Authors
- Tareva Elena Genrikhovna Doctor of Pedagogy, full professor Russian Federation Moscow
- Vishnevetskaya Natalya V. PhD (Pedagogy) Russian Federation Moscow
- Korobko Darya A. Russian Federation Moscow
Annotation
In the context of widespread digitalization, covering all levels of societal functioning, the need to find new ways of optimizing educational systems is growing. The field
of education, traditionally receptive to the integration of innovations, is beginning to actively adapt solutions developed in the business sector regarding the implementation of AI agents, which are currently considered a qualitatively new stage in the development of neural network technologies. The purpose of this article is to conduct a comparative analysis of foreign approaches to the use of AI agents, based on studying the experience of the People’s Republic of China, the Republic of Korea, and the United States of America, in order to identify the main areas of development in this field, as well as to determine possible vectors for the development of this issue within the educational system of the Russian Federation. A particular line of reasoning about the potential of AI agents pertains to the field of foreign language teaching — an area traditionally at the forefront of any educational innovations and transformations. Analyzing the experience of using AI tools at the levels of secondary and higher education, the authors identify the most successful initiatives and present effective practices for the use of AI assistants. Based on the results obtained, the functions and key tasks of applying agentic AI technologies in education are highlighted, grouped into several areas: communication and student support; improving the quality of the educational process; and creating individualized educational trajectories both in and outside the classroom. Emphasis is placed on specific specialized functions of using AI agents: psychological support for teachers, assistance in writing research papers for academic staff, and others. With regard to foreign language teaching, the potential of AI agents in developing various language skills of students within the targeted process of school and university education is analyzed. The article also addresses the issue of ethical norms for the use of AI assistants in the educational sphere. Based on the research findings, two sets of recommendations are formulated: recommendations for designing agentic systems based on advanced technological mechanisms, thoughtful pedagogical design, and regulatory-ethical
aspects that are taken into account in countries that have already gone through the initial phase of integrating agentic technologies into education; and recommendations on possible directions for the use of AI agents in the educational sphere of the Russian Federation in general and in language education in particular.
How to link insert
Tareva, E. G., Vishnevetskaya, N. V. & Korobko, D. A. (2026). AI AGENTS: GLOBAL PRACTICES AND THE POTENTIAL FOR LANGUAGE EDUCATION Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", 2 (62), 184. https://doi.org/10.24412/2076-913X-2026-262-184-205
References
1.
1. Tivyaeva, I. V., & Mikhaylova, S. V. (2025). Artificial Intelligence — a Fashion Trend or Real Help for a Teacher? Russkaya slovesnost’, (1), 3–10. (In Russ.).
2.
2. Qu, X., Damoah, A., Sherwood, J., Liu, P., Shun Jin, Ch., Chen, L., Shen, M., Aleisa, N., Hou, Z., Zhang, Ch., Gao, L., Li, Y., Yang, Q., Wang, Q., & De Souza, Ch. (2025). A Comprehensive Review of AI Agents: transforming possibilities in technology and beyond. Preprint: arXiv: 2508.11957 (2026, March 1). https://arxiv.org/abs/2508.11957
3.
3. MIT Technology Review. Are we ready to hand AI agents the keys? (2025). (2026, March 1). https://www.technologyreview.com/2025/06/12/1118189/ai-agents-manus-control-autonomy-operator-openai/
4.
4. Berman, N. D. (2025). Overcoming the challenges of implementing AI agents in the educational environment: Focus on ethical aspects and risks. CITISĖ, 3(45), 166–177. (In Russ.).
5.
5. Effective context engineering for AI agents. (2025). (2026, March 1). https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents?clckid=7012eb82
6.
6. Allmendinger, S., Bonenberger, L., Endres, K., Fetzer, D., Gimpel, H & Kühl, N. (2026). Multi-agent AI. Electron Markets, (36), 18.
7.
7. Uchoa, A. P., Oliveira, C. E. T., Motta, C. L. R., & Schneider, D. (2026). Multi-stakeholder Alignment in LLM-Powered collaborative AI Systems: a multi-agent framework for intelligent tutoring. In J. F. Krems, H. P. da Silva, P. Cipresso (Eds.). Computer-Human Interaction Research and Applications. CHIRA. Communications in Computer and Information Science. Springer, Cham, 2835, 360–379.
8.
