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.
The rapid advancement of Generative Artificial Intelligence (Generative AI) has created new opportunities for enhancing teaching and learning, particularly in vocational education, where innovation and workforce readiness are essential. Despite the growing adoption of AI technologies, empirical evidence regarding the factors influencing vocational instructors’ acceptance of Generative AI remains limited, especially in developing-country contexts. This study aims to examine the determinants of Generative AI acceptance for supporting innovative learning among private vocational education instructors in Thailand by integrating the Information Systems Success Model, the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Trust in Technology perspective. A quantitative cross-sectional survey design was employed. Data were collected from 544 private vocational education instructors using stratified random sampling and a structured questionnaire measured on a seven-point Likert scale. The proposed research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results revealed that 15 of the 18 proposed hypotheses were supported. System/service quality and information quality significantly enhanced performance expectancy and effort expectancy, while social influence and trust emerged as the strongest predictors of behavioral intention. Behavioral intention, in turn, exerted the strongest direct effect on innovative pedagogy behavior. The model explained 62.2% of the variance in behavioral intention and 51.7% of the variance in innovative pedagogy behavior, demonstrating substantial explanatory and predictive capability. In addition, the PLSpredict assessment indicated satisfactory out-of-sample predictive performance. The findings extend existing technology acceptance research by integrating multiple theoretical perspectives and positioning innovative pedagogy behavior as the ultimate outcome of AI adoption. The study also provides practical guidance for policymakers, educational administrators, and technology developers by emphasizing the importance of high-quality AI systems, institutional support, trust-building mechanisms, and professional development programs to promote the effective and responsible integration of Generative AI in vocational education.
Ponprom Chooppawa, Potsirin Limpinan, Thada Jantakoon· World Journal of Education· 0 citations
The present study aimed to: (1) examine the levels of perception and opinion of vocational students toward factors influencing their acceptance and use of Generative AI (GenAI) for learning; (2) develop and validate a structural model of causal relationships among such factors; and (3) analyze the direct, indirect, and total effects of predictor variables on behavioral intention and actual use behavior. A sample of 500 students enrolled in Vocational Certificate (VC) and Higher Vocational Certificate (HVC) programs under the Office of the Vocational Education Commission was selected through stratified random sampling. Data was collected using a seven-point Likert scale questionnaire (reliability = 0.978) and analyzed via descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM) using ADANCO software.
Results revealed that vocational students held highly positive opinions regarding GenAI acceptance across all dimensions. The PLS-SEM analysis confirmed satisfactory model fit. Performance expectancy (β = 0.149, p < 0.05), effort expectancy (β = 0.138, p < 0.05), social influence (β = 0.127, p < 0.05), hedonic motivation (β = 0.194, p < 0.01), personal innovativeness (β = 0.262, p < 0.01), and trust (β = 0.147, p < 0.01) all had significant positive effects on behavioral intention. Privacy did not exert a significant effect on behavioral intention. Behavioral intention strongly predicted actual use behavior (β = 0.625, p < 0.01), with the model explaining 79.97% of variance in behavioral intention and 39.05% in use behavior. Findings highlight the growing importance of GenAI in vocational education and suggest that institutions should promote AI literacy, ethical AI practices, and supportive learning environments.
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 and tested Behavioral Intention as an antecedent of Digital Competency.
