On the application of information and information processes psychosocial theory in the artificial intelligence systems development and use

The article describes a study of theoretical approaches to the creation and development of artificial intelligence systems from the perspective of the emerging psychosocial theory of information and information processes. Current theoretical and methodological aspects and directions for the development and use of such systems are examined, a few conditions that should be considered during their development are formulated, and the specifics of ensuring information security and mitigating emerging risks are discussed, as well as the use of artificial intelligence technologies in education.

The objective of this study is to identify areas and substantiate the feasibility of using the psychosocial theory of information and information processes to address the challenges of studying and creating information systems that implement or imitate certain functions of natural intelligence.

The article examines issues related to the construction and development of artificial intelligence systems solely within the framework of computer modelling of human cognitive (rational, conscious) capabilities.

Several key conclusions have been drawn. When developing artificial intelligence systems, it is methodologically correct to utilize the accumulated knowledge about the genesis of natural intelligence. Possible scenarios for the development of artificial intelligence systems, which may arise as a result of their development into self-learning and self-development capabilities, should be analysed. Requirements for the use of artificial intelligence systems must be detailed to ensure their effective and safe application in human activities.

1. Grinshkun V. V., Podufalov N. D. Information, information processes and technologies: a psychosocial approach to theory development. Methodology of pedagogical research: Modern challenges. Collective monograph. E-publication. Moscow, Publishing house of RAE; 2026. 424 p. (In Russian.) (In print.)

2. Lazarev V. S. On the psychological and pedagogical foundations for designing the digital transformation of general education. Pedagogy. 2022;86(3):5–16. (In Russian.) EDN: WXNCKS.

3. Harrison O. E., Kandekere R. C., Fasanmade A. A. The future of software development: Integrating AI and machine learning into front-end technologies. Global Journal of Advanced Research and Reviews. 2024;2(1):69–77. DOI: 10.58175/gjarr.2024.2.1.0031. EDN: LHILUT.

4. Russel S., Norvig P. Artificial intelligence. A modern approach. Moscow, Williams; 2006. 1408 p. (In Russian.) EDN: QMPEYN.

5. Dushkin R. V. Review of approaches and methods of artificial intelligence. Radioelectronic technology. 2018;(3):85– 89. (In Russian.)

6. Dushkin R. V., Andronov M. G. Hybrid design of artificial intelligent systems. Cybernetics and programming. 2019;(4):51–58. (In Russian.) DOI: 10.25136/2644-5522.2019.4.29809. EDN: OKAMBF.

7. Ilyenkov E. V. Philosophy and culture. Moscow, Politizdat; 1991. 464 p. (In Russian.)

8. Yasnitsky L. N. The future of artificial intelligence. Forecast of Perm scientists. Artificial Intelligence in Solving Current Social and Economic Problems of the 21st Century. Proc. Ninth All-Russ. Scientific and Practical Conf. with Int. Participation. Perm, Perm State National Research University; 2024:10–16. (In Russian.) EDN: NTQLUB.

9. Anokhin K. V. Cognitome: In search of fundamental neuroscientific theory of consciousness. I.P. Pavlov Journal of Higher Nervous Activity. 2021;71(1):39–71. (In Russian.) DOI: 10.31857/S0044467721010032. EDN: TTTGKL.

10. Rodrigues C. A. D., Mendes A. D., Wanderley A. A., Cardoso G. D., Fernandes M. D. F., Barbosa T. O., De Mu-niz T. C., Demuner J. A. Skills development for the 21st century: The influence of AI and technologies on the educational curriculum. ARACfi. 2024;6(3):5784–5799. DOI: 10.56238/arev6n3-090. EDN: YLCAAU.

11. Bran E., Rughinsiş R., Ţurcanu D., Radovici A. AI leads, cybersecurity follows: Unveiling research priorities in sustainable development goal-relevant technologies across nations. Sustainability. 2024;16(20):8886. DOI: 10.3390/su16208886. EDN: ZZBNQW.

12. Rabchevskiy A. N., Yasnitsky L. N. Creating and using synthetic data for neural network training, using the creation of a neural network classifier of online social network user roles as an example. In: Antipova T. (ed) Digital Science. DSIC 2021. Lecture Notes in Networks and Systems. Cham, Springer; 2022;381:412–421. DOI: 10.1007/978-3-030-93677-8_36. EDN: XKXAMM.

13. Grigoriev S. G., Anikieva M. A. Generative artificial intelligence application enhancement in educational activities. Informatics and Education. 2024;39(3):5–15. (In Russian.) DOI: 10.32517/0234-0453-2024-39-3-5-15. EDN: MKCCKW.

14. Grigoriev S. G. Artificial intelligence in education. Problems of Training Teachers of Mathematics, Informatics, and Natural Sciences. Collection of Articles by Participants of the Int. Scientific and Methodological Conf. Nizhny Novgorod, Lobachevsky State University of Nizhny Novgorod; 2024:16–19. (In Russian.) EDN: JYBDUK.

15. Podufalov N. D. Didactic, legal and moral and ethical aspects of the formation and regulation of the electronic (digital) information educational environment. Innovative Processes in Higher and Secondary Vocational Education and Professional Self-determination. Collection of Scientific Papers. Moscow, Author’s Club; 2025:313–334. (In Russian.) EDN: ILWQCH.

16. Gerova N. V., Karelina M. V., Kastornova V. A. et. al. Artificial intelligence in education. Moscow, ISMO; 2024. 306 p. (In Russian.)

17. Robert I. V. Artificial intelligence in education: — an object of study; — a means of improving the effectiveness of learning. In: Gevorkyan E. N., Podufalov N. D., Strikhanov M. N. Innovative Processes in Higher and Secondary Vocational Education and Professional Self-determination. Moscow, Ekon-Inform; 2023:502–514. (In Russian.) EDN: OZRWGN.

18. Robert I. V. Artificial intelligence in education: Imple-mentation directions. Modern Education in a Multicultural World: Development Trends and Prospects. Proc. Int. Scientific and Practical Conf. Makhachkala, ALEF; 2024:25–36. (In Russian.) DOI: 10.33580/9785002124411_25. EDN: OCVCQB.

19. Kuzminov Ya. I., Kiryushina M. A., Vorochkov A. P., Kruchinskaya E. V., Terentyev E. A., Froumin I. D. The beginning of the end or a new era? The effects of generative artificial intelligence in higher education. Moscow, HSE; 2024. 64 p. (In Russian.)

20. Onalbek Z. K., Grinshkun V. V., Omarov B. S., Abuseytov B. Z., Makhanbet E. T., Kendzhaeva B. B. The main systems and types of forming of future teacher-trainers’ professional competence. Life Science Journal. 2013;10(4):2397–2400. EDN: QPGKTG.

21. Grigoriev S. G., Lerner I. M., Marinosyan A. Kh. Mentoring in the age of artificial intelligence. Modern pedagogy: The relationship between traditions and innovations. Moscow, MAX Press; 2024:213–222. (In Russian.) EDN: DPGADH.

22. Chibisov O. N. Gnoseology of machine learning for artificial general intelligence. Science. Art. Culture. 2024;(2(42)):143–157. (In Russian.) EDN: YXKOHW.