Validation of Pre-Service Chemistry Teachers’ Acceptance and Use Of Generative Artificial Intelligence Scale: Confirmatory Factor Analysis - Wahyuni Adam; Suci Rizkina Tari; Hilman Qudratuddarsi; Meili Yanti; Eli Meivawati (FKIP University of Mataram, 2026)
The rapid development of generative artificial intelligence (GenAI) is transforming educational practices by enabling new forms of knowledge construction, problem-solving, and instructional support. Its growing integration into academic contexts has raised important questions about how future teachers perceive and adopt these technologies. This study aims to examine pre-service chemistry teachers’ attitudes and use of GenAI by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theory of Planned Behavior (TPB) within a validated framework. A quantitative cross-sectional survey was conducted with 240 Generation Z pre-service teachers using a structured questionnaire covering constructs such as performance expectancy, effort expectancy, social norms, facilitating conditions, habit, attitude, perceived behavioral control, behavioral intention, and AI use. Confirmatory Factor Analysis (CFA) was applied to test both first-order and second-order models. The findings indicate that the proposed model achieved acceptable goodness-of-fit and strong reliability and validity across constructs. Performance expectancy, effort expectancy, and social norms significantly influenced attitudes and behavioral intention, while facilitating conditions and perceived behavioral control supported AI use. Behavioral intention emerged as the strongest predictor of use. Overall, the study highlights that AI adoption is shaped by interconnected psychological, social, and contextual factors, emphasizing the need for holistic teacher education strategies.