Personalized learning through AI: Pedagogical approaches and critical insights

Klarisa I. Vorobyeva; Svetlana Belous; Natalia V. Savchenko; Lyudmila M. Smirnova; Svetlana A. Nikitina; Sergei P. Zhdanov

doi:10.30935/cedtech/16108

Klarisa I. Vorobyeva ¹, Svetlana Belous ², Natalia V. Savchenko ³, Lyudmila M. Smirnova ⁴, Svetlana A. Nikitina ⁵, Sergei P. Zhdanov ⁶ ⁷ ^*

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CONT ED TECHNOLOGY, Volume 17, Issue 2, Article No: ep574

https://doi.org/10.30935/cedtech/16108

Submitted: 08 December 2024, Published Online: 10 March 2025

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Abstract

In this analysis, we review artificial intelligence (AI)-supported personalized learning (PL) systems, with an emphasis on pedagogical approaches and implementation challenges. We searched the Web of Science and Scopus databases. After the preliminary review, we examined 30 publications in detail. ChatGPT and machine learning technologies are among the most often utilized tools; studies show that general education and language learning account for the majority of AI applications in the field of education. Supported by particular learning approaches stressing student characteristics and expectations, the results show that automated feedback systems and adaptive content distribution define AI’s educational responsibilities mostly. The study notes major difficulties in three areas: technical constraints and data privacy concerns; educational and pragmatic barriers. Although curriculum integration and teacher preparation are considered major concerns, pedagogical challenges come first above technology integration. The results also underline the need for thorough professional development activities for teachers and AI tools for especially targeted instruction. The study shows that the efficient application of AI-enabled PL requires a comprehensive strategy addressing technological, pedagogical, and ethical issues all at once. These results help to describe the current state of AI in education and provide ideas for future developments as well as techniques for its use.

Keywords: artificial intelligence, personalized learning, adaptive learning, intelligent tutoring systems, ethics in AI education

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Citation

Vorobyeva, K. I., Belous, S., Savchenko, N. V., Smirnova, L. M., Nikitina, S. A., & Zhdanov, S. P. (2025). Personalized learning through AI: Pedagogical approaches and critical insights. Contemporary Educational Technology, 17(2), ep574. https://doi.org/10.30935/cedtech/16108

Vorobyeva, K. I., Belous, S., Savchenko, N. V., Smirnova, L. M., Nikitina, S. A., and Zhdanov, S. P. (2025). Personalized learning through AI: Pedagogical approaches and critical insights. Contemporary Educational Technology, 17(2), ep574. https://doi.org/10.30935/cedtech/16108

Vorobyeva KI, Belous S, Savchenko NV, Smirnova LM, Nikitina SA, Zhdanov SP. Personalized learning through AI: Pedagogical approaches and critical insights. CONT ED TECHNOLOGY. 2025;17(2), ep574. https://doi.org/10.30935/cedtech/16108

Vorobyeva, Klarisa I., Svetlana Belous, Natalia V. Savchenko, Lyudmila M. Smirnova, Svetlana A. Nikitina, and Sergei P. Zhdanov. "Personalized learning through AI: Pedagogical approaches and critical insights". Contemporary Educational Technology 2025 17 no. 2 (2025): ep574. https://doi.org/10.30935/cedtech/16108

Vorobyeva, Klarisa I. et al. "Personalized learning through AI: Pedagogical approaches and critical insights". Contemporary Educational Technology, vol. 17, no. 2, 2025, ep574. https://doi.org/10.30935/cedtech/16108

Vorobyeva KI, Belous S, Savchenko NV, Smirnova LM, Nikitina SA, Zhdanov SP. Personalized learning through AI: Pedagogical approaches and critical insights. CONT ED TECHNOLOGY. 2025;17(2):ep574. https://doi.org/10.30935/cedtech/16108