Transformation of educational practices in the context of post-industrial challenges

author:

Ruslan Filonenko (https://orcid.org/0009-0001-1743-5946)

Relevance of the research. Contemporary higher education is facing challenges associated with the mismatch between the traditional university model and the new socio-economic conditions of the post-industrial era. Particular importance is attached to the ability to acquire new knowledge or adapt existing knowledge to new contexts. This implies the need to think beyond clearly given information and to act in situations where ready-made solutions are absent. In this sense, the emphasis shifts from the accumulation of knowledge to the development of the ability to reinterpret and apply it flexibly. The dynamics of the labor market are associated not only with the disappearance of certain professions, but primarily with the rapid transformation of skill sets within existing ones. This renders the current model of training insufficient and reinforces the need for continuous updating of the competence profile. The situation is further complicated by the rapid spread of generative artificial intelligence (AI). Alongside its significant potential for the personalization of learning, there emerge risks of academic misconduct, simulation of learning activity, and the weakening of students’ cognitive autonomy. Under such conditions, particular importance is attached not only to the renewal of educational practices but also to the reconsideration of the very phenomenon of education as a space for forming the individual’s capacity to remain a subject of thinking in a technologically mediated world.

Problem statement. Despite the growing attention to the transformation of higher education in the context of digitalization, the philosophical and pedagogical discourse still reveals a number of unresolved contradictions that indicate a gap between shifting educational orientations and the persistence of institutional practices. This primarily concerns the mismatch between the competence-based model of training and test-centered forms of assessment, which mainly evaluate the reproduction of knowledge rather than complex learning outcomes. At the same time, the issue of integrating generative artificial intelligence into the educational process remains insufficiently defined, particularly in terms of combining its capabilities with the preservation of students’ independent thinking. Further clarification is also required regarding the mechanisms for implementing the concept of lifelong learning, especially in terms of aligning fundamental education with modular formats and recognizing the results of non-formal learning. A separate challenge is posed by micro-credentials, which, on the one hand, expand educational opportunities, but, on the other hand, raise concerns about the quality and trustworthiness of learning outcomes.

Analysis of recent research and publications. The theoretical foundation for understanding educational transformations in the post-industrial era has been established by classical works that analyze the transition from stability to dynamic uncertainty. Alvin Toffler, in his concept of “future shock”, already in the second half of the twentieth century emphasized the need for an educational model that develops the individual’s ability to continuously update both knowledge and modes of thinking. A fundamental shift in the educational paradigm—from an exclusive focus on knowledge transmission to personalized learning—is discussed in the work of Robert Barr and John Tagg. The necessity of overcoming the mechanistic and standardized “factory model” of schooling is highlighted in the research of Ken Robinson. For Ukrainian scholars, Dmytro Lievit and Tetiana Yevtukhova, the development of soft technologies is considered a key factor in the sustainable development of contemporary education. Issues of digital anthropology and the impact of globalization on the formation of personality within the modern educational space have been explored in the works of Valentyna Voronkova, who addresses the transformation of identity and cognitive practices as factors directly influencing educational processes. The philosophical and anthropological dimension of the emergence of Homo Digitalis has been examined by Maryna Kultaieva. Anatolii Kolot and Olha Herasymenko analyze the interaction of Generation Z with the “Work 4.0” labor platform, reflecting transformations in socio-labor relations in the digital age. In recent years, scholarly attention has increasingly focused on the integration of artificial intelligence into the educational process.

Research task. The purpose of the study is to analyze the directions of updating university education oriented toward the development of meta-skills, AI literacy, and the personalization of learning in the context of digitalization and the spread of generative artificial intelligence, as well as to substantiate the model of lifelong learning as a response to the challenges of technological transformation. Achieving this purpose requires addressing the following objectives: to analyze the transformation of the functions of the university in the context of a post-industrial society; to characterize the impact of labor market dynamics on changing requirements for educational outcomes and the competence profile of graduates; to examine the potential and risks of using generative artificial intelligence in the educational process; and to substantiate the necessity of developing AI literacy as a component of contemporary university education.

