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.
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.