PSYCHOLOGICAL IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON THE EVERYDAY LIFE OF INDIVIDUALS

Abstract

The rapid development of generative artificial intelligence (GenAI) has become one of the most significant technological transformations of the twenty-first century, fundamentally changing the ways people search for, process, generate, and use information in educational, professional, and everyday contexts. Unlike traditional digital technologies, large language models not only provide access to information but also generate, analyze, interpret, and adapt it to users’ needs, thereby becoming an active component of human cognitive activity. As a result, generative AI is increasingly regarded not merely as a technological tool but as a new psychological factor of the digital environment that influences cognitive functioning, emotional experiences, behavioral strategies, and social interaction.

The article systematizes contemporary theoretical and empirical findings on the psychological consequences of generative AI use and integrates fragmented research into a unified conceptual framework. The analysis demonstrates that generative AI simultaneously creates substantial opportunities and psychological risks. Positive effects include cognitive support, improved information processing, personalized learning, enhanced creativity, reduced emotional tension, increased productivity, expanded communication opportunities, and broader access to knowledge. At the same time, excessive reliance on AI may contribute to cognitive offloading, weakened critical thinking, automation bias, emotional attachment to digital systems, reduced behavioral autonomy, changes in decision-making strategies, transformation of interpersonal communication, and increased dependence on algorithmic recommendations.

Based on the synthesis of recent psychological research, the paper proposes a four-level classification of the psychological consequences of generative AI use, encompassing cognitive, emotional, behavioral, and socio-psychological dimensions. The proposed model emphasizes the interdependence of these dimensions and demonstrates that changes within one psychological domain inevitably influence the others. This integrative framework provides a theoretical basis for future empirical investigations, the development of psychological assessment instruments, and evidence-based recommendations for the safe, critical, and responsible use of generative artificial intelligence.

References 

1.                        Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7, 52-62. https://doi.org/10.61969/jai.1337500

2.                        Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences of the United States of America, 120(6), e2218523120. https://doi.org/10.1073/pnas.2218523120

3.                        Chen, L., Zaharia, M., & Zou, J. (2024). How Is ChatGPT’s Behavior Changing Over Time? Harvard Data Science Review, 6(2). https://doi.org/10.1162/99608f92.5317da47

4.                        Clark, A. and Chalmers, D. (1998) The Extended Mind. Analysis, 58, 7-19. https://doi.org/10.1093/analys/58.1.7

5.                        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

6.                        Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M. (2023). ChatGPT outperforms humans in emotional awareness evaluations. Frontiers in psychology, 14, 1199058. https://doi.org/10.3389/fpsyg.2023.1199058

7.                        Floridi, L. and Chiriatti, M. (2020) GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30, 681-694. https://doi.org/10.1007/s11023-020-09548-1

8.                        Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science (New York, N.Y.), 381(6654), 187-192. https://doi.org/10.1126/science.adh2586

9.                        Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: an attentional integration. Human factors, 52(3), 381-410. https://doi.org/10.1177/0018720810376055

10.                    Perlis R. H. (2026). Artificial Intelligence and the Potential Transformation of Mental Health. JAMA psychiatry, 83(4), 409-413. https://doi.org/10.1001/jamapsychiatry.2025.4116

11.                    Sharma, A., Lin, I.W., Miner, A.S., Atkins, D.C., & Althoff, T. (2022). Human-AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5, 46-57. https://doi.org/10.1038/s42256-022-00593-2

12.                    Skjuve, M., Brandtzaeg, P. B., & Følstad, A. (2022). My AI Friend: How Users of a Social Chatbot Understand Their Human-ai Friendship. Human Communication Research, 48, 404-429. https://doi.org/10.1093/hcr/hqac008

13.                    Storm, B. C., & Stone, S. M. (2015). Saving-enhanced memory: the benefits of saving on the learning and remembering of new information. Psychological science, 26(2), 182-188. https://doi.org/10.1177/0956797614559285