SUNʼIY INTELLEKT ASOSIDA ESHITUV-NUTQ KOʻNIKMALARI MONITORINGI
Keywords:
sun’iy intellekt, eshituv-nutq ko‘nikmalari, monitoring, koxlear implant, nutqni tanib olish, machine learning, raqamli diagnostika, adaptiv baholash, korreksion pedagogika, inklyuziv ta’lim, ta’lim analitikasi, aqlli monitoring tizimi, eshituv reabilitatsiyasi, nutq rivojlanishi, avtomatlashtirilgan baholash, pedagogik texnologiyalar..Abstract
Mazkur maqolada sun’iy intellekt asosida eshituv-nutq ko‘nikmalarini monitoring qilishning pedagogik, psixologik va texnologik imkoniyatlari tahlil qilingan. Eshitishida nuqsoni bo‘lgan hamda koxlear implantdan foydalanuvchi bolalarda eshituv va nutq rivojlanishini muntazam baholash korreksion-pedagogik jarayonning muhim tarkibiy qismi hisoblanadi. Zamonaviy sun’iy intellekt texnologiyalarining rivojlanishi eshituv-nutq ko‘nikmalarini real vaqt rejimida kuzatish, diagnostika qilish va rivojlanish dinamikasini avtomatik tahlil etish imkoniyatlarini yaratmoqda. Tadqiqotda sun’iy intellekt asosidagi monitoring tizimlarining nutqni tanib olish, eshituv reaksiyalarini qayd etish, individual rivojlanish trajektoriyalarini shakllantirish hamda korreksion dasturlar samaradorligini baholashdagi imkoniyatlari o‘rganildi. Ilmiy manbalarni nazariy tahlil qilish, pedagogik kuzatish, raqamli monitoring, ekspert baholash va eksperimental metodlar asosida olib borilgan tadqiqot natijalari sun’iy intellekt texnologiyalari eshituv-nutq rivojlanishini baholashning aniqligi va tezkorligini sezilarli darajada oshirishini ko‘rsatdi. Shuningdek, individual pedagogik yondashuvlarni takomillashtirish, reabilitatsiya samaradorligini monitoring qilish va korreksion ta’lim sifatini oshirishda sun’iy intellektning muhim ahamiyati asoslandi.
References
Russell S., Norvig P. Artificial Intelligence: A Modern Approach. 4th Edition. – Boston: Pearson Education, 2021.
Luckin R. Machine Learning and Human Intelligence: The Future of Education for the 21st Century. – London: UCL Institute of Education Press, 2018.
Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. – Boston: Center for Curriculum Redesign, 2019.
Baker R.S. Learning Analytics and Educational Data Mining. In: International Encyclopedia of Education. – Oxford: Elsevier, 2023.
Siemens G., Long P. Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review. 2011. Vol. 46(5). pp. 30–40.
Woolf B.P. Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-learning. – Burlington: Morgan Kaufmann, 2021.
Ferguson R. Learning Analytics: Drivers, Developments and Challenges. International Journal of Technology Enhanced Learning. 2019. Vol. 4(5–6). pp. 304–317.
Rose Luckin, Holmes W., Griffiths M., Forcier L.B. Intelligence Unleashed: An Argument for AI in Education. – London: Pearson, 2016.
D’Mello S., Graesser A. AutoTutor and Affective Autotutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers. ACM Transactions on Interactive Intelligent Systems. 2015. Vol. 2(4). pp. 1–39.
Marschark M., Spencer P.E. The Oxford Handbook of Deaf Studies in Learning and Cognition. – Oxford: Oxford University Press, 2018.
Nittrouer S. Speech Perception Development in Children with Hearing Loss. – San Diego: Plural Publishing, 2020.
Po‘latova P.M. Maxsus pedagogika va inklyuziv ta’lim asoslari. – Toshkent: Fan va texnologiyalar, 2022.
Ayupova M.Y. Korreksion pedagogika va reabilitatsiya texnologiyalari. – Toshkent: Innovatsion rivojlanish nashriyoti, 2023.
Karimova V.M. Ta’lim psixologiyasida raqamli texnologiyalar. – Toshkent: Tafakkur, 2023.
Nishonova Z.T. Pedagogik psixologiya. – Toshkent: O‘zbekiston faylasuflari milliy jamiyati, 2021.
Downloads
Published
Issue
Section
How to Cite
