The Role of Artificial Intelligence in Enhancing Self-Regulated Learning and Academic Performance

Abstract

This study focused on understanding how AI educational technologies affect students' self-regulated learning (SRL) and learning outcomes. A quantitative research approach was used, collecting data through structured questionnaires from 300 university students across various disciplines. Correlation analysis revealed a strong positive relationship between AI use and SRL. Increased engagement with AI technology helped students achieve better study planning, monitoring, and reflective self-regulated learning practices. Advanced regression analysis indicated that SRL was a strong predictor of academic performance; thus, students with higher self-regulated learning performed better academically. High F-values and significant p-values from ANOVA confirmed that AI use explained a substantial proportion of variance in academic performance, demonstrating AI's predictive value for academic success. Demographic analysis showed that students from both public and private universities benefited from AI applications. However, concerns about equal access were raised. The findings acknowledged that while AI offers tailored assistance and feedback, students need to develop autonomy and self-pacing skills to avoid over-reliance and dependency. Aligning with prior research, these findings demonstrate that AI and self-regulated learning work hand in hand in cultivating continuous learning and achieving positive academic outcomes. Most importantly, the study highlights that with careful implementation and attention to equity, AI has transformative potential in educational contexts.