ArticleFrontiers in psychology2026
Examining the impact of generative AI on student motivation and engagement: the mediating role of autonomy-support and autonomous motivation in education.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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