Despite the growing popularity of ChatGPT in higher education, the factors shaping its acceptance in Iranian academic environments remain unclear. This study aims to analyze the factors influencing students’ acceptance and use of ChatGPT and to examine the differences between students at higher-ranked and lower-ranked universities (based on QS rankings). Data were collected through an online survey, consisting of 26 items based on the extended UTAUT2 model, from 354 students, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Mann–Whitney U test. The findings revealed that, in both groups, "Performance Expectancy", "Effort Expectancy", "Social Influence", "Hedonic Motivation", and "Habit" were significant predictors of "Behavioral Intention". However, "Performance Expectancy" was the strongest predictor for students at higher-ranked universities, while "Habit" was the strongest predictor for students at lower-ranked universities. "Behavioral Intention" had a direct and significant effect on "Use Behavior" in both groups, though this effect was stronger among students at higher-ranked universities. Additionally, "Facilitating Conditions" significantly influenced "Use Behavior" only in lower-ranked universities. The Mann–Whitney U test indicated that students at higher-ranked universities generally scored higher on most model constructs, including "Behavioral Intention" and "Use Behavior". These findings highlight the need to strengthen digital infrastructure, provide targeted training, and implement equitable access policies to bridge the digital divide and promote more effective use of artificial intelligence in higher education.