8. Hieroglyphs. (05), 63–75. = Wu, Y., Jiang, Y., Chen, Y., & Zhang, W. (2024). Multi-agent systems supported by large language models: technological pathways, educational applications, and future prospects. Kaifang jiaoyu yanjiu, (5), 63–75. (In Chinese).
9.
9. Hieroglyphs. (2017). = Notice of the State Council on Issuing the Development Plan for a New Generation of Artificial Intelligence. The State Council. (2026, March 1). https://www.beijing.gov.cn/zhengce/zhengcefagui/201905/t20190522_60314.html (In Chinese).
10.
10. (Hieroglyphs. 2021). = Personal Information Protection Law of the People’s Republic of China, adopted at the 30th Meeting of the Standing Committee of the 13th National People’s Congress. (2026, March 1). http://www.npc.gov.cn/npc/c2/c30834/202108/t20210820_313088.html (In Chinese).
11.
11. Hieroglyphs. (2023). = Interim Measures for the Management of Generative Artificial Intelligence Services, adopted by the Cyberspace Administration of China, the National Development and Reform Commission, the Ministry of Education, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the National Radio and Television Administration. (2026, March 1). https://www.cac.gov.cn/2023-
07/13/c_1690898327029107.htm. (In Chinese).
12.
12. Hieroglyphs. (2025). = Announcement on Collecting the «University Teaching Intelligent Agent Tools and Application Case Catalog». China Association of Higher Education. (2026, March 1). https://heec.cahe.edu.cn/news/yaowen/19315.html. (In Chinese).
13.
13. Initiatives for the Development of AI in the Republic of Korea. National Research University Higher School of Economics. (2025). (2026, March 1). https://issek.hse.ru/news/1079272634.html
14.
14. Asim, S., Kim, H., & Aedo, C. (2024). Teachers are leading an AI revolution in Korean classrooms. WorldBank. (2026, March 1). https://blogs.worldbank.org/en/education/teachers-are-leading-an-ai-revolution-in-korean-classrooms
15.
15. America’s AI Action plan. (2025). (2026, March 1). https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf
16.
16. Vijayvargiya, A., Huang, S., Saharia, C., Yeh, C., Chen, M., Zhao, Z., & Lee, H. (2025). OpenAgentSafety: A comprehensive framework for evaluating real-world AI agent safety. arXiv. (2026, March 1). https://arxiv.org/html/2507.06134v1
17.
17. Riedl, M. O., & Desai, D. R. (2025). AI Agents and the Law, 8(3), 2189–2198. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
18.
18. The AI Agent Index. (2025). (2026, March 1). https://aiagentindex.mit.edu/
19.
19. Tamascelli, M. O., Bunch, J., Fowler, R., Taeb, F., & Cohen, R. (2025). Academic advising chatbot powered with AI agent. Proceedings of the 2025 ACM Southeast Conference (ACM SE ’25) (p. 212–217).
20.
20. Dieker, L., Hines, R., Wilkins, I., Hughes, C., Hawkins Scott, K., Smith, S., & Shah, S. (2024). Using an Artificial Intelligence (AI) Agent to support teacher instruction and student learning. Journal of Special Education Preparation, 4(2), 78–88.
21.
21. Yao, H., Xu, W., Turnau, J., Kellam, N., & Wei, H. (2026) Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design. arXiv. (2026, March 1). https://arxiv.org/abs/2508.19611
22.
22. The challenges of governing AI Agents. AI Frontiers (2025). (2026, March 1). https://ai-frontiers.org/articles/the-challenges-of-governing-ai-agents?clckid=955942c1
23.
23. Correia, A. P., Hickey, S., & Xu, F. (2025). Realizing the possibilities of the large language models: Strategies for prompt engineering in educational inquiries. Theory Into Practice, 64(4), 434–447.
24.
24. Yu, J., Zhang, Z., Zhang-li, D., Tu, S., Hao, Z., Miao Li, R., Li, H., Wang, Y., Li, H., Gong, L., Cao, J., Lin, J., Zhou, J., Qin, F., Wang, H., Jiang, J., Deng, L., Zhan, Y., Xiao, C., Dai, X., et al. (2024). From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents. Preprint: arXiv: 2409.03512. (2026, March 1). https://arxiv.org/abs/2409.03512
25.
25. AI Agents Are Set to Transform Higher Education — Here’s How. (2025). (2026, March 1). https://www.forbes.com/sites/avivalegatt/2025/06/16/ai-agents-are-set-to-transform-higher-education-heres-how/?clckid=948f8654

