Psychological aspects of AI acceptance:  a review of trust factors and specific  characteristics of Human - AI interaction

Kseniia Kokurina; Olga Kruzhkova

VAK Russia 5.3.9 UDC 159.9

2 more Collapse

PSYCHOLOGICAL ASPECTS OF AI ACCEPTANCE: A REVIEW OF TRUST FACTORS AND SPECIFIC CHARACTERISTICS OF HUMAN - AI INTERACTION Psychological aspects of AI acceptance: a review of trust factors and specific characteristics of Human - AI interaction

АА Аа

Published in Russian Journal of Deviant Behavior · Volume 6, Issue 3 · Pages 429–445 · Rubric: Psychological research on deviant behavior

Received: 25.06.2026 Accepted: 24.08.2026 Published: 25.10.2026

Authors

Kseniia Kokurina

ORCID

Olga Kruzhkova

ORCID

Kseniia Kokurina

Ural Federal University named after the first President of Russia B. N. Yeltsin Department of Psychology · Russian Federation · Ekaterinburg

ORCID

Olga Kruzhkova

Academic degree: candidate of psychological sciences

Science rang: docent

Ural Federal University named First President of Russia B. Yeltsin Associate Professor · Department of General and Social Psychology · Ekaterinburg

Ural State Pedagogical University Head of Laboratory · Laboratoriya perspektivnyh sociosredovyh issledovaniy · Russian Federation · Ekaterinburg

ORCID

ABSTRACT

The relevance of this study is determined by the contradiction between the rapid integration of artificial intelligence into everyday and professional life and insufficient development of psychological models which explain the acceptance or rejection of AI. Existing studies describe trust, cognitive biases and personality traits as predictors of AI acceptance only fragmentarily, without proposing an integrative approach. The problem lies in the lack of systematisation of the psychological determinants linking the technical-axiological, cognitive and personality-regulatory levels. The review aims to systematise research data on the psychological factors acting as predictors of the formation and manifestation of human attitude (including trust) towards artificial intelligence. The methodology comprises the analysis, generalisation and structuring of the results from theoretical and empirical studies published between 2020 and 2026 using the scientometric databases Web of Science, PubMed and eLibrary.ru. It has been found that trust in AI is a multicomponent dynamic construct including subjective, technical and axiological aspects; moreover, the axiological barriers (fairness, transparency and confidentiality) may outweigh the technical perfection of the system. It has been shown that the digital environment gives rise to the delegation of tasks to AI and cognitive offloading, which, with prolonged use of AI, may lead to a decline in critical thinking and long-term memory (a negative correlation in critical thinking); however, the verification of AI responses can mitigate these risks. The key conclusions are that attitude towards AI is formed at the intersection of three levels: technical-axiological, cognitive and personal-regulatory. Effective AI integration requires a shift from the contrasting models of “technology acceptance” and “technology rejection” towards a model of “deliberate delegation”, in which digital competence and the preservation of personal integrity play a central role. The findings may provide a basis for preventing technostress and AI dependance and for development of programmes to enhance metacognitive skills.

KEYWORDS

artificial intelligence, trust in AI, cognitive offloading, technologically extended personality, self-management of digital everyday life, technostress, critical thinking, delegation of authority, personal integrity, AI technology acceptance

Text

Text (PDF)

Read Download

1. Averkin, A. N., Voronina, N. F., & Pelipas, O. V. (2025). Partnerskie otnosheniia cheloveka i iskusstvennogo intellekta: mif ili real'nost' v sovremennom mire. Informatsionno-ekonomicheskie aspekty standartizatsii i tekhnicheskogo regulirovaniia, 2(83), 44–51.

2. Andrianova, R. A. (2024). ChatGPT (model' II): opyt vzaimodeistviia po analizu rezul'tatov i vyvodov issledovaniia problemy kiberagressii. Nauka, tekhnologiia i obshchestvo, 1, 5–32. https://doi.org/10.31249/scis/2024.01.01

3. Kokurina, K. E., Kruzhkova, O. V., Bogdanova, M. V. (2026). Gotovnost' cheloveka k vzaimodeistviiu s iskusstvennym intellektom. Vestnik Omskogo universiteta. Seriia «Psikhologiia», 3, 53–61. https://doi.org/10.24147/2410-6364.2026.3.53-61

4. Kruzhkova, O. V., Kolesnikova, K. A., Bogdanova, M. V. (2025). Otsenka keisov primeneniia iskusstvennogo intellekta v psikhologicheskom konsul'tirovanii. Pedagogicheskoe obrazovanie v Rossii, 6, 303–310.

