Transformative potential of AI in education: сhallenges in developing new conceptual models of pedagogical design
DOI: 10.23951/2307-6127-2025-5-87-98
The article explores the transformative potential of artificial intelligence (AI) technologies in education and the key barriers to their integration into pedagogical practice. The authors emphasize that, despite AI’s capacity for personalized learning and its potential to radically rethink educational models, its application often remains limited to technical support for routine tasks, without addressing the fundamental foundations of pedagogical design. The study analyzes classical theoretical models of technology adoption — TAM (Technology Acceptance Model), SAMR (Substitution, Augmentation, Modification, Redefinition), and SCOT (Social Construction of Technology) — identifying their limitations in explaining the socio-professional contexts of AI implementation in education. The core thesis of the work lies in the necessity of synthesizing SAMR and SCOT models to overcome their individual limitations. SAMR provides a framework for analyzing stages of transformation (from substitution to redefinition), while SCOT explains how social negotiations among teachers, administrators, and students influence the acceptance or rejection of technologies. It is demonstrated that fragmented AI implementation stems not only from technical challenges but also from the inertia of the pedagogical community, which clings to pre-digital approaches, as well as fears of algorithmic autonomy and the erosion of educators’ professional roles. The practical significance of the research lies in recommendations for fostering sustainable AI integration practices, including developing teachers’ digital competence, involving them in designing AI tools, and promoting ethical reflection. The authors stress that transitioning to the “redefinition” of educational practices is possible only through social consensus that considers both technological capabilities and the values of the pedagogical community. The article contributes to the development of hybrid conceptual frameworks that combine technological and socio-cultural analysis, potentially opening new directions for research into the symbiosis of human and artificial intelligence in education.
Keywords: artificial intelligence in education, pedagogical design, digitalization, technology integration models, teacher’s professional identity, ethical risks, social construction of technologies
References:
1. O natsional’nykh tselyakh i strategicheskikh zadachakh razvitiya Rossiyskoy Federatsii na period do 2024 goda: Ukaz Prezidenta RF ot 7 maya 2018 g. no. 204 [Decree of the President of the Russian Federation No. 204 of May 7, 2018 “On National Goals and Strategic Objectives for the Development of the Russian Federation until 2024”] (in Russian). URL: https://base.garant.ru/71937200/ (accessed 15 April 2025).
2. Ponkin I.V., Kupriyanovskiy V.P., Moreva S.L., Ponkin D.I. Proryvnyye tekhnologicheskiye innovatsii: ponyatiye, znacheniye i ontologiya [Breakthrough technological innovations: concept, significance, and ontology]. Mezhdunarodnyy zhurnal otkrytykh informatsionnykh tekhnologiy – International Journal of Open Information Technologies, 2020, vol. 8, no. 8, pp. 60–68 (in Russian).
3. Kshirsagar P., Jagannadham D., Alqahtani H., Naveed Q., Islam S., Thangamani M., Dejene M. Human Intelligence Analysis through Perception of AI in Teaching and Learning. Computational Intelligence and Neuroscience, 2022. URL: https://doi.org/10.1155/2022/9160727 (accessed 27 April 2025).
4. Popenici S., Kerr S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 2017, no. 12. URL: https://doi.org/10.1186/s41039-017-0062-8 (accessed 27 April 2025).
5. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign, 2021. 242 p.
6. Luckin R., Cukurova M. Designing educational technologies in the age of AI: A learning sciences-informed approach. British Journal of Educational Technology, 2019, vol. 50, no. 6, pp. 2824–2838. URL: https://doi.org/10.1111/bjet.12861 (accessed 27 April 2025).
7. Crompton H., Burke D. Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 2023, no. 20, p. 22. URL: https://doi.org/10.1186/s41239-023-00392-8 (accessed 27 April 2025).
8. Buckingham Shum S., Luckin R. Learning analytics and AI: Politics, pedagogy and practices. British Journal of Educational Technology, 2019, vol. 50, pp. 2785–2793.
9. Ochoa X., Knight S., Wise A.F. Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility. Journal of Learning Analytics, 2020, vol. 7, no. 3, pp. 1–5. URL: https://doi.org/10.18608/jla.2020.73.1 (accessed 27 April 2025).
