Melnyk Y. B. 1,2, Pypenko I. S. 1,2
| 1 Kharkiv Regional Public Organization “Culture of Health”, Ukraine 2 Scientific Research Institute KRPOCH, Ukraine |
Abstract
Background and Aim of Study: The current stage of artificial intelligence (AI) development is characterised by the unpredictability and emergent nature of models, the hidden capabilities of which are not always recognised by developers. The widespread integration of digital products, coupled with the emergent nature of this process, inevitably lead to the creation of general artificial intelligence (AGI). In this context, identifying mechanisms that enable society to interact safely with the latest technological systems is becoming an increasingly important task.
The aim of the study: to identify key trends in the development of artificial intelligence in contemporary society and to predict its impact on the transformation of the educational environment as a dominant factor in the digital adaptation of the younger generation.
Material and Methods: The study is grounded in a systems approach, employing theoretical methods of analysis, synthesis, induction, deduction, abstraction, comparison, systematization, and data interpretation.
Results: The present study provided a theoretical basis for the principle of responsibility for implementing and using artificial intelligence. The problem of anthropomorphism and human attachment to technologies was identified. It has been confirmed that the key risk does not stem from the use of AI itself, but rather from the environment in which it operates. It has also been established that the younger generation is the most adaptable demographic group in the process of acculturation to AI. The study identified four main vectors of AI’s influence on the transformation of the educational environment: 1) a paradigm shift: from digitalisation to an “AI-centred educational ecosystem”; 2) a radical change in the roles of stakeholders in education; 3) forming of new ethical and legal literacy among young people; 4) psychological adaptation and cognitive symbiosis.
Conclusions: Long-term forecasting confirms that institutional transformation in education and the transition to AI-centred ecosystems are inevitable. Transforming the teacher into a mentor and facilitator will equip students with adaptive autonomy and change the nature of the subject-subject relationship. The education sector plays a crucial role in preparing society to use AI responsibly for the benefit of humanity.
Keywords
artificial intelligence, artificial general intelligence, large language models, Human-AI system, principle of responsibility, problem of anthropomorphism and attachment, transformation of the educational environment.
References
Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
AIC AI Chatbots. (2023). AIC AI Chatbots attribution. https://doi.org/10.26697/ai.chatbots
Bellas, F., Naya-Varela, M., Mallo, A., & Paz-Lopez, A. (2024). Education in the AI era: A long-term classroom technology based on intelligent robotics. Humanities and Social Sciences Communications, 11, Article 1425. https://doi.org/10.1057/s41599-024-03953-y
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Brenneis, A. (2025). Assessing dual use risks in AI research: Necessity, challenges and mitigation strategies. Research Ethics, 21(2), 302–330. https://doi.org/10.1177/17470161241267782
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR, 81, 77–91. https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
Chess. (2017). How is Alpha Zero “more human”? https://chess.stackexchange.com/questions/19360/how-is-alpha-zero-more-human
Dijk, J. (2020). The digital divide. Polity. https://www.wiley.com/en-be/shop/cultural-studies-general/the-digital-divide-p-9781509534463
European Commission. (2025, December 17). First draft code of practice on transparency of AI-generated content. https://digital-strategy.ec.europa.eu/en/library/first-draft-code-practice-transparency-ai-generated-content
European Commission. (2025, February 04). Guidelines on prohibited artificial intelligence practices, as defined by the AI Act. https://digital-strategy.ec.europa.eu/en/library/commission-publishes-guidelines-prohibited-artificial-intelligence-ai-practices-defined-ai-act
European Commission. (2020, February 19). White paper on artificial intelligence: A European approach to excellence and trust. https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en
European Parliament, Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L, 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
