Self-Regulated Learning as a Mediator between AI Literacy, Academic Resilience, and Academic Performance among University Students

Abstract

The rapid integration of artificial intelligence (AI) technologies into higher education has transformed how students learn, access information, and engage in academic activities. However, limited research has examined the psychological mechanisms through which AI literacy contributes to academic performance. This study investigates the relationships among AI literacy, academic resilience, self-regulated learning (SRL), and academic performance among university students, with SRL examined as a mediating variable. Using a quantitative cross-sectional design, data were collected from 760 university students in Indonesia through structured questionnaires. Structural Equation Modeling (SEM) was employed to analyze both direct and indirect relationships among the study variables. The findings revealed that AI literacy and academic resilience positively predicted both self-regulated learning and academic performance. Self-regulated learning also significantly predicted academic performance and partially mediated the relationships between AI literacy, academic resilience, and academic performance. The structural model demonstrated excellent goodness-of-fit indices (χ²/df = 1.44, RMSEA = 0.024, CFI = 0.99, TLI = 0.99, SRMR = 0.043). The study contributes to educational psychology literature by integrating technological competence and psychological resilience within a unified self-regulated learning framework in AI-supported higher education contexts. The findings also provide practical implications for universities seeking to enhance students’ adaptive learning competencies and academic success in increasingly digital learning environments.