AI Insight
This study examined how psychological and motivational factors influence student engagement with Generative AI in higher education, surveying 297 students at King Saud University in Saudi Arabia. The research found that perceived autonomy, relatedness, and value significantly enhanced students' autonomy support for AI use, while autonomy support and autonomous motivation for AI increased overall student motivation, which was the strongest predictor of engagement. Notably, perceived expectancy showed no significant influence on AI adoption factors, and perceived competence did not affect autonomy support.
Why it matters
The findings provide evidence-based guidance for educators and policymakers on implementing Generative AI in ways that enhance student engagement by focusing on autonomy, relatedness, and perceived value rather than solely on competence or expectancy factors. This can inform the design of AI-supported learning environments that promote sustainable engagement in higher education.
Understand the Science
The rapid adoption of Generative Artificial Intelligence (GenAI) in higher education has transformed learning experiences; however, limited research has examined how psychological and motivational factors influence student engagement in AI-supported learning environments. Drawing upon Self-Determination Theory (SDT), Expectancy-Value Theory (EVT), and the Technology Acceptance Model (TAM), this study investigates the relationships among perceived autonomy, competence, relatedness, expectancy, value, autonomy support for AI use, autonomous motivation for AI use, student motivation, and student engagement. A quantitative research design was employed, and data were collected from 297 undergraduate and postgraduate students at King Saud University, Saudi Arabia. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived autonomy, perceived relatedness, and perceived value significantly enhanced autonomy support for AI use, while perceived autonomy, competence, and relatedness positively influenced autonomous motivation for AI use. Furthermore, autonomy support and autonomous motivation significantly increased student motivation, which subsequently emerged as the strongest predictor of student engagement. In contrast, perceived expectancy showed no significant influence on either autonomy support or autonomous motivation, while perceived competence did not significantly affect autonomy support. This study extends existing AI-in-education literature by integrating SDT, EVT, and TAM within a unified framework to explain student engagement in Generative AI-supported learning environments. Practically, the study provides valuable guidance for educators, instructional designers, and policymakers seeking to implement Generative AI technologies in ways that enhance meaningful learning experiences and sustainable student engagement in higher education.