METHODOLOGY FOR USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN PRIMARY SCHOOL MATHEMATICS LESSONS
DOI:
https://doi.org/10.5281/zenodo.20983063Keywords:
artificial intelligence, primary education, mathematics lessons, teaching methodology, adaptive learning, digital technologies, mathematical competence, personalized learning, interactive learning, automated assessment.Abstract
. This article examines the methodology for integrating artificial intelligence technologies into primary school mathematics lessons from a pedagogical and methodological perspective. The study explores the didactic potential of AI-based educational tools, emphasizing their role in improving mathematics instruction, enhancing students’ mathematical competencies, and increasing overall learning effectiveness. Particular attention is given to adaptive learning systems, personalized instruction, automated assessment, interactive learning activities, and intelligent digital educational resources. The paper discusses methodological approaches to using AI technologies for diagnosing students’ learning progress, providing individualized feedback, and implementing differentiated instruction according to learners’ needs and achievement levels. The findings indicate that the systematic and pedagogically grounded application of artificial intelligence technologies contributes to the development of logical thinking, mathematical literacy, problem-solving abilities, and independent learning skills among primary school students. Furthermore, the study highlights the importance of strengthening teachers’ digital competencies and presents practical recommendations for the effective implementation of AI-supported teaching strategies in primary mathematics education. The proposed methodology demonstrates that the appropriate integration of artificial intelligence can significantly improve the quality, accessibility, and learner-centered nature of mathematics instruction in primary education.
References
1. Asmolov A.G. Psikhologiya lichnosti: kul'turno-istoricheskoe ponimanie razvitiya cheloveka. – Moscow: Smysl, 2019. – 448 p.
2. Baker R.S.J.d., Inventado P.S. Educational Data Mining and Learning Analytics // Learning Analytics: From Research to Practice / Ed. by J.A. Larusson, B. White. – New York: Springer, 2014. – P. 61–75.
3. Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. – Boston: Center for Curriculum Redesign, 2018. – 56 p.
4. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. – Boston: Center for Curriculum Redesign, 2019. – 56 p.
5. Luckin R. Machine Learning and Human Intelligence: The Future of Education for the 21st Century. – London: UCL Institute of Education Press, 2018. – 184 p.
6. OECD. Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. – Paris: OECD Publishing, 2023. – 220 p.
7. Robert I.V. Teoriya i metodika informatizatsii obrazovaniya (psikhologo-pedagogicheskiy i tekhnologicheskiy aspekty). – 3-e izd. – Moscow: BINOM. Laboratoriya znaniy, 2014. – 398 p.
8. Selwyn N. Should Robots Replace Teachers? AI and the Future of Education. – Cambridge: Polity Press, 2019. – 168 p.
9. UNESCO. Guidance for Generative AI in Education and Research. – Paris: UNESCO, 2023. – 64 p.
10. UNESCO. Recommendation on the Ethics of Artificial Intelligence. – Paris: UNESCO, 2022. – 48 p.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Umida Khuddiyeva
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain copyright and grant the journal the right to first publication. The license permits others to share, adapt, and build upon the work as long as appropriate credit is given to the original author(s) and the source.