Innovativeness and Optimism as Predictors of Generative AI Acceptance Among Pre-service Elementary School Teachers - Meivawati, Eli; Noviana, Evy; Pratama, I Putu Yogi; Qudratuddarsi, Hilman; Indriyanti, Nor (LPPM Universitas PGRI Argopuro Jember, 2026)
This study investigates Generative Artificial Intelligence (Gen AI) acceptance among Generation Z pre-service elementary school teachers by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model with innovativeness and optimism as additional psychological predictors. As Gen AI becomes increasingly embedded in educational practice, understanding how future elementary school teachers perceive and accept this technology is critical for informing teacher preparation and digital transformation initiatives. Using a quantitative, cross-sectional survey design, data were collected from 563 pre-service teachers across four universities in Indonesia through an online questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS was used for data analysis. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity, while model fit indices indicated that the proposed model adequately represented the data. Results show that both innovativeness and optimism significantly predict performance expectancy, effort expectancy, facilitating conditions, habit, and hedonic motivation, with innovativeness emerging as the stronger personality-based determinant. Regarding behavioral intention to use Gen AI, performance expectancy, effort expectancy, facilitating conditions, and habit exhibit significant positive relationships, whereas hedonic motivation does not significantly influence intention. The structural model explains 75.9% of the variance in behavioral intention, indicating strong explanatory power within the context of Gen AI adoption in teacher education. The findings highlight the dual importance of cognitive evaluation (e.g., perceived usefulness and ease of use) and personal traits (e.g., innovativeness and optimism) in shaping Gen AI acceptance among pre-service elementary school teachers. Theoretically, the study extends UTAUT2 by demonstrating the value of incorporating personality-related constructs into contemporary AI acceptance studies. Practically, the results suggest that teacher education programs should provide structured opportunities for meaningful engagement with Gen AI tools while fostering mindsets that encourage experimentation and openness to technological change. These efforts may better prepare future teachers to integrate Gen AI responsibly and effectively in elementary education settings.