Modern approaches to studying human interaction with generative artificial intelligence

Context and relevance. The intensive development and use of generative AI tools has necessitated the analysis of the psychological aspects of human-AI interaction. Objective. Identification of the main approaches to studying the peculiarities of human interaction with generative AI in contemporary international psychology. Methods and materials. An analysis of articles published from 2023 to 2025 on this topic in academic journals and conference proceedings indexed in major databases Results. Five key approaches have been identified, each emphasizing specific psychological factors and consequences of human interaction with generative AI. The structural-functional approach enables the identification of potential structural roles and functions delegated by the individual to generative AI during interaction. The informational approach focuses on the specific features (properties) of generated information that determine its possibilities and limitations in human intellectual activity. The cognitive approach examines the impact of interaction with generative AI on the individual’s cognitive processes and measuring LLM cognitive abilities. The personality-based approach describes the role of personality traits and their transformation during the dialogue with generative artificial intelligence. The institutional approach highlights legal, social, and cultural norms that ensure the safe use of generative AI. Conclusions. Within each approach, evidence is presented demonstrating both positive and negative effects of generative AI on users’ mental states. The diversity of approaches in the psychological research discourse allows for outlining future research directions or studying psychological factors and consequences that characterize safe and effective human interaction with modern digital systems, including monitoring the long-term effects of using generative AI technologies.

1. Ahern, S.J. (2025). What is lazy metacognition and what can we do about it? Journal of Learning Development in Higher Education, 37, Article 1713. https://doi.org/10.47408/jldhe.vi37.1713
2. Ahmad, O., Maliha, H., Ahmed, I. (2024). AI Syndrome: an intellectual asset for students or a progressive cognitive decline. Asian Journal of Psychiatry, 94, Article 103969. https://doi.org/10.1016/j.ajp.2024.103969
3. Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-covid ERA and its impact on faculty members’ occupational well-being and teaching self efficacy: A path analysis using the UTAUT 2 model. Applied Artificial Intelligence, 37(1), Article 2175110. https://doi.org/10.1080/08839514.2023.2175110
4. Alkaissi, H., McFarlane S.I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), Article e35179. https://doi.org/10.7759/cureus.35179
5. Anđić, B. (2025). Artificial intelligence and deepfake learning in higher education. Journal of Baltic Science Education, 24(1), 4—6. https://doi.org/10.33225/jbse/25.24.04
6. Bambini, V., Lecce, S. (2025). At the heart of human communication: New views on the complex relationship between pragmatics and Theory of Mind. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 380(1932), Article 20230486. https://doi.org/10.1098/rstb.2023.0486
7. Crawford, J., Allen, K.-A., Pani, B., Cowling, M. (2024). When artificial intelligence substitutes humans in higher education: The cost of loneliness, student success, and retention. Studies in Higher Education, 49(5), 883—897. https://doi.org/10.1080/03075079.2024.2326956
8. Dam, S.K., Hong, C.S., Qiao, Y., Zhang, C. (2024). A complete survey on LLM-based AI chatbots. arXiv, Preprint arXiv:2406.16937. https://doi.org/10.48550/arXiv.2406.16937
9. Day, T. (2023). A Preliminary Investigation of Fake Peer-Reviewed Citations and References Generated by ChatGPT. The Professional Geographer, 75(6), 1024—1027. https://doi.org/10.1080/00330124.2023.2190373
10. Deng, Z., Deng, Z. (2025). Becoming a cognitive miser? Antecedents and consequences of addictive ChatGPT use. Social Science & Medicine, 383, Article 118467. https://doi.org/10.1016/j.socscimed.2025.118467
11. Dillion, D., Tandon, N., Gu, Y., Gray, K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences, 27(7), 597—600. https://doi.org/10.1016/j.tics.2023.04.008
