Adoption of generative AI in higher education: Understanding usage through a technology acceptance model

Abstract

The aim of this study is to examine the adoption of Generative AI (GenAI) tools among faculty in higher education using a revised Unified Theory of Acceptance and Use of Technology (UTAUT) model. A quantitative survey was conducted with 244 faculty members from eight Saudi universities, and the data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS. The results show that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI) significantly influence the adoption of GenAI tools. Among these, PE and EE had the strongest positive impact on BI, stressing the importance of user-friendly design and faculty training, while SI had a weaker but still meaningful effect. Gender and academic position were also found to moderate adoption behaviors, indicating differences across faculty groups. This study extends the UTAUT model by introducing new moderating factors and provides empirical evidence from Saudi Arabia. It further recommends intuitive system design, technical support, training, and the development of an AI-friendly academic culture to promote effective integration of GenAI in higher education.