Enhancing self-regulated learning through artificial intelligence: experimental evidence from high school students
Abstract
Self-regulated learning is a critical competency for supporting students’ academic success, yet its development remains limited by outdated, inflexible teaching approaches. Meanwhile, the potential of artificial intelligence in education continues to grow, but empirical evidence regarding its effectiveness in enhancing self-regulated learning among secondary school students in Indonesia remains limited. This study aims to test the effectiveness of using an artificial intelligence application in enhancing students’ self-regulated learning. The research method employed a quantitative approach using an experimental design with a pretest-posttest control group, involving 64 students divided into experimental and control groups via cluster random sampling. Data were collected using a self-regulated learning scale that had been validated for validity and reliability, then analyzed using an ANCOVA test with JAMOVI software. The results indicate that the experimental group demonstrated a significant increase in self-regulated learning compared to the control group (p < 0.05). These findings confirm that the use of artificial intelligence applications is highly effective in enhancing self-regulated learning among secondary school students and provide practical implications for technology-based development within school.
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