Psychological and pedagogical effects of the implementation of artificial intelligence technologies in educational practice

Context and Relevance. In recent years, artificial intelligence technologies have become an integral part of educational processes across all levels, from primary to professional education. Their role has grown particularly significant in the post-pandemic period, when digitalization accelerated and the demand for new tools of personalization and learning support intensified. Objective. To identify the psycho-pedagogical effects and current trends in the application of AI technologies in education. Hypothesis. It was assumed that the use of AI in education can be classified into key domains, and that its integration would contribute to enhancing the efficiency and quality of learning through personalization and optimization of processes, while at the same time being associated with a range of risks. Methods and Materials. A systematic review was conducted of articles in English indexed in Web of Science, Scopus databases between 2005 and 2025. Only peer-reviewed publications were included, which ensured the rigor and reliability of the analysis. Results. Six key areas of AI application in education were identified: intelligent tutoring systems, educational robots, dialog agents, learning analytics, automated assessment, and administrative and career guidance systems. Most studies report positive effects such as improved academic performance, increased engagement, and higher levels of personalization. At the same time, a number of challenges were noted, including algorithmic bias, ethical issues, data privacy concerns, teacher resistance, and the risk of digital dependency. Conclusions. AI technologies are becoming a significant factor in the transformation of education, strengthening its adaptability and effectiveness. Their successful integration requires a responsible approach that addresses the emerging risks and challenges.

1. Соколов, Н.В., Виноградский, В.Г. (2022). Искусственный интеллект в образовании: анализ, перспективы и риски в РФ. Проблемы современного педагогического образования, 76(2), 166—169. URL: https://www.elibrary.ru/item.asp?id=49809360 (дата обращения: 15.05.2025).
   Sokolov, N.V., Vinogradsky, V.G. (2022). Artificial intelligence in education: Analysis, prospects, and risks in the Russian Federation. Problems of Modern Pedagogical Education, 76(2), 166—169. (In Russ.). URL: https://www.elibrary.ru/item.asp?id=49809360 (дата обращения: 15.05.2025).
2. Adiguzel, T., Mehmet, H.K., Fatih, K.C. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), Article ep429. https://doi.org/10.30935/cedtech/13152
3. Akhmadieva, R.Sh., Kalmazova, N.A., Belova, T., Prokopyev, A., Molodozhnikova, N.M., Spichak, V.Y. (2024). Research trends in the use of artificial intelligence in higher education. Frontiers in Education, 9, Article 1438715. https://doi.org/10.3389/feduc.2024.1438715
4. Bankins, S., Jooss, S., Restubog, S.L.D., Marrone, M., Ocampo, A.C., Shoss, M. (2024). Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research. Journal of Vocational Behavior, 153, Article 104011. https://doi.org/10.1016/j.jvb.2024.104011
5. Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), Article aat5954. https://doi.org/10.1126/scirobotics.aat5954
6. Bravo Perucho, A., Alimardani, M. (2023). Social robots in secondary education: Can robots assist young adult learners with math learning? In: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (pp. 355—359). New York: Association for Computing Machinery. https://doi.org/10.1145/3568294.3580105
7. 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
8. Chiu, T.K.F., Xia Q., Zhou, X., Chai, C. S., Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118
9. Dai, W., Tsai, Y.-S., Lin, J., Aldino, A., Jin, H., Li, T., Gašević, D., & Chen, G. (2024). Assessing the proficiency of large language models in automatic feedback generation: An evaluation study. Computers and Education: Artificial Intelligence, 7, Article 100299. https://doi.org/10.1016/j.caeai.2024.100299
10. Eke, D.O. (2023). ChatGPT and the rise of generative AI: Threat to academic integrity? Journal of Responsible Technology, 13, Article 100060. https://doi.org/10.1016/j.jrt.2023.100060
11. Evripidou, S., Georgiou, K., Doitsidis, L., Amanatiadis, A.A., Zinonos, Z., Chatzichristofis, S.A. (2020). Educational robotics: Platforms, competitions and expected learning outcomes. IEEE Access, 8, 219534—219562. https://doi.org/10.1109/ACCESS.2020.3042555
12. Halkiopoulos, C., Gkintoni, E. (2024). Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology — A systematic analysis. Electronics, 13(18), Article 3762. https://doi.org/10.3390/electronics13183762
13. Garzón, J., Patiño, E., Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), Article 84. https://doi.org/10.3390/mti9080084
14. Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., Zhou, R. (2021). Evolution and trends in intelligent tutoring systems research: A multidisciplinary and scientometric view. Asia Pacific Education Review, 22(3), 441—461. https://doi.org/10.1007/s12564-021-09697-7
15. Guo, S., Zheng, Y., Zhai, X. (2024). Artificial intelligence in education research during 2013—2023: A review based on bibliometric analysis. Education and Information Technologies, 29, 16387—16409. https://doi.org/10.1007/s10639-024-12491-8
16. Lampropoulos, G. (2025). Social robots in education: Current trends and future perspectives. Information, 16(1), Article 29. https://doi.org/10.3390/info16010029
17. Lo, C.K., Hew, K.F., Jong, M.S.Y. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers & Education, 219, Article 105100. https://doi.org/10.1016/j.compedu.2024.105100
18. Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901—918. https://doi.org/10.1037/a0037123
19. Mao, J., Chen, B., Liu, J.C. (2023). Generative artificial intelligence in education and its implications for assessment. TechTrends, 68, 58—66. https://doi.org/10.1007/s11528-023-00911-4
20. Waheed, H., Hassan, S.U., Aljohani, N.R., Hardman, J., Alelyani, S., Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, Article 106189. https://doi.org/10.1016/j.chb.2019.106189
21. Wang, F., Cheung, A.C.K., Neitzel, A.J., Chai, C.S. (2025). Does chatting with chatbots improve language learning performance? A meta-analysis of chatbot-assisted language learning. Review of Educational Research, 95(4), 623—660. https://doi.org/10.3102/00346543241255621
22. Yan, L., Greiff, S., Teuber, Z., Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850. https://doi.org/10.1038/s41562-024-02004-5
23. Zawacki-Richter, O., Marín, V.I., Bond, M., Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education — Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), Article 39. https://doi.org/10.1186/s41239-019-0171-0
24. Zhai, X., Chu, X., Chai, C.S., Jong, M.S.Y., Spector, J.M. Liu, J.-B., Yuan, J., Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, Article 8812542. https://doi.org/10.1155/2021/8812542