Assel Bekmoldayeva

• Modern online courses often replicate traditional instruction as static artifacts, failing to reveal the cognitive causes of learner errors. This paper proposes a self-improving educational ecosystem integrating interactive modules, diagnostic assessments, and AI-driven analytics in a closed feedback loop. The model is implemented on a real platform using WordPress as a flexible application framework. Each module combines theory, interactive practice (H5P), and diagnostic assessment. Natural-language queries to an AI assistant serve as diagnostic signals, revealing hidden cognitive barriers. A three-level management model separates operational support (AI tutor), pedagogical quality assurance, and strategic product development. Continuous improvement follows a four-stage cycle: signal collection, pattern analysis, targeted instructional adjustments, and impact verification. This approach demonstrates that intelligent, evidence-based learning management can transform courses into self-correcting systems, where each cohort improves the experience for the next.