Dataset_Differences in the Intended Use of Artificial Intelligence between Students and Faculty in Higher Education: An Empirical Analysis

This research develops and validates a comprehensive explanatory model of generative AI adoption in higher education, specifically examining differences between students and faculty in their intention to use ChatGPT. Through multigroup structural equation modeling with 450 participants, the paper integrates different theoretical frameworks to identify principal determinants of adoption and quantify structural differences between populations. The study confirms full measurement invariance while revealing that students and faculty operate under partially distinct adoption mechanisms: students are significantly more influenced by affective responses and social influence, whereas faculty prioritize AI literacy and institutional facilitating conditions. The model achieves exceptional explanatory power, substantially exceeding traditional technology acceptance models. The research shows that aggregating diverse populations fundamentally mischaracterizes adoption processes and provides empirically-grounded recommendations for differentiated implementation strategies in educational institutions.