12. Duong, C.D., Ngo, T.V.N., Khuc, T.A., Tran, N.M., Nguyen, T.P.T. (2025). Unraveling the dark side of ChatGPT: A moderated mediation model of technology anxiety and technostress. Information Technology & People, 38(4), 2015—2040. https://doi.org/10.1108/ITP-11-2023-1151
13. Enam, M.D.A., Murmu, C., Dixon, E. (2025). «Artificial intelligence-carrying us into the future»: A study of older adults’ perceptions of LLM-based chatbots. International Journal of Human-Computer Interaction, 41(21), 13747—13770. https://doi.org/10.1080/10447318.2025.2476710
14. Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489—530. https://doi.org/10.1111/bjet.13544
15. Frenkenberg, A., Hochman, G. (2025). It’s scary to use it, it’s scary to refuse it: The psychological dimensions of AI adoption — anxiety, motives, and dependency. Systems, 13(2), Article 82. https://doi.org/10.3390/systems13020082
16. Garcia, M.B. (2024). Addressing the mental health implications of ChatGPT dependency: The need for comprehensive policy development. Asian Journal of Psychiatry, 98, Article 104140. https://doi.org/10.1016/j.ajp.2024.104140
17. Gerlich, M. (2023). The power of virtual influencers: Impact on consumer behaviour and attitudes in the age of AI. Administrative Sciences, 13(8), Article 178. https://doi.org/10.3390/admsci13080178
18. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(9), Article 252. https://doi.org/10.3390/soc15010006
19. Girma, A.H. (2025). The role of artificial intelligence in shaping human interaction and cognitive function. Kotebe Journal of Education, 3(1), 69—88. https://doi.org/10.61489/30053447.3(1).69
20. Goldstein, J.A., Sastry, G., Musser, M., DiResta, R., Gentzel, M., Sedova, K. (2023). Generative language models and automated influence operations: Emerging threats and potential mitigations. arXiv, Preprint arXiv:2301.04246. https://doi.org/10.48550/arXiv.2301.04246
21. Grossmann, I., Feinberg, M., Parker, D.C., Christakis, N.A., Tetlock, P.E., Cunningham, W.A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108—1109. https://doi.org/10.1126/science.adi1778
22. Hsieh, I.-T., Oh, C.-H. (2024). Users' attachment styles and ChatGPT interaction: Revealing insights into user experiences. Journal of The Korea Society of Computer and Information, 29(3), 21—41. https://doi.org/10.9708/jksci.2024.29.03.021
23. Jin, Y., Yan, L., Echeverria, V., Gašević, D., Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, Article 100348. https://doi.org/10.1016/j.caeai.2024.100348
24. Kalam, K.T., Rahman, J.M., Islam, M.R., Dewan, S.M.R. (2024). ChatGPT and mental health: Friends or foes? Health Science Reports, 7(2), Article e1912. https://doi.org/10.1002/hsr2.1912
25. Kim, T.W. (2023a). Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: A narrative review. Journal of Educational Evaluation for Health Professions, 20, Article 38. https://doi.org/10.3352/jeehp.2023.20.38
26. Kim, L. (2023b). The echo chamber-driven polarization on social media. Journal of Student Research, 12(4). https://doi.org/10.47611/jsr.v12i4.2274
27. Kirk, H.R., Gabriel, I., Summerfield, C., Vidgen, B., Hale, S.A. (2025). Why human–AI relationships need socioaffective alignment. Humanities and Social Sciences Communications, 12(1), Article 728. https://doi.org/10.1057/s41599-025-04532-5
28. Kolding, S., Lundin, R.M., Hansen, L., Østergaard, S.D. (2024). Use of generative artificial intelligence (AI) in psychiatry and mental health care: A systematic review. Acta Neuropsychiatrica, 37, Article e37. https://doi.org/10.1017/neu.2024.50
29. Kosmyna, N., Hauptmann, E., Yuan, Y.T., Situ, J., Liao, X.H., Beresnitzky, A.V., Braunstein, I., Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv, Preprint arXiv:2506.08872. https://doi.org/10.48550/arXiv.2506.08872
30. Lademann, J., Henze, J., Becker-Genschow, S. (2025). Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables. Physical Review Physics Education Research, 21(1), Article 010147. https://doi.org/10.1103/PhysRevPhysEducRes.21.010147
31. Lu, T., Zhang, Y. (2025). 1+1>2? Information, humans, and machines. Information Systems Research, 36(1), 394—418. https://doi.org/10.1287/isre.2023.0305
32. Ma, D., Akram, H., Chen, I.-H. (2024). Artificial intelligence in higher education: A cross-cultural examination of students’ behavioral intentions and attitudes. International Review of Research in Open and Distributed Learning, 25(3), 134—157. https://doi.org/10.19173/irrodl.v25i3.7703
