Generative artificial intelligence usage by university students in the light of Diffusion of Innovations theory

Abstract The rapid proliferation of generative artificial intelligence (AI) tools, particularly large language models such as ChatGPT, has fundamentally transformed the landscape of higher education. This study investigates the factors influencing the adoption of generative AI by university students through the theoretical lens of Diffusion of Innovations (DOI) theory. Drawing upon Rogers’ seminal framework, this research examines how five perceived attributes of innovation-relative advantage, compatibility, simplicity, trialability, and observability-is related to students’ behavioral intention to use generative AI tools in their academic pursuits. A cross-sectional online survey was conducted among students from multiple Polish universities, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated satisfactory psychometric properties, with all constructs achieving acceptable levels of internal consistency reliability, convergent validity, and discriminant validity. The structural model results indicate that relative advantage, simplicity, observability and compatibility are significantly related to behavioral intention, while trialability demonstrates a non-significant effect. The model explains approximately 48.7% of variance in behavioral intention. These findings contribute to the theoretical understanding of technology adoption in educational contexts and provide practical implications for higher education institutions seeking to integrate generative AI tools into their pedagogical frameworks. The study concludes with recommendations for fostering responsible AI adoption while addressing concerns regarding academic integrity and critical thinking development.