Determinants of ChatGPT acceptance among pre-service English language teaching students in Uzbekistan

Abstract

The integration of artificial intelligence (AI) applications, such as Google’s Little Language Labs and ChatGPT, is progressing rapidly, particularly in the field of language education. Pre-service English Language Teaching (ELT) students play a crucial role in determining how these technologies are used in instructional settings. This study investigates their acceptance of ChatGPT within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT2), a model that has rapidly gained substantial scholarly recognition since its introduction. Employing a cross-sectional survey design, data were collected in May 2025 from 456 pre-service ELT students enrolled at universities across Uzbekistan through an anonymous online questionnaire administered via Google Forms. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a weighting path scheme in SmartPLS 4. The results indicate that habit, performance expectancy, effort expectancy, and personal innovativeness have significant positive effects on behavioral intention to use ChatGPT. By contrast, social influence, facilitating conditions, and hedonic motivation did not significantly predict behavioral intention. Furthermore, facilitating conditions, habit, and behavioral intention did not significantly influence actual use behavior. Gender and year of study were tested as moderating variables; however, neither demonstrated a significant moderating effect. These findings highlight the central importance of habit, performance expectancy, effort expectancy, and personal innovativeness in shaping pre-service ELT students’ intentions to adopt ChatGPT, while other UTAUT2 constructs appear less influential in this context. Notably, this is the first study to apply the UTAUT2 framework to examine ChatGPT acceptance among pre-service ELT students in Uzbekistan. Future research should extend this line of inquiry by including broader samples across universities and regions, thereby enabling more comprehensive insights into technology acceptance following cultural adaptation of the UTAUT2 framework in underexplored regions. © The Author(s) 2026.