Autonomous AI assistants as a means of information support for student learning activities

The article presents a theoretical analysis of the educational potential of autonomous AI assistants — systems based on large language models (LLMs) capable of independently searching, aggregating, and synthesizing information from open Internet sources upon user request. Unlike traditional search engines, such assistants (Perplexity AI, Microsoft Copilot) do not merely index pages but generate structured responses with source references, thereby transforming the nature of student interaction with the information environment.

A functional architecture of the autonomous AI assistant is proposed, considered in the context of student information search activities (query decomposition, source aggregation and attribution, clarifying dialogue). A comparative analysis of three classes of information support systems — traditional search engines, recommendation platforms, and autonomous AI assistants — is conducted according to criteria of system autonomy, level of cognitive support, and feedback characteristics. Pedagogical conditions for productive integration of autonomous AI assistants into the educational process are substantiated: a three-stage integration model (propaedeutic, instrumental, analytical) is formulated, key risks (uncritical acceptance of results, substitution of cognition with generation) and conditions for their minimization are identified. It is shown that the effectiveness of an AI assistant as a means of information support is determined not so much by the technical characteristics of the system as by the student’s information literacy level and critical evaluation skills, as well as by the presence of systematic pedagogical support.

1. Sysoyev P. V. Artificial intelligence in education: Awareness, readiness, and practice of using artificial intelligence technologies in professional activities by university faculty. Higher Education in Russia. 2023;32(10):9–33. (In Rus-sian.) DOI: 10.31992/0869-3617-2023-32-10-9-33. EDN: TZYTKM.

2. Korol A. D., Bushmanova E. A. Artificial intelligence in the mirror of education: The problem of dialogue. Higher Education in Russia. 2025;34(2):125–135. (In Russian.) DOI: 10.31992/0869-3617-2025-34-2-125-135. EDN: PBTQXG.

3. Endovitskiy D. A., Gaidar K. M. University science and education in the context of artificial intelligence. Higher Education in Russia. 2021;30(6):121–131. (In Russian.) DOI: 10.31992/0869-3617-2021-30-6-121-131. EDN: NJJXGQ.

4. Zakharova I. G., Vorobeva M. S., Boganyuk Yu. V. Supporting individual educational trajectories based on the concept of explainable artificial intelligence. The Education and Science Journal. 2022;24(1):163–190. (In Russian.) DOI: 10.17853/1994-5639-2022-1-163-190. EDN: IOBYEJ.

5. Elshansky S. P. School of the future: Can artificial intelligence provide cognitive learning efficiency? Tomsk State University Journal. 2021;(462):192–201. (In Russian.) DOI: 10.17223/15617793/462/23. EDN: YILXYS.

6. Bukina T. V., Khramova M. V., Kurkin S. A., Andrikov D. A., Goman S. S., Dedkov A. E., Hramov A. E. Neuroeducational software recommendational service as a tool for personalizing the educational process. Informatics and Edu-cation. 2024;39(5):50–62. (In Russian.) DOI: 10.32517/0234-0453-2024-39-5-50-62. EDN: NJWJOY.

7. Sysoyev P. V. Personalized learning based on artificial intelligence technologies: How ready are modern students for new educational opportunities. Higher Education in Russia. 2025;34(2):51–71. (In Russian.) DOI: 10.31992/0869-3617-2025-34-2-51-71. EDN: WEAGVQ.

8. Gnedykh D. S. Trends and prospects of using braincomputer interfaces in education. Sibirskiy Psikhologicheskiy Zhurnal. 2021;(79):108–129. (In Russian.) DOI: 10.17223/17267080/79/7. EDN: IJVQFH.

9. Shchurina S. V. The impact of the digital revolution on the development of artificial intelligence and the introduction of robot process automation into the activities of a modern company. Economics, Taxes & Law. 2023;16(4):65–75. (In Russian.) DOI: 10.26794/1999-849X-2023-16-4-65-75. EDN: YANAKO.

10. Belolobova A. A. Artificial intelligence in education: A problem of its unfair use by students. Engineering Education. 2025;(37):86–98. (In Russian.) DOI: 10.54835/18102883_2025_37_8. EDN: QWCINF.

11. Valkova Ju. E. Using artificial intelligence technologies to prepare and compose scientific articles. Informatics and Education. 2024;39(6):38–52. (In Russian.) DOI: 10.32517/0234-0453-2024-39-6-38-52. EDN: OFVOAD.

12. Levin B. A., Piskunov A. A., Poliakov V. Yu., Savin A. V. Artificial intelligence in engineering education. Higher Education in Russia. 2022;31(7):79–95. (In Russian.) DOI: 10.31992/0869-3617-2022-31-7-79-95. EDN: KCNAPJ.

13. Nosova L. S., Belousova N. A., Korchemkina Yu. V. Development of an applied intelligent system based on neurophysiological data to support decision-making of the organization of the educational process. Informatics and Education. 2023;38(2):16–25. (In Russian.) DOI: 10.32517/0234-0453-2023-38-2-16-25. EDN: JJPHDY.

14. Buyakova K. I., Dmitriev Ya. A., Ivanova A. S., Feshchenko A. V., Yakovleva K. I. Students’ and teachers’ attitudes towards the use of tools with generative artificial intelligence at the university. The Education and Science Journal. 2024;26(7):160–193. (In Russian.) DOI: 10.17853/1994-5639-2024-7-160-193. EDN: BSEUWY.

15. Kukuev E. A., Gladkova L. N., Krezhevskikh O. V., Frolenkova A. L. Students’ research competencies in the era of artificial intelligence. Perspectives of Science and Education. 2025;(3(75)):55–71. (In Russian.) DOI: 10.32744/pse.2025.3.4. EDN: XIWZHA.

16. Sysoyev P. V., Evstigneev M. N. The use of artificial intelligence technologies in students’ research work. Moscow University Bulletin. Series 19: Linguistics and Intercultural Communication. 2025;28(1):85–101. (In Russian.) DOI: 10.55959/MSU-2074-1588-19-28-1-6. EDN: AYNWSU.

17. Kapranova L. D. Digital transformation of society and the standard of living of the population. Economics, Taxes & Law. 2025;18(3):81–91. (In Russian.) DOI: 10.26794/1999-849X-2025-18-3-81-91. EDN: GDXTGU.

18. Vishnekov A. V., Erokhina E. A., Ivanova E. M., Trubochkina N. K. Features of the educational process for training IT-specialists in the conditions of increasing capabilities of generative AI. Engineering Education. 2023;(34):123–135. (In Russian.) DOI: 10.54835/18102883_2023_34_11. EDN: ZNKWCN.

19. Karakozov S. D., Samokhvalova E. A., Ryzhova N. I. Implementation of artificial intelligence essentials in the secondary general education informatics course: The practice-oriented aspect. Informatics and Education. 2024;39(5):6–20. (In Russian.) DOI: 10.32517/0234-0453-2024-39-5-6-20. EDN: EJGPIY.

20. Zakharov A. A., Zakharova I. G., Shabalin A. M., Khanbekov Sh. I., Dzhalilzoda D. B. Intelligent voice assistant as an example of inclusive design methodology implementation. The Education and Science Journal. 2024;26(3):149–175. (In Russian.) DOI: 10.17853/1994-5639-2024-3-149-175. EDN: KZYPDW.

21. Maximova M. V., Frolova O. V., Etuev Kh. Kh., Aleksandrova L. D. Adaptive personalized learning: Implementation of emerging technologies in higher education. Informatics and Education. 2023;38(4):14–27. (In Russian.) DOI: 10.32517/0234-0453-2023-38-4-14-27. EDN: ZXOKFV.