Tudásfejlesztés az alapoktatásban a mesterséges intelligencia támogatásával

Keywords: Iteracy, adaptive learning, question generation, gamification

Abstract

Nowadays, the emergence of artificial intelligence in various areas of life is not surprising at all, as there is no area where generative AI can­not bring something new. Education is no exception, and in fact, it can be applied very effectively and widely in this field. In this study, the author presents some examples of applications that have appeared in basic educa­tion. The applications are presented mainly through international examples, with a detailed description of each solution. The author discusses question generation, personalized education, gamification, and their applicability and connection to increasing literacy levels. In the summary, the author men­tions the most important area where the greatest progress is expected in pri­mary education and explains his opinion on how this may affect the role of teachers and educators.

References

Varga A. (2024): Az esélyegyenlőség fenntarthatósági aspektusai. In: Balázs László– Rajcsányi-Molnár Mónika–András István–Keszi-Szeremlei Andrea (Szerk.): A jövő fenntartható közgazdaságtana. Dunaújváros: DUE Press, pp. 53–63.

Varga A. (2023): A szervezeti kultúra versus esélyegyenlőség. Civil Szemle, 20., (7.), pp. 61–75.

Pedagógiai lapok, 13., (2025), Ősz – Oktatás 6. Budapest: Oktatási Hivatal. https://www.oktatas.hu/pub_bin/dload/kozoktatas/pok/Budapest/2025/pedagogiai_lapok13_osz_okt6.pdf

Bachiri, Y. A.– Mouncif, H.–Bouikhalene, B. (2023): Artificial intelligence empowers gamification: Optimizing student engagement and learning outcomes in e-learning and moocs. International Journal of Engineering Pedagogy, 13., (8.). https://online-journals.org/index.php/i-jep/article/view/40853/14311

Bachiri, Y. A.– Mouncif, H.–Bouikhalene, B. (2023): Artificial intelligence empowers gamification: Optimizing student engagement and learning outcomes in e-learning and moocs. International Journal of Engineering Pedagogy, 13., (8.). https://online-journals.org/index.php/i-jep/article/view/40853/14311

Tarus, J. K.–Niu, Z.–Yousif, A. (2017): A hybrid knowledgebased recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72., pp. 37–48.

Awad, S. O.–Mohamed, Y.–Shaheen, R. (2022): Applications of artificial intelligence in education. Al-Azkiyaa- International Journal of Language and Education, 1., (1.), pp. 71–81. https://azkiyaa.usim.edu.my/index.php/jurnal/article/view/10/7

Nugraheni, E.– Pangaribuan, N. (2006): Gaya belajar dan strategi belajar mahasiswa jarak jauh: Kasus di Universitas Terbuka. Jurnal pendidikan terbuka dan jarak jauh, 7., (1.), pp. 68–82.

Koren, Y.–Bell, R. –Volinsky, C. (2009): Matrix factorization techniques for recommender systems. Computer, 42., (8.), pp. 30–37. https://ieeexplore.ieee.org/abstract/document/5197422

Pardamean, B.– Suparyanto, T.–Cenggoro, T. W.–Sudigyo, D.–Anugrahana, A. (2022): AI-based learning style prediction in online learning for primary education. IEEE Access, 10., pp. 35725–35735. https://ieeexplore.ieee.org/stamp/stamp.

Farkas, I. (2024): Literacy levels in small villages in relation to local primary school. Journal of Applied Technical and Educational Sciences, 14., (1.), pp. 1–23. https://real.mtak.hu/201286/1/374-Article%20Text-1258-1-10-20240414.pdf

Published
2026-01-22
How to Cite
FarkasI. (2026). Tudásfejlesztés az alapoktatásban a mesterséges intelligencia támogatásával. Dunakavics, 14(1), 29-42. https://doi.org/10.63684/dk.2026.01.03
Section
Cikkek