USING ARTIFICIAL INTELLIGENCE TO ADAPT STUDENTS' LEARNING TRAJECTORIES

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In the modern era, where rapidly changing educational landscapes require adaptive learning mechanisms, integrating artificial intelligence into education is no longer a futuristic dream but a necessity. This paper presents a sophisticated intelligent system for real-time monitoring, detailed analyzing, and adaptive optimizing of competency acquisition throughout the learning process. Based on a neural network architecture augmented with ontological modeling and set-theoretic principles, this system provides a structured yet flexible framework for continuous learning improvement. Using the Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology, the proposed model systematically improves educational trajectories through data-driven analysis and iterative improvements, ensuring precise alignment with industry and institutional requirements. In addition, the system incorporates predictive analytics and personalized feedback mechanisms that adapt instructional strategies to individual learner needs, thus bridging the gap between standardized curricula and personal learning paths. It further enhances decision-making for educators by providing actionable insights, real-time performance dashboards, and evidence-based recommendations. By combining advanced computational intelligence with proven educational methodologies, this research contributes to the creation of resilient, scalable, and future-ready learning environments that foster innovation, efficiency, and lifelong skill development.

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