European Parliament. (2021). European Parliament resolution of 20 January 2021 on artificial intelligence: Questions of interpretation and application of international law in so far as the EU is affected in the areas of civil and military uses and of state authority outside the scope of criminal justice (2020/2013(INI)). Official Journal of the European Union, C456/34, 2021/C456/04. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=oj:JOC_2021_456_R_0005
European Parliament. (2020). European Parliament resolution of 20 October 2020 on intellectual property rights for the development of artificial intelligence technologies (2020/2015(INI)). Official Journal of the European Union, C404/129, 2021/C404/06. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=oj:JOC_2021_404_R_0007
Fengchun, M., & Mutlu, C. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Ferrara, E. (2026). The generative AI paradox: GenAI and the erosion of trust, the corrosion of information verification, and the demise of truth. Future Internet, 18(2), 73. https://doi.org/10.3390/fi18020073
Floridi, L. (2023). The ethics of artificial intelligence: Principles, challenges, and opportunities. Oxford Academic. https://doi.org/10.1093/oso/9780198883098.001.0001
Forrest, R. (2025). Chatting with the assistant: Enabling students to use ChatGPT for self-assessment before assignment submission. Education and New Developments, 2, 526–530. https://doi.org/10.36315/2025v2end114
Grinbaum, A., & Adomaitis, L. (2024). Dual use concerns of generative AI and large language models. Journal of Responsible Innovation, 11(1), Article 2304381. https://doi.org/10.1080/23299460.2024.2304381
Grinschgl, S., & Neubauer, A. (2022). Supporting cognition with modern technology: distributed cognition today and in an AI-enhanced future. Frontiers in Artificial Intelligence, 5, Article 908261. https://doi.org/10.3389/frai.2022.908261
Hähnel, M. (2026). A new age of dual-use technologies: Identifying and evaluating AI-induced risks and opportunities in military medical ethics. In B. Koch & D. Winkler (Eds.), Artificial Intelligence Ethics in Military Medicine and Humanitarian Healthcare. Military and Humanitarian Health Ethics (pp. 113–126). Springer. https://doi.org/10.1007/978-3-032-11331-3_7
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2
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
Kalniņa, D., Nīmante, D., & Baranova, S. (2024). Artificial intelligence for higher education: Benefits and challenges for pre-service teachers. Frontiers in Education, 9, Article 1501819. https://doi.org/10.3389/feduc.2024.1501819
Kay, J., Kasirzadeh, A., & Mohamed, S. (2024). Epistemic injustice in generative AI. Proceedings of the AAAI ACM Conference on AI, Ethics, and Society, 7(1), 684–697. https://doi.org/10.1609/aies.v7i1.31671
Livingstone, S., Pothong, K., Atabey, A., Hooper, L., & Day, E. (2024). The googlization of the classroom: Is the UK protecting children’s data and rights? Computers and Education Open, 7, Article 100195. https://doi.org/10.1016/j.caeo.2024.100195
Mansur, A., Amaliya, W., Lestari, W. A., & Adiba, F. (2025). Opportunities and threats of the ChatGPT revolution in completing students’ final assignments. Journal of Education Research and Evaluation, 9(1), 153–164. https://doi.org/10.23887/jere.v9i1.83227
Marcus, J. (2025, January 8). A looming “demographic cliff”: Fewer college students and ultimately fewer graduates. The Hechinger Report. https://www.npr.org/2025/01/08/nx-s1-5246200/demographic-cliff-fewer-college-students-mean-fewer-graduates
Maslov, Y. V., Pypenko, I. S., & Melnyk, Yu. B. (2021). The impact of COVID pandemic consequences on public demand for competence formation in humanitarian education. Rupkatha Journal on Interdisciplinary Studies in Humanities, 13(4). https://doi.org/10.21659/rupkatha.v13n4.26
Melnyk, Yu. B., & Pypenko, I. S. (2024). Artificial intelligence as a factor revolutionizing higher education. International Journal of Science Annals, 7(1), 8–16. https://doi.org/10.26697/ijsa.2024.1.2
Melnyk, Y. B., & Pypenko, I. S. (2026). Creating a higher education ecosystem based on artificial intelligence implementation. In Y. B. Melnyk & M. A. Segooa (Eds.), Artificial Intelligence in Digital Society, Vol. 1. (pp. 161–173). KRPOCH. https://doi.org/10.26697/aids.2026.11
Melnyk, Yu. B., & Pypenko, I. S. (2020). How will blockchain technology change education future?! International Journal of Science Annals, 3(1), 5–6. https://doi.org/10.26697/ijsa.2020.1.1
Melnyk, Yu. B., & Pypenko, I. S. (2023). The legitimacy of artificial intelligence and the role of ChatBots in scientific publications. International Journal of Science Annals, 6(1), 5–10. https://doi.org/10.26697/ijsa.2023.1.1
