The purpose of this study was to validate the Generative Artificial Intelligence Acceptance Scale, grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), among Iranian university students. The statistical population consisted of all undergraduate students nationwide, from whom 351 participants were selected through convenience sampling. Data were collected using the Persian version of the Generative AI Acceptance Scale, comprising 20 items across four subscales: performance expectancy, effort expectancy, facilitating conditions, and social influence. Prior to administration, cultural adaptation was ensured through back-translation procedures and expert review for face validity. Data analyses included first- and second-order confirmatory factor analyses, Cronbach’s alpha coefficients, and one-sample t-tests. First-order CFA results indicated that item loadings ranged from 0.52 to 0.87, confirming the four-factor structure (χ²/df = 2.12; RMSEA = 0.056; CFI = 0.95). Second-order CFA further demonstrated that the four components collectively formed a higher-order construct representing generative AI acceptance (χ²/df = 2.26; RMSEA = 0.060; CFI = 0.94). Factor loadings of the components on the higher-order construct ranged from 0.51 to 0.82. The overall reliability of the scale was 0.90, and the subscales showed reliability values ranging from 0.61 to 0.89. Examination of the construct level also revealed that the generative AI acceptance score among the participating students was significantly above average (p < 0.01). Based on these findings, the scale can be effectively used in future research to assess generative AI acceptance among university students.