Published July 1, 2025 | Version v1

METAKOGNITIV QOBILIYATLARNI RIVOJLANTIRISHDA SUN'IY INTELLEKTNING AHAMIYATI

  • 1. Qarshi xalqaro universiteti katta o'qituvchisi
  • 2. Abdulla Oripov nomidagi ijod maktabi o'qituvchisi

Description

Ushbu maqolada ta’lim jarayoni bo‘yicha ma’lumotlarga 
asoslangan ta’lim hamda sun’iy intellekt texnologiyalarining o‘zaro integratsiyasini 
tahlil qilinadi. Ikkita asosiy yondashuvning darslarni samarali o‘tilishiga ta’siri hamda 
o‘quvchilarning metakognitiv qobiliyatlarini rivojlantirishdagi o‘rni  yoritilgan. 
Shuningdek, pedagogik sinergiyaning model bilan birlashtirilganda, ularning kuchli 
tomonlaridan foydalanib, zaif tomonini muvozanatlashtirishni nazarda tutadi. 
Metakognitiv resurslardan foydalanish modeli ma’lumotlarga asoslangan ta’lim va 
sun’iy intellekt vositalarini ham qamrab olgan holda yangi yondashuv sifatida taklif 
etiladi. 
Metakognitiv resurslardan foydalanish modeli metakognitiv asoslarga tayangan 
bo‘lib, metakognitiv bilim va metakognitiv tartibga solish kabi asosiy o‘lchovni o‘z 
ichiga oladi. O‘quvchilarni turli resurslardan strategik va ongli foydalanishini 
ta’minlovchi pedagogik tavsiyalar berilgan.

Abstract (Russian)

В данной статье анализируется взаимосвязь данных
ориентированного обучения и технологий искусственного интеллекта в 
образовательном процессе. Рассматривается влияние двух основных подходов 
на эффективность обучения и развитие метакогнитивных способностей 
учащихся.  Предлагается модель педагогической синергии, которая объединяет 
их сильные стороны, компенсируя слабые.  Вводится новый подход – модель 
использования метакогнитивных ресурсов, охватывающая данные
ориентированное обучение и инструменты искусственного интеллекта. 
Модель использования метакогнитивных ресурсов основана на 
метакогнитивных основах, включая такие ключевые измерения, как 
метакогнитивные знания и метакогнитивное регулирование.  Предлагаются 
педагогические рекомендации, обеспечивающие стратегическое и осознанное 
использование учащимися различных ресурсов. 

Abstract (English)

This article analyzes the relationship between data-driven learning 
and artificial intelligence technologies in the educational process. It examines the 
influence of these two main approaches on the effectiveness of learning and the 
development of metacognitive skills in students. A model of pedagogical synergy is 
proposed, which combines their strengths while compensating for their weaknesses. A 
new approach is introduced – a model of using metacognitive resources, encompassing 
data-driven learning and artificial intelligence tools. 
The model of using metacognitive resources is based on metacognitive 
foundations, including key dimensions such as metacognitive knowledge and metacognitive regulation.  Pedagogical recommendations are provided to ensure 
strategic and conscious use of various resources by students. 

Files

5. Farmonov D.P., Nigmatov I. Sh. - METAKOGNITIV QOBILIYATLARNI RIVOJLANTIRISHDA SUN’IY INTELLEKTNING AHAMIYATI.pdf

Additional details

Additional titles

Alternative title (Russian)
РОЛЬ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В РАЗВИТИИ МЕТАКОГНИТИВНЫХ СПОСОБНОСТЕЙ
Alternative title (English)
ROLE OF ARTIFICIAL INTELLIGENCE IN THE DEVELOPMENT OF METACOGNITIVE SKILLS

Software

Repository URL
https://infocom.uz/magazine/18
Development Status
Active

References

  • 1. O'zbekiston Resbuplikasi Prezidentining 2025-yil 26-martdagi "O'zbekiston Respublikasi Prezidentining yoshlar bilan ochiq muloqotida belgilangan vazifalarni amalga oshirishga doir chora-tadbirlar to'g'risida"gi PF-61-son Farmoni. https://lex.uz/uz/docs/-7451522?query=sun%E2%80%99iy%20intellekt#sr-1 2. Boulton, A. (2012). What data for data-driven learning? EuroCALL Review: Proceedings of the EUROCALL 2011 Conference, 20, 23–27. http://eurocall.webs.upv.es/documento s/newsletter/papers_20(1)/07_boulton.pdf. 3. Geluso, J., 2013. Phraseology and frequency of occurrence on the web: Native speakers' perceptions of Google-informed second language writing.
  • 4. Sha, G., 2010. Using Google as a super corpus to drive written language learning: A comparison with the British National Corpus. Computer Assisted Language Learning 23 (5), 377–393. https://doi.org/10.1080/09588221.2010.514576. 5. Crosthwaite, P., Baisa, V., 2023. Generative AI and the end of corpus-assisted data-driven learning? Not so fast! Applied Corpus Linguistics 3 (3), 100066. https://doi.org/ 10.1016/j.acorp.2023.100066. 6. Lee, H., Warschauer, M., Lee, J.H., 2019. The effects of corpus use on second language vocabulary learning: A multilevel meta-analysis. Applied Linguistics 40 (5), 721–753. https://doi.org/10.1093/applin/amy012. 7. O'Keeffe, A., 2021. Data-driven learning: A call for a broader research gaze. Language Teaching 54, 259–272. https://doi.org/10.1017/S0261444820000245. 8. Perez-Paredes, P., 2022. A systematic review of the uses and spread of corpora and data- ´ driven learning in CALL research during 2011–2015. Computer Assisted Language Learning 35 (1–2), 36–61. https://doi.org/10.1080/09588221.2019.1667832. 9. Sato, M, 2022. Metacognition. In: Li, S., Hiver, P., Papi, M. (Eds.), The Routledge Handbook of Second Language Acquisition and Individual Differences. Routledge, pp. 95–110. https://doi.org/10.4324/9781003270546-8. 10. Flavell, J.H., 1979. Metacognition and cognitive monitoring: A new area of cognitive- developmental inquiry. American Psychologist 34 (10), 906–911. https://doi.org/ 10.1037/0003-066X.34.10.906. 11. Teng, M.F., Zhang, L.J., 2021. Development of children's metacognitive knowledge, reading, and writing in English as a foreign language: Evidence from longitudinal data using multilevel models. British Journal of Educational Psychology 91 (4), 1202–1230. https://doi.org/10.1111/bjep.12413 12. Chamot, A.U., 2009. The CALLA handbook: Implementing the cognitive academic language learning approach. Pearson Education. 13. Atsushi Mizumoto-"Data-driven Learning Meets Generative AI: Introducing the Framework of Metacognitive Resource Use" 2023. 14. Ishnazarov A. Nurullayeva Sh. (2021) "Ekonometrikaga kirish" o'quv qo'llanma.