5. Kumakhova, D. B. (2025). Vliianie iskusstvennogo intellekta na kognitivnye sposobnosti studentov. Nauka i Obrazovanie, 8(4), 1–8.

6. Leont'ev, D. A. (2025). Iskusstvennyi intellekt kak draiver tsifrovoi transformatsii strakhovoi otrasli na primere «Sber Strakhovaniia». Vestnik Altaiskoi akademii ekonomiki i prava, (3-2), 263–269. https://doi.org/10.17513/vaael.4054

7. Lyz', N. A., & Gladkaia, E. V. (2025). Vliianie tsifrovizatsii na kognitivnye protsessy: obzor prichin i posledstvii. Vestnik Permskogo universiteta. Filosofiia. Psikhologiia. Sotsiologiia, 3, 396–405. https://doi.org/10.17072/2078-7898/2025-3-396-405

8. Malafeevskaia, V. S. (2024). Sotsiologicheskoe issledovanie vozmozhnostei ispol'zovaniia iskusstvennogo intellekta studentami. Sovremennaia molodezh' i obshchestvo, 12, 35–40.

9. Soldatova, G. U. & Iliukhina, S. N. (2025). Chelovek tekhnologicheski dostroennyi: v poiskakh tselostnosti. Kul'turno-istoricheskaia psikhologiia, 21(1), 13–23. https://doi.org/10.17759/chp.2025210102

10. Soldatova, G. U., Chigar'kova, S. V., & Iliukhina, S. N. (2024a). Gotovnost' i opyt ispol'zovaniia tekhnologii kak pokazateli tekhnologicheski rasshirennoi lichnosti podrostkov i molodezhi. V Podrostok v megapolise: sbornik trudov XVII Mezhdunarodnoi nauchno-prakticheskoi konferentsii (9–10 aprelia 2024 g., Moskva) (s. 143–146). Natsional'nyi issledovatel'skii universitet «Vysshaia shkola ekonomiki».

11. Soldatova, G. U., Chigar'kova, S. V., & Iliukhina, S. N. (2024b). Tekhnologicheski rasshirennaia lichnost': razrabotka i aprobatsiia shkaly samoupravleniia tsifrovoi povsednevnost'iu. Vestnik Moskovskogo universiteta. Seriia 14. Psikhologiia, 47(2), 175–200. https://doi.org/10.11621/LPJ-24-20

12. Soldatova, G. U., Chigar'kova, S. V., & Iliukhina, S. N. (2025a). Rasshirennaia versiia Shkaly samoupravleniia tsifrovoi povsednevnost'iu (SUTsP-2): tselostnost' kak potentsial adaptatsii i blagopoluchiia. Kul'turno-istoricheskaia psikhologiia, 21(4), 25–37. https://doi.org/10.17759/chp.2025210403

13. Soldatova, G. U., Chigar'kova, S. V., & Iliukhina, S. N. (2025b). Tsifrovye prediktory psikhologicheskogo blagopoluchiia molodezhi v real'nom i virtual'nom mirakh. Vestnik Moskovskogo universiteta. Seriia 14. Psikhologiia, 48(1), 78–100. https://doi.org/10.11621/LPJ-25-04

14. Freimanis, I. F., & Shliapov, I. V. (2025). Riski primeneniia tekhnologii iskusstvennogo intellekta dlia psikhologicheskoi pomoshchi: vzgliad psikhologa i razrabotchika. Russian Journal of Education and Psychology, 16(5), 659–687. https://doi.org/10.12731/2658-4034-2025-16-5-829

15. Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: Progress, challenges, and future directions. Humanities and Social Sciences Communications, 11, Article 1568. https://doi.org/10.1057/s41599-024-04044-8

16. Borle, P., Reichel, K., Niebuhr, F., & Voelter-Mahlknecht, S. (2021). How are techno-stressors associated with mental health and work outcomes? A systematic review of occupational exposure to information and communication technologies within the technostress model. International Journal of Environmental Research and Public Health, 18, Article 8673. https://doi.org/10.3390/ijerph18168673

17. Bukhari, S. R., Batool, S., Shahab, I., Jalil, R., Arshad, A., Zahra, S. Q., Karim, S., Jamil, N., & Khan, N. (2025). Role of AI dependency in psychological distress and academic performance among university students. International Journal of Social Sciences Bulletin, 3(3), 286–294. https://ijssbulletin.com/index.php/IJSSB/article/view/495

18. Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391(6792), Article eaec8352. https://doi.org/10.1126/science.aec8352

