An Extended Technology Acceptance Model to Explain Higher Education Students' Behavioral Intention to Use <scp>ChatGPT</scp> : The Case of Kazakhstan

ABSTRACT The present study aimed to model university students' behavioral intention in Kazakhstan to use ChatGPT for academic purposes, based on the extended technology acceptance model (TAM), and to examine correlations among variables expected to influence behavioral intention. A quantitative research method was employed in this study, which involved 200 higher education students. In addition to the main TAM components—perceived ease of use, perceived usefulness, and behavioral intention (BI)—five exogenous variables, which included subjective norm (SNM), school influence (SIE), peer influence (PIE), perceived enjoyment (PEN), and AI self‐efficacy (AISE), were included in the research model. The partial least squares structural equation model (PLS‐SEM) was utilized to analyze the proposed model. The proposed model provided a good fit for explaining BI among university students in Kazakhstan and accounted for 50.3% of the variance in BI. It was determined that perceived usefulness and perceived ease of use, along with the exogenous variables, are direct and indirect predictors of BI. The theoretical and practical implications of using ChatGPT for academic support were discussed in the study.