Evaluation of an AI-Based Feedback System for Enhancing Self-Regulated Learning in Digital Education Platforms

Rakhmetov M. Sadvakassova A. Saltanova G. Kuanbayeva B. Zhusupkalieva G.
13 October 2025 Academic Conferences and Publishing International Limited

Electronic Journal of e-Learning
2025 #23 Issue 4 126 - 141 pp.

The development of self-regulated learning (SRL) skills, including the ability to plan, monitor and reflect, is increasingly recognised as essential for academic success in online learning environments. Despite this, most digital learning platforms continue to provide limited feedback, typically focused on task outcomes rather than learning processes. This study investigates the effectiveness of an artificial intelligence-based feedback system integrated into a standard online course platform. The system delivers adaptive, process-oriented feedback by analysing anonymised engagement summaries and short reflective inputs, aiming to promote self-regulated learning strategies without requiring additional instructor involvement or manual input for feedback generation. A quasi-experimental study was conducted with 180 undergraduate students enrolled in a fully online course. Participants were pseudo-randomly assigned by an automated allocation script to an experimental group (n = 90) receiving AI-based adaptive feedback or a control group (n = 90) with standard LMS features. The system employed behavioural indicators (e.g., time-on-task, quiz activity and content engagement) and natural language analysis of reflective entries to generate personalised prompts related to goal-setting (including time management), effort regulation and metacognitive reflection. Data sources included post-course surveys, aggregated system interaction records, academic performance data and open-ended student feedback on the system’s perceived effectiveness and usability. Students who received adaptive feedback exhibited significantly stronger engagement with SRL behaviours, including earlier task initiation, increased use of optional learning resources and greater consistency in study routines. Qualitative responses indicated that participants found the feedback clear, timely, actionable and supportive of their cognitive and motivational processes. In contrast, control group participants primarily relied on grade-based feedback and exhibited fewer strategic adjustments during the course. The findings suggest that a lightweight, AI-driven feedback mechanism can be effectively integrated into online course platforms to support SRL at scale. This study demonstrates how adaptive AI feedback can meaningfully influence academic outcomes, planning behaviour and engagement with feedback in digital learning environments.

Adaptive feedback , Artificial intelligence in education , Digital education , Learning analytics , Motivation and reflection , Self-regulated learning

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Department of Computer Science, Faculty of Physics, Mathematics and Information Technology, Khalel Dosmukhamedov Atyrau University, Kazakhstan
Department of Computer Science, Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Physics and Technical Disciplines, Faculty of Physics, Mathematics and Information Technology, Khalel Dosmukhamedov Atyrau University, Kazakhstan

Department of Computer Science
Department of Computer Science
Department of Physics and Technical Disciplines

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