33. Mannuru, N.R., Shahriar, S., Teel, Z.A., Wang, T., Lund, B.D., Tijani, S., Pohboon, C.O., Agbaji, D., Alhassan, J., Galley, J., Kousari, R., Ogbadu-Oladapo, L., Saurav, S.K., Srivastava, A., Tummuru, S.P., Uppala, S., Vaidya, P. (2025). Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development. Information development, 41(3), 1036—1054. https://doi.org/10.1177/02666669231200628
34. Marchetti, A., Manzi, F., Riva, G., Gaggioli, A., Massaro, D. (2025). Artificial intelligence and the illusion of understanding: A systematic review of theory of mind and large language models. Cyberpsychology, Behavior and Social Networking, 28(7), 505—514. https://doi.org/10.1089/cyber.2024.0536
35. McDiarmid, G.W., Zhao, Y. (2023). Time to rethink: Educating for a technology-transformed world. ECNU Review of Education, 6(2), 189—214. https://doi.org/10.1177/20965311221076493
36. Messeri, L., Crockett, M.J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627(8002), 49—58. https://doi.org/10.1038/s41586-024-07146-0
37. Metzler, H., Garcia, D. (2024). Social drivers and algorithmic mechanisms on digital media. Perspectives on Psychological Science, 19(5), 735—748. https://doi.org/10.1177/17456916231185057
38. Monteith, S., Glenn, T., Geddes, J.R., Whybrow, P.C., Achtyes, E., Bauer, M. (2024). Artificial intelligence and increasing misinformation. The British Journal of Psychiatry, 224(2), 33—35. https://doi.org/10.1192/bjp.2023.136
39. Noy, S., Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187—192. https://doi.org/10.1126/science.adh2586
40. Østergaard, S.D., Nielbo, K.L. (2023). False responses from artificial intelligence models are not hallucinations. Schizophrenia bulletin, 49(5), 1105—1107. https://doi.org/10.1093/schbul/sbad068
41. Pergantis, P., Bamicha, V., Skianis, C., Drigas, A. (2025). AI chatbots and cognitive control: Enhancing executive functions through chatbot interactions: A systematic review. Brain Sciences, 15(1), Article 47. https://doi.org/10.3390/brainsci15010047
42. Reddy, S. (2024). Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implementation Science, 19(1), Article 27. https://doi.org/10.1186/s13012-024-01357-9
43. Rodilosso, E. (2024). Filter bubbles and the unfeeling: How AI for social media can foster extremism and polarization. Philosophy & Technology, 37(2), Article 71. https://doi.org/10.1007/s13347-024-00758-4
44. Sheh, C. (2024). Unlocking educational transformation: Entrepreneurs and the power of AI. Journal of Advanced Management Science, 12(2), 52—56. https://doi.org/10.18178/joams.12.2.52-56
45. Tuong, N.K., Dan, T.C. (2024). A study on Duolingo mobile applications to improve EFL students' listening comprehension performances. European Journal of Alternative Education Studies, 9(1), 217—265. https://doi.org/10.46827/ejae.v9i1.5342
46. Wach, K., Duong, C.D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7—30. https://doi.org/10.15678/EBER.2023.110201
47. Wang, E., Smith, J., Katz, S., Bishay, M., Dissanayake, T., Jones, N., Reddy, S., Sholter, D., Soo, J., Ye, C. (2025). Adoption and perception of LLM-based chatbots in health care: An exploratory cross-sectional survey of individuals with rheumatic diseases. Rheumatology Advances in Practice, 9(3), Article rkaf083. https://doi.org/10.1093/rap/rkaf083
48. William, F.K.A., Misheal, M.R. (2024). Exploring graduate students' perception and adoption of AI chatbots in Zimbabwe: Balancing pedagogical innovation and development of higher-order cognitive skills. Journal of Applied Learning & Teaching, 7(1), 65—75. https://doi.org/10.37074/jalt.2024.7.1.12
49. Xie, T., Pentina, I., Hancock, T. (2023). Friend, mentor, lover: Does chatbot engagement lead to psychological dependence? Journal of Service Management, 34(4), 806—828. https://doi.org/10.1108/JOSM-02-2022-0072
50. Yoshizawa, S., Onzo, A., Nozawa, S., Takano, T., Mogi, K. (2024). Metacognition in large language models. Proceedings of the Annual Conference of JSAI, 38, Article 4Xin232. https://doi.org/10.11517/pjsai.JSAI2024.0_4Xin232
51. Yu, S.-C., Chen, H.-R., Yang, Y.-W. (2024). Development and validation the problematic ChatGPT use scale: A preliminary report. Current Psychology, 43(31), 26080—26092. https://doi.org/10.1007/s12144-024-06259-z
52. 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