Science for Education Today, 2025, vol. 15, no. 3, pp. 159–179
© Toktarova V. I., Semenova D. A., 2025
UDC: 
004+378.14+37.03+159.91

The model of integrating a neurocognitive assistant into the educational process of the HEI: Structural and content analysis

Toktarova V. I. 1 (Yoshkar-Ola, Russian Federation), Semenova D. A. 1 (Yoshkar-Ola, Russian Federation)
1 Mari State University
Full text (Open Access) — Downloaded 48
Abstract: 

Introduction. The article is devoted to the problem of personalization of the educational process through neurocognitive technologies in the context of digital transformation of higher education. The purpose of the article is to theoretically substantiate and present a structural and content model of integrating a neurocognitive assistant into the educational environment of a HEI, to determine the components and describe the results of assessing its effectiveness in the process of preparing future teachers.
Materials and Methods. The methodological basis of the study was formed by the systemic, personality-centered, cognitive-activity and information-communication approaches. Theoretical methods of analysis and synthesis, empirical methods of educational experiment, diagnostic methods (Edinburgh Handedness Inventory, SOLAT) and methods of mathematical and statistical data processing were used. Experimental work on assessing the effectiveness of integrating a neurocognitive assistant into the educational process was carried out within the framework of the discipline ‘Project Activity Management’ at the Pedagogical Institute of the Mari State University. 104 students took part in the study.
Results. The main results consist in the development of a structural and content model, including functional-target, content-technological and result-criterial components. The article examines the current aspects of the implementation of neurocognitive technologies in teaching practice and identifies the mechanisms of their integration into the educational environment. It is emphasized that such technologies contribute to a more accurate consideration of individual cognitive characteristics of students based on the diagnosis of functional asymmetry of the brain, the selection of optimal forms and methods of presenting educational material and providing personalized recommendations to students. The characteristics of team members during the implementation of each project stage are identified and described depending on their functional asymmetry of the brain. Experimental data confirmed a high level (correlation coefficient R = 0.7) of the effectiveness of using a neurocognitive assistant to personalize the educational process, improve academic performance and develop students’ project competence.
Conclusions. The study concludes that the proposed model of integration of the neurocognitive assistant has high potential for modernization of higher education in designing educational programs and recommendations for adjusting educational content to individual cognitive characteristics of students, and developing digital systems of personalized learning. For wider integration of this technology, further development of personalization algorithms, elimination of technical limitations and training teachers for their use are required.

Keywords: 

Neurocognitive assistant; Personalization of learning; Cognitive characteristics of students; Functional asymmetry of the brain; Structural and content model; Digital educational environment; Pedagogical experiment

For citation:
Toktarova V. I., Semenova D. A. The model of integrating a neurocognitive assistant into the educational process of the HEI: Structural and content analysis. Science for Education Today, 2025, vol. 15, no. 3, pp. 159–179. DOI: http://dx.doi.org/10.15293/2658-6762.2503.08
References: 
  1. Ignatova J. P., Makarova I. I., Zenina O. J., Aksenova A. V. Current aspects of functional hemispheric asymmetry studying (literature review). Human Ecology, 2016, vol. 23 (9), pp. 30-39. (In Russian) URL: https://elibrary.ru/item.asp?id=26606113  DOI: http://dx.doi.org/10.33396/1728-0869-2016-9-30-39
  2. Luk'yanova I. E., Sigida E. A., Utenkova S. N. Functional asymmetry of the brain: New opportunities in defectology. Special Education, 2020, no. 2, pp. 62-72. (In Russian) URL: https://elibrary.ru/item.asp?id=43152466  DOI: http://dx.doi.org/10.26170/sp20-02-06
  3. Smirnov I. V., Panov A. I., Skrynnik A. A., Chistova E. V. Personal cognitive assistant: Concept and key principals. Computer Science and its Applications, 2019, vol. 13 (3), pp. 105-113. (In Russian) URL: https://elibrary.ru/item.asp?id=41264226  DOI: http://dx.doi.org/10.14357/19922264190315
  4. Stepanova G. K. I., Krivoshapkin K. A. I., Makarov A. A., Sliyugrov N. I. Hemispheric dominance in students. Bulletin of the North-Eastern Federal University named after M.K. Ammosov. Series: Medical Sciences, 2017, vol. 4 (9), pp. 96-99. (In Russian) URL: https://elibrary.ru/item.asp?id=32260371
  5. Toktarova V. I., Rebko O. V., Semenova D. A. Comparative analysis of models of educators’ digital competencies in the context of digital transformation of education. Science for Education Today, 2023, vol. 13 (5), pp. 79-104. (In Russian) URL: https://elibrary.ru/item.asp?id=54808511  DOI: http://dx.doi.org/10.15293/2658-6762.2305.04
  6. Abushihab I. The Theory of multiple intelligences and its implications in teaching foreign languages: An analytical study. Journal of Southwest Jiaotong University, 2024, vol. 59 (1), pp. 4. DOI: http://dx.doi.org/10.35741/issn.0258-2724.59.1.4
  7. Ansari D., De Smedt B., Grabner R. Neuroeducation – A critical overview of an emerging field. Neuroethics, 2011, vol. 5 (2), pp. 105–117. DOI: http://dx.doi.org/10.1007/s12152-011-9119-3
  8. Geake J., Cooper P. Cognitive neuroscience: Implications for education? Westminster Studies in Education, 2003, vol. 26 (1), pp. 7-20. DOI: http://dx.doi.org/10.1080/0140672030260102
  9. Goswami U. Neuroscience and education: From research to practice? Nature Reviews Neuroscience, 2006, vol. 7(5), pp. 406-413. DOI: http://dx.doi.org/10.1038/nrn1907

10. Hachem M., Daignault K., Wilcox G. Impact of educational neuroscience teacher professional development: Perceptions of school personnel. Frontiers in Education, 2022, vol. 7, pp. 912827. DOI: http://dx.doi.org/10.3389/feduc.2022.912827

11. Hellige J., Cox P., Litvac L. Information processing in the cerebral hemispheres: Selective hemispheric activation and capacity limitations. Journal of Experimental Psychology. General, 1979, vol. 108 (2), pp. 251-279. DOI: http://dx.doi.org/10.1037/0096-3445.108.2.251

12. Khanal L., Shah S., Koirala S., Rimal J., Adhikari B. R., Baral D. Relationship between hemispheric preference score and academic performance among preclinical medical students studying medicine and dentistry. International Journal of Applied Basic Medical Research, 2023, vol. 13 (1), pp. 16-22. DOI: http://dx.doi.org/10.4103/ijabmr.ijabmr_440_22

13. Maedche A., Morana S., Schacht S., Werth D., Krumeich J. Advanced user assistance systems. Business and Information Systems Engineering, 2016, vol. 58 (5), pp. 367-370. DOI: https://doi.org/10.1007/s12599-016-0444-2

14. Sajja R., Sermet, Y., Cikmaz M. Cwiertny D., Demir I. Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. ArXiv, 2023, vol. 15 (10), pp. 596. DOI: http://dx.doi.org/10.48550/arXiv.2309.10892

15. Sperry R. W. Hemisphere deconnection and unity in conscious awareness. American Psychologist, 1968, vol. 23 (10), pp. 723-733. DOI: https://doi.org/10.1037/h0026839

16. Weinmann M., Schneider C., Brocke J. Digital nudging. Business & Information Systems Engineering, 2016, vol. 58 (6), pp. 433-436. DOI:  https://doi.org/10.1007/s12599-016-0453-1

Date of the publication 30.06.2025