A Value-Based Structural Model of Generative AI Adoption for Vocational Teachers

This study develops and empirically tests a value-based structural model of Generative AI adoption among vocational teachers, situating technology acceptance within a techno-pedagogy perspective. Extending the Unified Theory of Acceptance and Use of Technology (UTAUT), the model reconceptualizes performance expectancy, effort expectancy, perceived enjoyment, and computer self-efficacy as cognitive–affective antecedents of perceived AI value. Rather than treating adoption as a linear, intention-driven process, this study positions perceived AI value as the central evaluative mechanism shaping attitudes, behavioral intentions, and actual AI use in instructional contexts. A quantitative explanatory design was employed, involving 110 vocational teachers with prior experience using Generative AI tools. Data were collected using a 5-point Likert-scale instrument and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived AI value is the strongest predictor of both attitude and actual AI usage, while behavioral intention does not significantly predict usage, revealing an intention–behavior gap. Facilitating conditions were also found to be non-significant predictors of actual use. These results suggest that Generative AI adoption in vocational education is primarily value-driven rather than structurally enforced. The study contributes to the techno-pedagogy literature by demonstrating that utilitarian and hedonic value perceptions can directly activate the use of pedagogical technology, thereby extending UTAUT with a value-centered evaluative pathway. Practical implications emphasize the need for pedagogically grounded AI training that enhances perceived instructional relevance, usability, and experiential value, thereby fostering sustainable AI integration in vocational teaching environments.