Main material presentation. The post-industrial era is radically transforming the functions of the university: the value of education is increasingly determined not by the volume of acquired information, but by the individual’s ability to act under conditions of uncertainty arising from the technologicalization of contemporary society. The university is gradually moving away from its role as a space for transmitting a fixed body of knowledge and is increasingly emerging as an institution that creates an environment for development: it teaches how to think, collaborate, make responsible decisions, build individual learning trajectories, and sustain professional mobility. This shift necessitates a reconsideration of the content of educational programs, approaches to assessment, and the roles of both teachers and students. In contemporary philosophical and scientific discourse, the concept of AI literacy is being formed as a new component of general literacy alongside digital and information literacy. Within the framework of lifelong learning, micro-credentials acquire particular significance as a tool for the rapid and standardized supplementation of the competence profile. The combination of these transformations—the changing role of the university, the restructuring of skill sets under the influence of automation, the emergence of the requirement for AI literacy, and the challenges posed by generative AI to academic integrity—renders the preservation of test-centered assessment logic increasingly untenable.

Conclusions. The conducted analysis has demonstrated that the transformation of higher education in the post-industrial era is of a paradigmatic nature: the traditional knowledge-transmission (“factory”) model is becoming increasingly inadequate to the conditions of digitalization, the dynamic change of professional requirements, and the spread of generative artificial intelligence. It has been substantiated that the key outcome of contemporary university education should not be the reproduction of information, but the development of meta-skills, cognitive autonomy, critical thinking, collaboration, and the capacity for continuous updating of competencies. The dual impact of generative AI has been revealed: while it expands opportunities for the personalization of learning, it simultaneously brings to the forefront the risks of academic misconduct, the simulation of learning activity, and the weakening of independent judgment, which necessitates the systematic implementation of AI literacy and the reorientation of assessment practices. It has been established that lifelong learning and micro-credentials are important tools for professional mobility and resilience; however, their integration requires ensuring the quality, transparency, and comparability of learning outcomes. At the same time, the digital transformation of higher education must be carried out with due consideration of the inclusive dimension.

Ключові слова: post-industrial era, digitalization, generative artificial intelligence, AI literacy, micro-credentials, lifelong learning.

References:

