<![CDATA[-The integration of artificial intelligence (AI) in higher education has attracted growing scholarly attention; however, existing studies largely emphasize technology adoption while offering limited insight into the mechanisms through which AI influences academic performance. Addressing this gap, this study proposes and tests a mechanism-based model that examines the role of self-regulated learning (SRL) in mediating the relationship between AI usage and academic outcomes, while incorporating learning motivation and digital literacy as antecedent factors. Using survey data from 547 undergraduate students, the study employs structural equation modeling to analyze both direct and indirect relationships among the constructs. The findings reveal that AI usage has a significant positive effect on academic performance and SRL, with SRL emerging as the strongest predictor of academic outcomes and a key mediating mechanism. Learning motivation significantly drives AI usage, whereas digital literacy does not directly influence academic performance but operates indirectly through SRL. These results suggest that the effectiveness of AI is contingent upon students’ ability to regulate their learning processes rather than the mere adoption of technology. This study contributes to the literature by extending technology adoption perspectives toward a process-oriented framework that integrates motivational, technological, and cognitive dimensions. The findings offer important implications for educators and policymakers, emphasizing that optimizing AI in education requires not only access to technology but also the development of students’ self-regulatory capacities.]]>
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