Melnyk, Y. B. (2025). Should we expect ethics from artificial intelligence: The case of ChatGPT text generation. International Journal of Science Annals, 8(1), 5–11. https://doi.org/10.26697/ijsa.2025.1.5
Organisation for Economic Co-operation and Development. (2019, May 22). Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449). OECD Legal Instruments. https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449
Pypenko, I. S. (2024). Benefits and challenges of using artificial intelligence by stakeholders in higher education. International Journal of Science Annals, 7(2), 28–33. https://doi.org/10.26697/ijsa.2024.2.7
Pypenko, I. S. (2019). Digital product: The essence of the concept and scopes. International Journal of Education and Science, 2(4), 56. https://doi.org/10.26697/ijes.2019.4.41
Pypenko, I. S. (2023). Human and artificial intelligence interaction. International Journal of Science Annals, 6(2), 54–56. https://doi.org/10.26697/ijsa.2023.2.7
Pypenko, I. S., & Melnyk, Yu. B. (2021). Principles of digitalisation of the state economy. International Journal of Education and Science, 4(1), 42–50. https://doi.org/10.26697/ijes.2021.1.5
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Saura, J. R., Škare, V., & Dosen, D. O. (2024). Is AI-based digital marketing ethical? Assessing a new data privacy paradox. Journal of Innovation & Knowledge, 9(4), Article 100597. https://doi.org/10.1016/j.jik.2024.100597
Schmidt, J. T., & Tang, M. (2020). Digitalization in education: Challenges, trends and transformative potential. In M. Harwardt, P. Niermann, A. Schmutte, & A. Steuernagel (Eds.), Führen und Managen in der Digitalen Transformation (pp. 287–312). Springer Gabler. https://doi.org/10.1007/978-3-658-28670-5_16
Schut, L., Tomašev, N., McGrath, T., Hassabis, D., Paquet, U., & Kim, B. (2025). Bridging the human-AI knowledge gap through concept discovery and transfer in AlphaZero. Proceedings of the National Academy of Sciences, 122(13), e2406675122. https://doi.org/10.1073/pnas.2406675122
Selwyn, N., Ljungqvist, M., & Sonesson, A. (2025). When the prompting stops: exploring teachers’ work around the educational frailties of generative AI tools. Learning, Media and Technology, 50(3), 310–323. https://doi.org/10.1080/17439884.2025.2537959
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404
Stadnik, A. V., & Mykhaylyshyn, U. B. (2026). The evolution of the theory and practice of artificial intelligence. In Y. B. Melnyk & M. A. Segooa (Eds.), Artificial Intelligence in Digital Society, Vol. 1. (pp. 01–16). KRPOCH. https://doi.org/10.26697/aids.2026.1
Stead, W. W. (2018). Clinical implications and challenges of artificial intelligence and deep learning. Jama, 320(11), 1107–1108. https://doi.org/10.1001/jama.2018.11029
Susskind, D. (2020). A world without work: Technology, automation and how we should respond. Penguin UK. http://reparti.free.fr/susskind2020.pdf
Söderlund, M. (2026). Human versus non-human agents who make predictions of service delivery: An examination of user responses to agents’ view of the future. Journal of Retailing and Consumer Services, 89, Article 104638. https://doi.org/10.1016/j.jretconser.2025.104638
Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Mones A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695–6726. https://doi.org/10.1007/s10639-022-11431-8
UNESCO. (2021). Recommendation on the ethics of artificial intelligence (SHS/BIO/PI/2021/1). UNESCO Digital Library. https://unesdoc.unesco.org/ark:/48223/pf0000381137
Williamson, B., Molnar, A., & Boninger, F. (2024, March 5). Time for a pause: Without effective public oversight, AI in schools will do more harm than good. National Education Policy Center. http://nepc.colorado.edu/publication/ai
Melnyk Yuriy Borysovych (Corresponding Author) – https://orcid.org/0000-0002-8527-4638;
Pypenko Iryna Sergiivna – https://orcid.org/0000-0001-5083-540X; Doctor of Philosophy in Economics, Affiliated Associate Professor, Secretary of Board, Kharkiv Regional Public Organization “Culture of Health”; Co-Director, Scientific Research Institute KRPOCH, Ukraine.
| |
APA
Melnyk, Y. B., & Pypenko, I. S. (2026). Artificial intelligence in digital society. International Journal of Science Annals, 9(1), 5–15. https://doi.org/10.26697/ijsa.2026.1.1
Harvard
Melnyk, Y. B., & Pypenko, I. S. "Artificial intelligence in digital society." International Journal of Science Annals, [online] 9(1), pp. 5–15. viewed 30 June 2026, https://culturehealth.org/ijsa_archive/ijsa.2026.1.1.pdfVancouver
Melnyk, Y. B., & Pypenko, I. S. Artificial intelligence in digital society. International Journal of Science Annals [Internet]. 2026 [cited 30 June 2026]; 9(1): 5–15. Available from: https://culturehealth.org/ijsa_archive/ijsa.2026.1.1.pdf https://doi.org/10.26697/ijsa.2026.1.1
