<![CDATA[The integration of Artificial Intelligence (AI) into higher education has opened new opportunities to enhance Self-Regulated Learning (SRL) through AI-generated feedback. This study investigates the effectiveness of automated, real-time feedback in supporting students’ metacognitive processes of planning, monitoring, and reflection. Using a quasi-experimental mixed-method design, 124 undergraduate students were divided into experimental and control groups, with only the experimental group receiving AI-generated feedback through a university Learning management system. Data were collected via pre- and post-tests using the adapted Indonesian version of the MSLQ questionnaire, system log analytics, and semi-structured interviews. The results revealed a significant increase in SRL scores for the experimental group (average improvement of 0.67 points, p < 0.01, Cohen’s d = 0.62) and higher weekly engagement (rising from 65 to 91) compared to the control group. The AI feedback system achieved 87.4% accuracy in classifying Learning errors. Interview findings highlighted three key themes: students perceived the feedback as accurate and motivating, valued its immediacy, but also expressed concerns about its impersonal nature. These findings demonstrate that AI-generated feedback can effectively scaffold learner autonomy while complementing human facilitation. This study contributes both theoretically by linking SRL frameworks to AI-supported Learning and practically by offering insights into designing feedback systems that balance technological efficiency with pedagogical sensitivity.]]>
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