10. Zawacki-Richter O., Marín V.I., Bond M., Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 2019, vol. 16, no. 1, p. 39-40. URL: https://doi.org/10.1186/s41239-019-0171-0 (accessed 27 April 2025).
11. Selwyn N. Should robots replace teachers? AI and the Future of Education. 1st ed. Cambridge: Polity Press, 2019. 160 p.
12. Mishra P., Koehler M.J. Technological Pedagogical Content Knowledge: A framework for teacher knowledge. Teachers College Record, 2006, vol. 108, no. 6, pp. 1017–1054.
13. Cukurova M., Luckin R., Clark-Wilson A. Creating the Golden Triangle of Evidence informed Education Technology with EDUCATE. British Journal of Educational Technology, 2019, vol. 50, no. 2, pp. 490–504.
14. Davis F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 1989, vol. 13, no. 3, pp. 319–340.
15. Venkatesh V., Thong J.Y., Xu X. Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 2016, vol. 17, no. 5, pp. 328–376.
16. Naseri R., Abdullah M. S. Understanding AI Technology Adoption in Educational Settings: A Review of Theoretical Frameworks and their Applications. Information Management and Business Review, 2024, vol. 16, no. 3, pp. 174–181. URL: https://doi.org/10.22610/imbr.v16i3(I).3963 (accessed 27 April 2025).
17. Puentedura R.R. Transformation, Technology, and Education, 2006. URL: http://hippasus.com/resources/tte/ (accessed 27 April 2025).
18. Romrell D., Kidder L., Wood E. The SAMR Model as a Framework for Evaluating mLearning. Journal of Asynchronous Learning Network, 2014, no. 18. doi: 10.24059/olj.v18i2.435
19. Zamri M., Mohamad S.N. A. Technology Integration in Education: A Review and Analysis of SAMR Model. International Journal of Research and Innovation in Social Science, 2025, vol. VIII, pp. 6195–6200. doi: 10.47772/IJRISS.2024.803463
20. Swindell A., Greeley L., Farag A., Verdone B. Against artificial education: Towards an ethical framework for generative artificial intelligence (GenAI) use in education. Online Learning Journal, 2024, vol. 28, no. 2, pp. 7–28.
21. Bijker W.E., Hughes T.P., Pinch T.J. The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology. Cambridge: MIT Press, 1987. 456 p.
22. Williamson B. The Social life of AI in Education. International Journal of Artificial Intelligence in Education, 2023, vol. 34, pp. 97–104.
23. Xu W., Ouyang F. The application of AI technologies in STEM education: a systematic review from 2011 to 2021. IJ STEM Ed, 2022, vol. 9, pp. 59-60. URL: https://doi.org/10.1186/s40594-022-00377-5 (accessed 27 April 2025).
24. Ullmann T., Edwards C., Bektik D., Herodotou C., Whitelock D. Towards Generative AI for Course Content Production: Expert Reflections. European Journal of Open, Distance and E-Learning, 2024, vol. 26, pp. 20–34. doi: 10.2478/eurodl-2024-0013
25. Sysoyev P.V. Iskusstvennyy intellekt v obrazovanii: osvedomlonnost’, gotovnost’ i praktika primeneniya prepodavatelyami vysshey shkoly tekhnologiy iskusstvennogo intellekta v professional’noy deyatel’nosti [Artificial intelligence in education: awareness, readiness and practice of using artificial intelligence technologies in professional activities by higher education teachers]. Vyssheye obrazovaniye v Rossii – Higher education in Russia. 2023, vol. 32, no. 10, pp. 9–33 (in Russian).
26. Ilyushin L.S., Torpashova N.A. Tekhnologii iskusstvennogo intellekta kak resurs transformatsii obrazovatel’noy praktiki [Artificial intelligence technologies as a resource for transforming educational practice]. Yaroslavskiy pedagogicheskiy vestnik – Yaroslavl Pedagogical Bulletin, 2024, vol. 138, no. 3. pp. 62–71 (in Russian). URL: https://elibrary.ru/ADWMMG (accessed 27 April 2025).
( 421.76 kB ) 
( 408.89 kB )
Issue: 5, 2025
Series of issue: Issue 5
Rubric: METHODOLOGY AND TECHNOLOGY OF VOCATIONAL EDUCATION
Pages: 87 — 98
Downloads: 175