The aim of this study was to identify the factors influencing the acceptance and use of artificial intelligence technology among higher education students, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, and to extend it by incorporating the construct of “perceived risk.” The research employed a descriptive–correlational design. The study population consisted of students at the University of Tehran; 335 participants were selected using convenient sampling. Data were collected through a researcher-made questionnaire based on the UTAUT model and relevant prior studies. The questionnaire’s reliability and validity were confirmed using Cronbach’s alpha (0.78 to 0.91) and confirmatory factor analysis with appropriate fit indices (CFI > 0.90, RMSEA < 0.08). Data were analyzed using SPSS-27 and AMOS-24, employing covariance-based structural equation modeling. The results showed that performance expectancy, effort expectancy, and social influence had positive and significant effects on behavioral intention, whereas facilitating conditions and perceived risk did not have significant effects. Moreover, behavioral intention had a strong and significant effect on actual technology-use behavior. The findings highlight the importance of strengthening infrastructure, simplifying AI tools, providing targeted training, and emphasizing the benefits of this technology to enhance student acceptance. These results can be used to design and improve AI-related educational programs in higher education.