Abstarct: The present study was conducted with the aim of examining and testing a structural model for predicting students’ behavioral intention to use artificial intelligence technology, baxsed on the key factors of AI acceptance, including perceived usefulness, perceived ease of use, social influence, attitude, and enjoyment. In terms of purpose, this research is applied, and regarding methodology, it is a descxriptive-survey study of a correlational nature. The statistical population consisted of students at Shahrood University of Technology, estimated at approximately 6,000 students. Using Cochran’s formula for finite populations, the required sample size was estimated at 240 respondents. The sampling method was cluster random sampling, in which faculties were considered as clusters. Data were collected using a standardized questionnaire baxsed on a five-point Likert scale. The validity of the measurement instrument was confirmed through factor loadings and convergent validity indices, including AVE values above 0.50. The reliability of the questionnaire was also verified using Cronbach’s alpha and composite reliability coefficients exceeding the acceptable threshold of 0.70. The overall Cronbach’s alpha of the questionnaire was 0.821. The collected data were analyzed using SPSS and SmartPLS software through Partial Least Squares Structural Equation Modeling. The findings revealed that all five key factors of AI acceptance had positive and significant effects on students’ behavioral intention to use artificial intelligence technology. Among these factors, attitude had the strongest effect on behavioral intention, followed by enjoyment, perceived ease of use, perceived usefulness, and social influence. Specifically, the results showed that perceived usefulness, perceived ease of use, social influence, attitude, and enjoyment significantly increased students’ intention to use AI technology. Therefore, it can be concluded that students’ intention to use artificial intelligence is shaped not only by functional and technological perceptions, such as usefulness and ease of use, but also by psychological and experiential factors, especially attitude and enjoyment. These findings highlight the importance of designing useful, user-friendly, and engaging AI-baxsed educational tools in higher education.