IMPROVING STUDENTS’ INDEPENDENT LEARNING SKILLS THROUGH AI-BASED PERSONALIZED FEEDBACK SYSTEMS - Eshonkulova Gulrukh Yunusovna (American Journals, 2025)
This article examines the potential of AI-based personalized feedback systems in enhancing university students’ independent learning skills. Rapid developments in generative artificial intelligence, including automated feedback engines and adaptive learning platforms, have introduced new pedagogical opportunities for structuring students’ self-regulated learning processes. The study analyzes how AI tools support learners in planning, monitoring, and evaluating their independent study activities through individualized feedback, real-time error detection, formative assessment, and targeted recommendations. Particular attention is given to the mechanisms by which AI reduces cognitive load, increases learning autonomy, and strengthens metacognitive strategies. The article synthesizes theoretical perspectives from self-regulated learning theory, constructivist pedagogy, and digital competence frameworks to design an AI-supported feedback model suitable for higher education. The findings highlight the pedagogical benefits, limitations, and ethical considerations associated with integrating AI feedback systems into independent learning and provide practical recommendations for teachers and educational institutions.
Keywords: AI feedback, independent learning, autonomy, metacognition, adaptive systems, digital competence, formative assessment, higher education.