Sun’iy intellekt texnologiyalarining ta’limdagi o‘rni va nutqni baholashdagi imkoniyatlari
DOI:
https://doi.org/10.5281/zenodo.20956185Ключевые слова:
sun’iy intellekt, nutqni baholash, ta’lim texnologiyalari, nutqni tanish, avtomatik baholashАннотация
Ushbu maqolada sun’iy intellekt texnologiyalarining zamonaviy ta’lim tizimidagi o‘rni va ularning nutqni
avtomatik baholashdagi imkoniyatlari tahlil qilinadi. Nutqni tanish (Speech Recognition), tabiiy tilni qayta ishlash (NLP) va
avtomatik baholash tizimlarining ta’limga integratsiyasi ko‘rib chiqiladi. Toshkent shahridagi til o‘rgatish markazlarida olib
borilgan kuzatuv tadqiqotlari asosida AI asosidagi baholash vositalarining an’anaviy usullar bilan taqqoslanishi amalga
oshirildi. Ushbu texnologiyalar nutqni baholash aniqligini oshirishi, o‘qituvchi yuklamasini kamaytirishi va talabalar uchun
shaxsiylashtirilgan ta’lim imkoniyatlarini yaratishi aniqlandi. Nutqni baholash jarayonida fonetik tahlil, ohang va grammatik
to‘g‘rilikni aniqlashda sun’iy intellekt tizimlarining samaradorligi baholandi. Tadqiqot natijalari ko‘rsatishicha, AI texnologiyalari
nutqni baholashda 87 % gacha aniqlikka erishishi mumkin va bu ko‘rsatkich an’anaviy baholash usullaridan sezilarli
darajada yuqoridir.
Библиографические ссылки
1. Baxramova, M.M., 2025. The role of speech recognition AI in enhancing listening and pronunciation for Uzbek learners.
Ilm Fan Xabarnomasi, 8(2), 197-199.
2. Dulilyev, E.N., 2025. Sun’iy intellekt va chet tillarini o‘qitish - AI texnologiyalari ta’lim jarayonida qanday yordam bera
oladi? Science Shine, 7(42), 177-183.
3. Yuldasheva, G.N., 2024. Ta’lim tizimida raqamli texnologiyalar va sun’iy intelektdan foydalanish. Universal Xalqaro
Ilmiy Jurnal, 1(4), 748-751.
4. Jumamuratova, R., 2025. “Raqamli texnologiyalarning ta’limdagi o‘rni”. Современные подходы и новые исследования
в современной науке, 4(10), 140-142.
5. Bhatt, P., Jain, A., Bhatt, A., 2021. Multilingual automatic speech recognition: challenges and future directions. Journal
of Intelligent Systems 30(1), 612-628.
6. Brown, H.D., Douglas, D., 2004. Language assessment: principles and classroom practices. Pearson Education, New
York.
7. Chapelle, C.A., Douglas, D., 2006. Assessing language through computer technology. Cambridge University Press,
Cambridge.
8. Chen, X., Xie, H., Zou, D., Hwang, G.J., 2020. Application and theory gaps during the rise of artificial intelligence in
education. Computers and Education: Artificial Intelligence 1, 100002.
9. Heil, C.R., Wu, J.S., Lee, J.J., Schmidt, T., 2016. A review of mobile language learning applications: trends, challenges,
and opportunities. International Journal of Computer-Assisted Language Learning and Teaching 6(3), 1-13.
10. Hwang, G.J., Xie, H., Wah, B.W., Gašević, D., 2021. Vision, challenges, roles and research issues of Artificial Intelligence
in Education. Computers and Education: Artificial Intelligence 1, 100001.
11. Jurafsky, D., Martin, J.H., 2023. Speech and language processing: an introduction to natural language processing,
computational linguistics, and speech recognition, 3rd ed. Pearson, Stanford.
12. Karpagavalli, S., Chandra, E., 2016. A review on automatic speech recognition architecture and approaches. International
Journal of Signal Processing, Image Processing and Pattern Recognition 9(4), 393-404.
13. Lado, R., 1961. Language testing: the construction and use of foreign language tests. McGraw-Hill, New York.
14. Litman, D., 2016. Natural language processing for enhancing teaching and learning. Proceedings of the AAAI Conference
on Artificial Intelligence 30(1), 4170-4176.
15. Warschauer, M., Meskill, C., 2000. Technology and second language teaching. In J. Rosenthal (Ed.), Handbook of
undergraduate second language education. Lawrence Erlbaum, Mahwah, NJ, 303-318.
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