Using artificial intelligence to adapt students' learning trajectories
Artykuł w czasopiśmie
MNiSW
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| Status: | |
| Warianty tytułu: |
Студенттердің оқу траектoриясын бейімдеу үшін жасанды интеллектті қолдану
Применение искусственного интеллекта для адаптивного прогнозирования yчебных траекторий студентов
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| Autorzy: | Kaderkeyeva Zulfiya Kenesovna, Omarbekova Assel, Miłosz Marek, Bigaliyeva Zhanar, Baiturganova Vinera |
| Dyscypliny: | |
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| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 4 |
| Wolumen/Tom: | 20 |
| Strony: | 65 - 72 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 26 października 2025 |
| Abstrakty: | angielski |
| 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 sophist icated 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 principl es, 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 da ta - 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 in dividual 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 recommendatio ns. 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 deve lopment. |
