Acceptance of Generative AI for Supporting Innovative Learning in Vocational Education

Abstract

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.