ENHANCING STUDENTS’ INDEPENDENT LEARNING SKILLS THROUGH AI-BASED PERSONALIZED FEEDBACK SYSTEMS IN HIGHER EDUCATION
Keywords:
independent learning, artificial intelligence, personalized feedback, self-regulation, higher education, learning analytics, formative assessment, autonomy.Abstract
This article investigates how AI-based personalized feedback systems can improve students’ independent learning skills in higher education today. Independent learning is interpreted as the learner’s ability to plan, monitor, regulate and evaluate academic tasks with decreasing dependence on direct teacher control. The study argues that artificial intelligence can strengthen this ability when feedback is timely, diagnostic, adaptive and pedagogically meaningful. The proposed research design combines a pre-test and post-test survey, learning analytics, teacher observation and semi-structured student reflection. The analysis focuses on five indicators: goal setting, time management, self-assessment, task persistence and reflective learning. The sample findings suggest that AI-generated feedback supports students in identifying individual weaknesses, selecting suitable learning resources and revising learning strategies more consciously. At the same time, the article emphasizes that AI feedback should not replace the teacher’s methodological guidance; rather, it should extend formative assessment and create conditions for more autonomous learning behavior. The results indicate that properly integrated AI feedback systems can become an effective didactic tool for developing learner autonomy in higher education.
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