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Subjectness of learners in the process of neuro‑digitalization of education
https://doi.org/10.20913/2618-7515-2025-1-17
Abstract
Introduction. Neuro-digitalization of education is the process of introducing, testing and improving digital technologies in education and the principles of learning and devices based on them, that plays an important role in the modern development of both school and university education. It includes also implementing of the principle of subjectness and related principles of modern education (individualization, humanization, activation). Purpose setting. In modern psychological and pedagogical science and practice, the tasks of studying and ensuring the subjectness of students in the process of neuro-digitalization of education have been set and solved, however, there is no systematic study, an integrative model of the subjectness of students in the process of neuro-digitalization of education. The purpose of the study is to analyze the phenomenon of students’ subjectness in this process. Methodology and methods of the study. The methodological basis of the study is a systematic approach to understanding the phenomenon of students’ subjectness in the process of neuro-digitalization of education. The main research method is a theoretical analysis of discussed phenomenon in this process. Results. Some personal, interpersonal, educational and professional competencies and the subjectness of students themselves are not fully formed and developed (many not only schoolchildren, but also students find it difficult to openly declare their needs / states and positions / goals and values (self-disclosure), it is also difficult to evaluate / reflect on their activity, bear or assign responsibility, present themselves and the results of their educational and professional work (self-presentation), self-determine and identify themselves as future specialists and individuals. Thus, it is important to note the importance of transforming the academic environment and relationships in it in accordance with the objectives of ensuring the subjectness of students, including in the process of neuro-digitalization of education. Conclusion. An integrated understanding of the main, typical conditions that contribute to and hinder the formation and development of students’ subjectness in the process of neuro-digitalization of education is an important step towards ensuring the quality of education. The prospects for the study are related to the development and testing of a system-activity model of students’ subjectivity in the process of neuro-digitalization of education.
About the Authors
G. V. ValeevaRussian Federation
Galina V. Valeeva – candidate of psychological sciences, associate professor, associate professor of the department of life safety and biomedical disciplines, Higher School of Physical Culture and Sports
69 Lenina Ave, Chelyabinsk, 454080
L. E. Filatova
Russian Federation
Liliya E. Filatova – candidate of psychological sciences, associate professor of the department of practical psychology
86 Lenina Str., Voronezh, 394024
P. V. Menshikov
Russian Federation
Petr V. Menshikov – candidate of psychological sciences, associate professor, associate professor of the department of development and education psychology
22/48 Razina Str., Kaluga, 248023
M. R. Arpentieva
Russian Federation
Mariam R. Arpentieva – doctor of psychological sciences, associate professor, leading researcher at the institute of humanitarian technologies and social engineering of the faculty of social sciences and mass communications; leading researcher at the institute of management research and consulting of the faculty «higher school of management»
49 Leningradskiy Ave., Moscow, GSP-3, 125993
References
1. Malinichev, D. M., Arpentyeva, M. R., Khotyeva, R. I., Koptyaeva, S. V. Neuro-digital technologies in inclusive education: a theoretical review. Pedagogy. Theory & Practice, 2023, vol. 8, Issue 7, pp. 721–731. DOI: https://doi.org/10.30853/ped20230116 (In Russ.).
2. Bagadaeva O. Yu., Merezhnikov A. P., Arshinov A. S., Arpentyeva M. R. Modern directions of psychological and pedagogical research of neurodigitalization of inclusive education. Modern information technologies and IT education, 2023, no. 3, pp. 713–725. URL: http://elib.fa.ru/art2023/bv3812.pdf. (In Russ.).
3. Stepanova G. A., Malinichev D. M., Khoteeva R. I., Arpentieva M. R. Neurodigital Technologies and Ethical Problems of Modernization in the Inclusive Education. Psychopedagogy in Law Enforcement, 2022, vol. 27, no. 2 (89), pp. 142–149 (In Russ). DOI: https://doi.org/10.24412/1999-6241-2022-289-142-149 (In Russ.).
4. Vladimirov N. M., Dorovskikh I. G., Menshikov P. V., Arpentyeva M. R. Neuroeducation and problems of subjectivity of inclusion. Special education, 2022, no. 1 (65), pp. 162–175. (In Russ.).
5. Tashcheva, A. I., Gridneva, S. V., Khoteeva, R. I., Setyaeva, N. N., & Arpentieva, M. R. Neurotechnology and development of subjectivity of students and teachers in inclusive education. Vocational Education and Labour Market, 2021, vol. 4, pp. 73–87. DOI: https://doi.org/10.52944/P0RT.2021.47.4.005 (In Russ.).
6. Rogers C. R. Freedom to learn. Canada, Merrill, Maxwell Macmillan, 1994, 406 p.
7. Steinberg M. Toward System Theoretical Foundations for Human – Autonomy Teams. Lawless W. F., Mittu R., Sofge D. A., Shortell T., McDermott T. A. (eds). Systems Engineering and Artificial Intelligence. Cham, Springer, 2021, pp 77–92 DOI: https://doi.org/10.1007/978-3-030-77283-3_5.
8. Koeszegi S. T. Automated Decision Systems: Why Human Autonomy is at Stake. Szapiro T., Kacprzyk J. (eds). Collective Decisions: Theory, Algorithms And Decision Support Systems. Studies in Systems, Decision and Control, vol. 392. Cham, Springer, 2022, pp 155–169. DOI: https://doi.org/10.1007/978-3-030-84997-9_7.
