by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) with three AI-specific constructs: AI Literacy, Trust, and Perceived AI Risk. It also tested Behavioral Intention as an antecedent of Digital Competency. A quantitative cross-sectional survey design was employed. Data were collected from 513 private vocational teachers across Thailand using a structured questionnaire. The proposed model included AI Literacy, Performance Expectancy, Effort Expectancy, Trust, Facilitating Conditions, Perceived AI Risk, Behavioral Intention, and Digital Competency. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both the measurement model and structural relationships among constructs. The results demonstrated that the proposed model explained 80.7% of the variance in Behavioral Intention (R² = 0.807), indicating substantial predictive power. Trust (β = 0.295, p < .001) and Effort Expectancy (β = 0.197, p = .001) were found to significantly and positively influence Behavioral Intention to use GenAI. Perceived AI Risk also exhibited a significant positive effect on Behavioral Intention (β = 0.339, p < .001), contrary to the hypothesized negative relationship. In contrast, AI Literacy, Performance Expectancy, and Facilitating Conditions did not significantly predict Behavioral Intention. Furthermore, Behavioral Intention did not significantly influence Digital Competency (β = −0.134, p = .243), suggesting that technology acceptance alone is insufficient to enhance teachers’ digital competency. The findings contribute to technology acceptance research by integrating AI-specific constructs within an extended UTAUT framework and providing evidence from the underexplored context of vocational education. The study highlights the critical roles of trust, ease of use, and responsible AI awareness in promoting GenAI adoption. It also emphasizes that digital competency development requires structured professional learning, practical engagement, and continuous competency-building initiatives beyond mere technology acceptance. These findings offer important implications for educational institutions, policymakers, and teacher development programs seeking to foster effective and sustainable AI integration in vocational education.