19. De Freitas, J., Agarwal, S., Schmitt, B., & Haslam, N. (2023). Psychological factors underlying attitudes toward AI tools. Nature Human Behaviour, 7, 1845–1854. https://doi.org/10.1038/s41562-023-01734-2

20. Elbadiansyah, Sain, Z. H., Shehu Lawal, U., Chansa Thelma, C., & Luqman Aziz, A. (2024). Exploring the role of artificial intelligence in enhancing student motivation and cognitive development in higher education. TechComp Innovations, 1(2), 59–67. https://doi.org/10.70063/techcompinnovations.v1i2.47

21. Eryilmaz Baran, F. D., & Cetin, M. (2025). AI-driven early diagnosis of specific mental disorders: A comprehensive study. Cognitive Neurodynamics, 19, Article 70. https://doi.org/10.1007/s11571-025-10253-x

22. Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., Maes, P., Phang, J., Lampe, M., Ahmad, L., & Agarwal, S. (2025). How AI and human behaviors shape psychosocial effects of extended chatbot use: A longitudinal randomized controlled study [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2503.17473

23. Gasanova, I. A., Prelovskii, D. S., Yurkin, V. A., Drobintsev, P. D., & Drobintseva, A. O. (2020). Modern possibilities of using AI methods in the analysis of biomedical data. Computing, Telecommunications and Control, 13(4), 21–33. https://doi.org/10.18721/JCSTCS.13402

24. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), Article 6. https://doi.org/10.3390/soc15010006

25. Hatch, S. G., Goodman, Z. T., Vowels, L., Hatch, H. D., Brown, A. L., Guttman, S., Le, Y., Bailey, B., Bailey, R. J., Esplin, C. R., Harris, S. M., Holt, D. P., Jr., McLaughlin, M., O’Connell, P., Rothman, K., Ritchie, L., Top, D. N., Jr., & Braithwaite, S. R. (2025). Correction: When ELIZA meets therapists: A Turing test for the heart and mind. PLOS Mental Health, 2(8), Article e0000426. https://doi.org/10.1371/journal.pmen.0000426.

26. Jones, V. K., Yan, C., Shade, M. Y., Boron, J. B., Yan, Z., Heselton, H. J., Johnson, K., & Dube, V. (2024). Reducing loneliness and improving social support among older adults through different modalities of personal voice assistants. Geriatrics, 9(2), Article 22. https://doi.org/10.3390/geriatrics9020022

27. Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., & Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in Psychology, 16, Article 1550621. https://doi.org/10.3389/fpsyg.2025.1550621

28. Kleinert, T., Waldschütz, M., Blau, J., Heinrichs, M., & Schiller, B. (2026). AI outperforms humans in establishing interpersonal closeness in emotionally engaging interactions, but only when labelled as human. Communications Psychology, 4, Article 23. https://doi.org/10.1038/s44271-025-00391-7

29. Lahusen, C., Maggetti, M., & Slavkovik, M. (2024). Trust, trustworthiness and AI governance. Scientific Reports, 14, Article 20752. https://doi.org/10.1038/s41598-024-71761-0

30. McClain, C., Anderson, M., Sidoti, O., & Bishop, W. (2026, February 24). How teens use and view AI. Pew Research Center. https://www.pewresearch.org/internet/2026-02-24/how-teens-use-and-view-ai/

31. Perlis, R. H., Gunning, F. M., Uslu, A. A., Santillana, M., Baum, M. A., Druckman, J. N., Ognyanova, K., & Lazer, D. (2026). Generative AI use and depressive symptoms among US adults. JAMA Network Open, 9(1), Article e2554820. https://doi.org/10.1001/jamanetworkopen.2025.54820

32. Yankouskaya, A., Almourad, M. B., Liebherr, M., Beyahi, F., Xu, G., & Ali, R. (2026). Who lets AI take over? Cross-national variation in willingness to delegate socially important roles to artificial intelligence. AI & Society, 41, 6151–6169. https://doi.org/10.1007/s00146-026-02858-5

33. Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7

34. Zhang, S., Guo, P., Yuan, Y., & Ji, Y. (2025). Anxiety or engaged? Research on the impact of technostress on employees' innovative behavior in the era of artificial intelligence. Acta Psychologica, 259, Article 105442. https://doi.org/10.1016/j.actpsy.2025.105442

35. Zhang, W., Yang, Y., Liu, M., Wang, L., Lei, Q., Zhang, Q., Wang, J., Li, H., & Wuri, G. (2025). The effect of artificial intelligence-empowered mobile health on psychological distress in women following abortion: Protocol for a mixed-methods study. Frontiers in Psychiatry, 16, Article 1665500. https://doi.org/10.3389/fpsyt.2025.1665500