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.
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.
Artificial Intelligence (AI) has emerged as a transformative technology with significant implications for teaching, learning, and academic administration. As AI-enabled tools become increasingly integrated into educational environments, understanding the factors that shape users’ intention to adopt these technologies has become imperative, particularly in developing countries where empirical evidence remains limited. Grounded in the Technology Acceptance Model (TAM), this study investigates the behavioral intentions toward AI adoption among students and educators in Nepalese secondary schools and higher education institutions. The proposed model extends TAM by incorporating trust and perceived risk as additional determinants of adoption intention. A quantitative research design was employed using a structured questionnaire administered to 321 respondents comprising secondary-level students, university students, school teachers, and faculty members from public and private educational institutions in Nepal. Data were analyzed using descriptive statistics and Structural Equation Modeling (SEM) to examine the relationships among perceived usefulness, perceived ease of use, trust, perceived risk, and behavioral intention toward AI adoption. The findings reveal that perceived usefulness, perceived ease of use, and trust exert significant positive effects on behavioral intention, whereas perceived risk negatively influences AI adoption intentions. The results indicate growing acceptance of AI technologies among educational stakeholders, although concerns regarding privacy, ethical issues, academic integrity, and overdependence on AI remain substantial challenges. This study extends the applicability of TAM in the educational context of a developing economy. It offers valuable implications for policymakers, educational institutions, and technology developers to foster digital literacy, promote responsible AI use, and establish supportive regulatory frameworks for sustainable AI integration in education.
S. Adhikari· Nepalese Journal of Manageme...· 0 citations
Generative artificial intelligence (GenAI) has entered classrooms faster than most institutions have been able to formulate policy, yet its instructional value ultimately depends on whether teachers choose to use it. This study examined the determinants of teachers' behavioral intention (BI) to use GenAI in teaching, drawing on an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Five predictors were specified: perceived ease of use (PEoU), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), and anxiety (ANX). A cross-sectional survey was administered to 500 in-service teachers drawn from three Chinese educational institutions spanning medical higher education, vocational higher education, and primary education. Each construct was operationalized as a composite mean of its constituent items, and the model was estimated using the Regression module of SmartPLS 4 with bootstrapping (5,000 subsamples) to obtain confidence intervals. Collinearity diagnostics were acceptable (VIF = 1.046-1.238). The model accounted for 32.7% of the variance in behavioral intention (adjusted R² = .320). Perceived usefulness was the strongest predictor (β = .228, p < .001), followed by facilitating conditions (β = .206, p < .001), perceived ease of use (β = .198, p < .001), and social influence (β = .195, p < .001). Contrary to expectations, anxiety exerted no significant effect (β = .018, p = .631), with a bootstrap confidence interval that spanned zero. The findings indicate that teachers' adoption decisions are jointly driven by instrumental value and institutional provisioning, and that generalized technology anxiety is not, in itself, a barrier among teachers who already have practical exposure to GenAI. Implications for professional development design and institutional AI policy are discussed.
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.
Background: Artificial Intelligence (AI) is increasingly transforming instructional processes across educational settings, creating a need to understand the factors associated with its adoption among future educators, particularly across gender groups. Purpose: This study examined the relationships among technological readiness, attitude toward AI, and behavioural intention to adopt AI among pre-service teachers, and investigated whether gender moderates these relationships. Specifically, the study aimed to investigate the extent to which technological readiness is related to attitudes toward AI and behavioural intention to adopt AI, and whether these relationships vary between male and female pre-service teachers. Method: A quantitative cross-sectional survey design was employed, and data were collected from 378 undergraduate pre-service teachers enrolled in teacher education programmes at public universities in southwestern Nigeria using a structured online questionnaire administered through Google Forms. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4, including multigroup analysis to examine gender differences. Results: The findings indicate that technological readiness was not significantly associated with behavioural intention to adopt AI (β = .092, t = 1.945, p = .052), and no significant gender differences were found in this relationship (t = 0.479, p = .630). However, technological readiness showed a significant positive association with attitude toward AI (β = .075, t = 2.330, p < .05), with significant gender differences in the strength and direction of this relationship (t = 3.991, p < .05). The relationship was positive among female pre-service teachers but negative among male pre-service teachers. Conclusion: These findings highlight the importance of strengthening technological confidence and promoting equitable AI integration initiatives in teacher education programmes. The study further highlights the need for teacher educators, informatics educators, and policymakers to create inclusive learning environments that support positive attitudes and engagement with AI technologies among future educators.
O. Falebita, Petrus Jacobus Kok· IJIE (Indonesian Journal of...· 0 citations
The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI, and institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour.
A.M. Al-Darabseh, S. F. Padlee, Siti Nur et al.· Journal of Intelligent Decis...· 0 citations
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
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