DIDACTIC SYSTEM FOR TEACHING STUDENTS BASED ON A NEURAL NETWORK MODEL
This article examines the theoretical and practical aspects of using neural network models and artificial intelligence technologies in student learning. The study analyzes the role of artificial neural networks in individualizing the learning process, automatically assessing student knowledge, adapting learning materials, and improving efficiency. The proposed neural network-based learning system enables real-time monitoring of student learning activities, identifying knowledge gaps, and creating a personalized learning roadmap
1. LeCun, Y., Bengio, Y., Hinton, G. Deep learning // Nature. – 2015. – №521. – B. 436–444.
2. Brusilovsky, P. Adaptive and intelligent technologies for web-based education // International Journal of Artificial Intelligence in Education. – 2018. – Vol. 29(2). – P. 1–28.
3. Baker, R., Siemens, G. Learning analytics and educational data mining: Towards communication and collaboration // Proceedings of the 10th International Conference on Educational Data Mining. – 2020. – P. 1–10.
4. Luckin, R., Holmes, W., Griffiths, M., Forcier, L. Intelligence Unleashed: An Argument for AI in Education. – London: Pearson Publishing, 2021. – 56 b.
5. Abdurahmonov, M. Raqamli pedagogika: nazariya va amaliyot. – Toshkent: Innovatsion rivojlanish nashriyoti, 2021. – 210 b.
6. Ashirova A., Yusupov D. Neyrotarmoq texnologiyasi asosida baholash jarayonini avtomatlashtirish.-UzMU xabarlari.2025. №1/4/1-B.62-64.
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