9. Fosch-Villaronga E., Lutz C., Tamò-Larrieux A. Gathering expert opinions for social robots’ ethical, legal, and societal concerns. International Journal of Social Robotics, 2020, vol. 12 (2), pp. 441–458.
10. Breazeal C. Toward sociable robots. Robotics and Autonomous Systems, 2003, vol. 42, pp. 167–175.
11. Floridi, L., Cowls, J. A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 2019, vol. 1 (1), p. 1–14. DOI: https://doi.org/10.1162/99608f92.8cd550d1.
12. Darwall S. The value of autonomy and autonomy of the will. Ethics, 2006, vol. 116, pp. 263–284.
13. O’Neill O. Autonomy and Trust in Bioethics. Cambridge, Cambridge University Press, 2002, 213 p.
14. Mackenzie C., Stoljar N. (Eds.). Relational Autonomy: Feminist Perspectives on Autonomy, Agency, and the Social Self. New York, NY, Oxford University Press, 2023, 300 p. DOI: https://doi.org/10.1093/oso/9780195123333.001.0001.
15. Benson P. Autonomy and oppressive socialization. Social Theory and Practice, 1991, vol. XVI, I, (3), pp. 385–408.
16. Begon J. What are adaptive preferences? Journal of Applied Philosophy, 2015, vol. 32 (3), pp. 241–257.
17. Christman J. The politics of persons: Individual autonomy and socio‑historical selves. Cambridge University Press, 2009, 274 p.
18. Susser D., Roessler B., Nissenbaum H. Technology, autonomy, and manipulation. Internet Policy Review, 2019, vol. 8, Issue 2, pp. 1–22. DOI: https://doi.org/10.14763/2019.2.1410.
19. Meyers D. Personal autonomy and the paradox of feminine socialization. Journal of Philosophy, 1987, vol. 84 (11), pp. 619–628.
20. Mackenzie C. Relational autonomy, normative authority and perfectionism. Journal of Social Philosophy, 2008, vol. 39 (4), pp. 512–533.
21. Friedman M. Autonomy and the split-level self. Southern Journal of Philosophy, 1986, vol. 24 (1), pp. 19–35.
22. Ryan R. M., Deci E. L. Self‑Determination Theory. New York, NY, Guilford Publications, 2017, 756 p.
23. Beauchamp, T. L., DeGrazia, D. Principles and principlism. G. Khushf (Ed.), Handbook of bioethics. New York, NY, Springer, 2004, pp. 55–74.
24. Etzioni A., Etzioni O. AI assisted ethics. Ethics and Information Technology, 2016, vol. 18 (2), pp. 149–156.
25. Hagendorff T. The ethics of Ai ethics: An evaluation of guidelines. Minds and Machines, 2020, vol. 30, pp. 99–120. DOI: https://doi.org/10.1007/s11023-020-09517-8.
26. Jobin A., Lenca M., Vayena, E. The global landscape of AI ethics guidelines. Nature Machine Intelligence, 2019, vol. 1 (9), pp. 389–399.
27. Bostrom N. Superintelligence. Oxford, University Press, 2014, 352 p.
28. Walsh T., Levy N., Bell G., Elliott A., Maclaurin J., Mareels I., Wood F. The Effective and ethical development of Artificial Intelligence. Australia, Melbourne Victoria, Australian Council of Learned Academies (ACOLA), 2019, 250 p.
29. Reeves B., Nass C. I. The media equation: How people treat computers, television, and new media like real people and places. Cambridge, Cambridge University Press, 1996, 305 p.
30. Borenstein J., Arkin R. Robotic nudges: The ethics of engineering a more socially just human being. Science and Engineering Ethics, 2016, vol. 22 (1), pp. 31–46.
31. Nash K., Lea J. M., Davies T., & Yogeeswaran, K. The bionic blues: Robot rejection lowers self-esteem. Computers in Human Behavior, 2018. Vol. 78, pp. 59–63.
32. Walker M. J., Mackenzie C. Neurotechnologies, Relational autonomy, and authenticity. International Journal of Feminist Approaches to Bioethics, 2020, vol. 13 (1), pp. 98–119.
33. Turkle S. Alone together. New York, Basic Books, 2017, 400 p.
34. Sparrow R. Robots, rape, and representation. International Journal of Social Robotics, 2017, vol. 9 (4), pp. 465–477.
35. Thaler R. H., Sunstein C. R. Nudge. New Haven, Yale University Press, 2008, 293 p.
36. Vallor S. Moral deskilling and upskilling in a new machine age. Philosophy & Technology, 2015, vol. 28 (1), pp. 107–124.
37. Gehman S., Gururangan S., Sap M., Choi Y., Smith N. A. Real Toxicity. Prompts: evaluating neural toxic degeneration in language models. T. Cohn, Y. He, Y. Liu (Eds.) Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, 2020, pp. 3356–3369. DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.301.
38. Nissenbaum H. A contextual approach to privacy online. Daedalus, 2011, vol. 140 (4), pp. 32–48.
Supplementary files
For citation: Valeeva G.V., Filatova L.E., Menshikov P.V., Arpentieva M.R. Subjectness of learners in the process of neuro‑digitalization of education. Professional education in the modern world. 2025;15(1):147-155. https://doi.org/10.20913/2618-7515-2025-1-17
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