PEDAGOGIKA YO‘NALISHI TALABALARINI BAHOLASHDA SUN’IY INTELLEKT FOYDALANISHINING TAHLILI

Authors

  • Ikromov Xusan Xolmaxamatovich Andijon mashinasozlik instituti

Keywords:

sun’iy intellekt, baholash tizimi, avtomatlashtirilgan baholash, ta’lim texnologiyalari, pedagogika, talabalarni baholash

Abstract

Ushbu maqolada pedagogika yo‘nalishi talabalarini baholashda sun’iy intellekt (SI) texnologiyalaridan foydalanishning dolzarbligi, samaradorligi va natijalari tahlil qilinadi. An’anaviy baholash tizimlari bilan taqqoslaganda, sun’iy intellekt asosida baholash jarayoni aniqroq va obyektivroq natijalar beradi. Sun’iy intellekt tizimlari baholashning avtomatlashtirilgan jarayonini ta’minlab, o‘qituvchilarga va talabalarga ta’sir ko‘rsatadi, vaqtni tejash va xatoliklarni minimallashtirish imkonini yaratadi. Baholash natijalari aniq va to‘g‘ri bo‘lib, talabalarning yutuqlarini yanada samarali baholashga imkon beradi. Zamonaviy sun’iy intellekt vositalari yordamida avtomatlashtirilgan baholash tizimlari joriy etilishida, ularning amaliy tajribalari va istiqbollari o‘rganiladi. Bu tizimlar talabalarga shaxsiylashtirilgan ta’lim yordamida o‘zlashtirish darajasini aniqlashda samarali ishlashini ko‘rsatadi. Tadqiqot natijalari asosida baholash jarayonini yanada optimallashtirish uchun sun’iy intellekt texnologiyalaridan foydalanishni kengaytirish bo‘yicha tavsiyalar beriladi. Bu esa o‘qitish va o‘rganish jarayonlarini yanada samarali va aniq qiladi.

References

IKROMOV, X. (2024). TALABALARNI MA’LUMOTLAR BAZASINI BOSHQARISH ASOSIDA INNOVATSION AXBOROT TIZIMLARINI ISHLAB CHIQISHGA O ‘RGATISH METODIKASI. UzMU xabarlari, 1(1.1. 1.), 93-96.

Gaziyeva M., Xolmatova D. Publisistik matn sarlavhalarining pragmatik va lisoniy xususiyatlari // Journal of Advanced Research and Stability. — 2022. — № 12 (2). — B. 309–316.

Brown, T. (2018). AI in Education: The Role of Artificial Intelligence in Assessments. Springer.

Graham, P., Johnson, K., & Lee, S. (2022). Automated Grading Systems: Challenges and Opportunities. Elsevier.

Jones, M., & Smith, R. (2021). AI-Powered Assessment: A Comparative Study with Traditional Methods. Educational Technology Journal, 45(3), 213-230.

Kim, H., & Park, Y. (2022). The Future of AI in Higher Education Evaluation Systems. IEEE Transactions on Learning Technologies, 14(2), 112-126.

Li, Z., & Chen, W. (2021). Artificial Intelligence and Subjective Bias Reduction in Education. Journal of Educational Research, 38(4), 145-160.

Liu, Y., Wang, X., & Zhou, H. (2021). Machine Learning Models for Student Performance Assessment. ACM Transactions on Education, 17(1), 1-19.

McCarthy, J. (2019). Understanding AI Limitations in Evaluating Creative Work. Oxford University Press.

Nguyen, T., & Walker, B. (2019). Artificial Intelligence in Education: Efficiency vs. Creativity. Cambridge University Press.

Roberts, A. (2021). AI-Based Adaptive Learning and Assessment Tools in Modern Classrooms. Educational Innovations, 29(5), 321-337.

Stevenson, R., & Patel, M. (2023). Ethical Considerations in AI-Powered Grading Systems. Harvard Educational Review, 92(1), 78-96.

Wang, L., Patel, R., & Anderson, T. (2020). The Reliability of AI-Assisted Grading: A Meta-Analysis. Journal of Computer-Assisted Learning, 36(4), 502-517.

Zhang, H., Kumar, P., & Lee, J. (2020). Automated Grading Systems: Enhancing Accuracy and Reducing Bias. AI & Society, 35(2), 189-203.

Zhao, X., Green, C., & Miller, D. (2019). The Challenges of AI-Based Assessment in E-Learning Platforms. Journal of Online Learning, 41(3), 177-194.

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Published

2025-04-28

How to Cite

Ikromov, K. (2025). PEDAGOGIKA YO‘NALISHI TALABALARINI BAHOLASHDA SUN’IY INTELLEKT FOYDALANISHINING TAHLILI. DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 3(2), 40–45. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v3i26