PROSPECTS AND FUTURE OF NEURAL NETWORK TECHNOLOGIES IN EDUCATION
Abstract
Neural network technologies, a subset of artificial intelligence (AI), are increasingly transforming the educational landscape. As machine learning models evolve, the potential to revolutionize how we learn, teach, and engage with knowledge has become more apparent. This article explores the prospects and future of neural network technologies in education, highlighting their current applications, challenges, and anticipated developments.
References
1. Baker, R. S., & Inventado, P. S. (2014). "Educational Data Mining and Learning Analytics." In Learning Analytics. Springer.
2. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
3. Камалова, Ш. Н., & Элмуратова, Н. Х. (2024). ИСТОРИЧЕСКИЕ АСПЕКТЫ ПЕРЕВОДА: ОСНОВНЫЕ ЭТАПЫ РАЗВИТИЯ. JOURNAL OF INNOVATIONS IN SCIENTIFIC AND EDUCATIONAL RESEARCH, 7(10), 46-54.
4. Zawacki-Richter, O., & Anderson, T. (2014). "Emerging Technologies in Distance Education." Journal of Distance Education Technologies.
5. Nugmanovna, K. S. (2023). INVESTIGATING THE PECULIARITY OF MODAL VERBS IN ENGLISH: A LINGUISTIC ANALYSIS.
6. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213490706
7. Hotamovna, F. (2024). Cognitive-semantic analysis of technical terms combinations and their expression for training engineers. In BIO Web of Conferences (Vol. 84, p. 04027). EDP Sciences.
