Validation of Pre-Service STEM Teachers’ Acceptance and Use of Generative Artificial Intelligence Scale: Rasch Model - Elsima Nainggolan; Saraswathy A/p Ramasundrum; Indra Maulana (Universitas Sulawesi Barat, 2025)
Generation Z pre-service STEM teachers, recognized as digital natives, possess strong potential to adopt Generative Artificial Intelligence (GAI) in educational contexts and advance its meaningful integration. Accordingly, this study aims to validate an instrument designed to measure their acceptance of and use of GAI. A quantitative cross-sectional survey was administered to 401 pre-service STEM teachers using a UTAUT2–TPB–based instrument. Data were collected via Google Forms and analyzed using Rasch modeling (Winsteps 3.7.3). The results confirm that the instrument possesses strong psychometric properties under the Rasch model. Analysis demonstrated high person (0.94) and item (0.97) reliabilities, well-defined separation indices, and acceptable item fit values, indicating that the scale effectively differentiates respondents and maintains stability across items. The unidimensionality test further supported that the instrument measures a single, coherent construct, reinforcing its internal structural integrity. Overall, these findings verify that the instrument is valid and reliable for assessing GAI acceptance and use among pre-service STEM teachers. The study offers both theoretical and practical contributions by providing a rigorously tested measurement tool to evaluate readiness for GAI adoption and integration in teacher education.