  1. Voronkova, VH & Nikitenko, VO 2022. Filosofiia tsyfrovoi liudyny i tsyfrovoho suspilstva: teoriia i praktyka (Philosophy of the Digital Human and Digital Society: Theory and Practice), monohrafiia, Lviv–Torun : Liha-Pres, 459 p.
  2. Kolot, AM & Herasymenko, OO 2020. ‘Pokolinnia Z i sotsialno-trudova platforma “Pratsia 4.0”: imperatyvy vzaiemodii (Generation Z and the socio-labor platform “Work 4.0”: imperatives of interaction)’, Demohrafiia ta sotsialna ekonomika, no. 2 (40), p. 103–138.
  3. Kultaieva, MD 2020. ‘Homo digitalis, dyhitalna kultura i dyhitalna osvita: filosofsko-antropolohichni i filosofsko-osvitni rozvidky (Homo Digitalis, Digital Culture and Digital Education: Philosophical-Anthropological and Philosophical-Educational Studies)’, Filosofiia osvity, no. 26 (1), p. 8–36. Available from : <http://nbuv.gov.ua/UJRN/PhilEdu_2020_26(1)__3>. [10 Zhovten 2025].
  4. Lievit, D & Yevtukhova, T 2024. ‘Rozvytok soft skills u konteksti staloho rozvytku osvity (Development of Soft Skills in the Context of Sustainable Development of Education)’, Innovatyka. Praktyka, vol. 12, no. 1, p. 57–62.
  5. Peleshok, OO 2025. ‘Shtuchnyi intelekt yak vyklyk akademichnii dobrochesnosti: etychnyi vymir osvitnoi vidpovidalnosti (Artificial Intelligence as a Challenge to Academic Integrity: Ethical Dimension of Educational Responsibility)’, Visnyk Hlukhivskoho natsionalnoho pedahohichnoho universytetu imeni Oleksandra Dovzhenka, no. 2, p. 41–48.
  6. Pro zatverdzhennia planu zakhodiv z realizatsii Kontseptsii rozvytku shtuchnoho intelektu v Ukraini na 2025–2026 roky (On Approval of the Action Plan for the Implementation of the Concept for the Development of Artificial Intelligence in Ukraine for 2025–2026) 2025. Zakonodavstvo Ukrainy, Verkhovna Rada Ukrainy. Available from : <https://zakon.rada.gov.ua/go/457-2025-%D1%80>. [10 Zhovten 2025].
  7. Semyhina, TV & Rashkevych, YuM 2022. ‘Mikrokvalifikatsii yak trend rozvytku suchasnoi bezperervnoi osvity (Micro-credentials as a Trend in the Development of Contemporary Lifelong Education)’, Theory and Practice of Modern Science: collection of scientific papers “SCIENTIA”, Krakow, vol. 2, p. 46–50.
  8. Barr, RB & Tagg, J 1995. ‘From teaching to learning: a new paradigm for undergraduate education’, Change: The Magazine of Higher Learning, vol. 27, no. 6, p. 12–25.
  9. Code of Practice on marking and labelling of AI-generated content 2025. Shaping Europe’s digital future. European Commission. Available from: <https://digital-strategy.ec.europa.eu/en/policies/code-practice-ai-generated-content>. [10 October 2025].
  10. Bauman, Z 2000. Liquid modernity. Cambridge : Polity Press, 228 p.
  11. European Commission 2025. AI Literacy – Questions & Answers. Available from: <https://digital-strategy.ec.europa.eu/en/faqs/ai-literacy-questions-answers [10 October 2025].
  12. Frey, CB Osborne, MA 2017. ‘The future of employment: how susceptible are jobs to computerisation?’, Technological Forecasting and Social Change, vol. 114, p. 254–280.
  13. Goddard, K, Roudsari, A & Wyatt, JC 2012. ‘Automation bias: a systematic review of frequency, effect mediators, and mitigators’, Journal of the American Medical Informatics Association, vol. 19, p. 121–127.
  14. Heckman, JJ & Kautz, T 2012. ‘Hard evidence on soft skills’, Labour Economics, vol. 19, no. 4, p. 451–464.
  15. Kasneci, E, Sessler, K & Küchemann, S et al. 2023. ‘ChatGPT for good? On opportunities and challenges of large language models for education’, Learning and Individual Differences, vol. 103, art. 102274.
  16. Making a European Area of Lifelong Learning a Reality 2001. Brussels, 43 p. Available from: <https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52001DC0678>. [10 October 2025].
  17. On a European approach to micro-credentials for lifelong learning and employability 2022. Official Journal of the European Union, C 243/10. Available from: <https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32022H0627(02) >. [10 October 2025].
  18. Pouliou, A 2024. Exploring the emergence of microcredentials in vocational education and training (VET). Luxembourg : Publications Office of the European Union, 48 p. Available from: <https://doi.org/10.2801/671358>.
  19. Regulation (EU) 2024/1689 of the European Parliament and of the Council 2024. EUR-Lex. Available from: <https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng>. [10 October 2025].
  20. Robinson, K 2011. Out of Our Minds: Learning to Be Creative. Chichester : Capstone Publishing Ltd., 320 p.
  21. Toffler, A 1970. Future shock. New York : Random House, 561 p.
  22. UNESCO 2023. Guidance for generative AI in education and research. Available from: <https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research>. [10 October 2025].
  23. World Economic Forum 2025. The Future of Jobs Report 2025. Geneva : World Economic Forum. Available from: <https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf>. [10 October 2025].

 P1