ENHANCING STUDENTS’ KNOWLEDGE COMPETENCE THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES: A METHODOLOGICAL APPROACH
- Authors
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Jamolova Gulbanbegim Muzaffar qizi
PhD, Associate Professor, Faculty of Pedagogy, University of Economics and Pedagogy, Non-State Higher Educational Institution, Karshi, Uzbekistan
Author
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- Keywords:
- Artificial Intelligence in Education (AIED), Knowledge Competence, Artificial Intelligence Technologies, Adaptive Learning Systems, Learning Analytics, Personalized Learning, Higher Education, Intelligent Tutoring Systems, Educational Data Mining, Digital Learning Environment.
- Abstract
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The rapid development of artificial intelligence (AI) technologies has significantly transformed modern educational practices, creating new opportunities for improving students’ knowledge competence. This study aims to develop and evaluate a methodological approach to enhancing students’ knowledge competence through the effective integration of artificial intelligence technologies in higher education. The research is based on the principles of personalized learning, adaptive educational environments, and data-driven decision-making in teaching and learning processes. A quasi-experimental research design was employed, involving undergraduate students divided into experimental and control groups. AI-based educational tools, including adaptive learning platforms, automated assessment systems, and learning analytics technologies, were implemented in the experimental group[1]. Data were collected through diagnostic tests, questionnaires, and observation methods, and analyzed using statistical techniques to determine the effectiveness of the proposed methodology. The results demonstrate that the integration of AI technologies significantly improves students’ academic performance, learning motivation, and knowledge competence compared to traditional instructional approaches. The study proposes a methodological framework that combines adaptive learning mechanisms, intelligent feedback systems, and pedagogical support. The findings contribute to the advancement of innovative teaching strategies and provide practical recommendations for integrating AI technologies into modern higher education systems.
- References
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1.Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 253–272). Cambridge University Press.
2.Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
3.D’Mello, S., & Graesser, A. (2013). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers. ACM Transactions on Interactive Intelligent Systems, 2(4), 1–39.
4.Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
5.Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Educational Technology Research and Development, 68(3), 1417–1430.
6.Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
7.Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599.
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- Published
- 2026-03-24
- Issue
- Vol. 2 No. 3 (2026)
- Section
- Articles
- License
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This work is licensed under a Creative Commons Attribution 4.0 International License.
