Neurotechnology and artificial intelligence as key factors in the customization of the lifelong learning route
https://doi.org/10.32517/0234-0453-2023-38-3-5-15
Abstract
Artificial Intelligence (AI) technologies are being actively developed and the number of applications is growing rapidly. The education sector is no exception, which in the future can be significantly transformed using AI, for example, in terms of the development of approaches to the customization of the educational route. At the same time, the active development of neuroimaging technologies, as well as progress in neuroscience and neurotechnology, allows providing AI algorithms with important data about the functioning of the learner’s brain.
The article considers the issues of customization of the lifelong learning route (CLLR) using the mentioned assistive technologies: neurotechnologies and artificial intelligence. The basic principle of functioning of the proposed CLLR is formulated: based on the analysis of recorded multimodal data about the learner algorithms based on AI propose actions to implement feedback, which will increase the efficiency and customization of the educational process.
The modular principle of constructing the CLLR system is proposed, and the methods of AI that can find application as the core of an intelligent subsystem of CLLR are discussed. In conclusion, various strategies of application of the proposed CLLR system, which will allow to implement a universal system of educational decision-making support on its basis, are presented.
Keywords
About the Authors
A. A. FedorovRussian Federation
Aleksandr A. Fedorov, Doctor of Sciences (Philosophy), Professor, Rector
Kaliningrad
S. A. Kurkin
Russian Federation
Semen A. Kurkin, Doctor of Sciences (Physics and Mathematics), Docent, Leading Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
Kaliningrad
M. V. Khramova
Russian Federation
Marina V. Khramova, Candidate of Sciences (Education), Director of the Higher School of Education and Psychology, ESC “Institute of Education and Humanities”; Senior Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
Kaliningrad
A. E. Hramov
Russian Federation
Alexander E. Hramov, Doctor of Sciences (Physics and Mathematics), Professor, Chief Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
Kaliningrad
References
1. Unt I. E. Individualization and differentiation of learning. Moscow, Pedagogika; 1990. 190 p. (In Russian.) EDN: SUSDWD
2. Selevko G. K. Modern educational technologies. Moscow, Narodnoe obrazovanie; 1998. 256 p. (In Russian.)
3. Drugova E. A., Zhuravleva I. I., Zakharova U. S., Sotnikova V. E., Yakovleva K. I. Artificial intelligence for learning analytics and instructional design steps: An overview of solutions. Educational Studies Moscow. 2022;(4):107–153. (In Russian.) EDN: BYFFFX. DOI: 10.17323/1814-9545-2022-4-107-153
4. Kushnir M. E., Rabinovich P. D., Zavedensky K. E., Tsarkov I. S. Student’s learning profile is a tool of personal learning logistics. Higher Education in Russia. 2021;30(12):48–58. (In Russian.) EDN: CGJRCF. DOI: 10.31992/0869-3617-2021-30-12-48-58
5. Komarov R. V., Kovaleva T. M. Personalization of the educational process: 3D space of interpretations. MCU Journal of Pedagogy and Psychology. 2021;(1(55)):8–21. (In Russian.) EDN: TNSPKP. DOI: 10.25688/2076-9121.2021.55.1.01
6. Zeer E. F., Krezhevskikh O. V. Conceptual and theoretical foundations of personalised learning. The Education and Science Journal. 2022;24(4):11–39. (In Russian.) EDN: JXGKAX. DOI: 10.17853/1994-5639-2022-4-11-39
7. Fedorov A. A., Budarina A. O., Polupan K. L., Zhitinevich D. G. Digital reform of higher education: Implementation experience. Samara Journal of Science. 2022;11(2):325–331. EDN: DHVVWV. DOI: 10.55355/snv2022112311
8. Kolesnikova I. A. Lifelong education in the 21st century: New research perspectives. Lifelong Education: 21st Century. 2013;1(1):2–18. (In Russian.) EDN: RXQXHD. DOI: 10.15393/j5.art.2013.1941
9. Grigoriev S. G. Artificial intelligence in education. Physics and Mathematics Education: Goals, Achievements, and Prospects: Proc. Int. Scientific and Practical Conf. Minsk, BSPU; 2022: 20–24. (In Russian.) EDN: GOKXAM
10. Grigoriev S. G., Sabitov R. A., Smirnova G. S., Sabitov Sh. R. The concept of the formation and development of a digital intellectual ecosystem of blended university learning. Informatics and Education. 2020;(5):15–23. EDN: LMWOIT. DOI: 10.32517/0234-0453-2020-35-5-15-23
11. Peng Y., Zhang Y., Wang L. Artificial intelligence in biomedical engineering and informatics: An introduction and review. Artificial Intelligence in Medicine. 2010;48(2-3):71–73. DOI: 10.1016/j.artmed.2009.07.007
12. Ashby W. R. An introduction to cybernetics. London, UK, Chapman & Hall Ltd.; 1957. 296 p.
13. Thomas M. S. C., Ansari D., Knowland V. C. P. Annual research review: Educational neuroscience: Progress and prospects. Journal of Child Psychology and Psychiatry. 2019;60(4):477–492. DOI: 10.1111/jcpp.12973
14. Goswami U. Neuroscience and education: From research to practice? Nature Reviews Neuroscience. 2006;7(5):406–413. DOI: 10.1038/nrn1907
15. Bukina T. V., Khramova M. V., Kurkin S. A. Modern research on primary school children brain functioning in the learning process: Review. Izvestiya Vysshikh Uchebnykh Zavedeniy. Applied Nonlinear Dynamics. 2021;29(3):449–456. EDN: UATPQA. DOI: 10.18500/0869-6632-2021-29-3-449-456
16. Kurkin S. A., Grubov V. V., Maksimenko V. A., Pitsik E. N., Khramova M. V., Hramov A. E. System monitoring and adjusting the learning process of primary schoolchildren based on the EEG data analysis. Information and Control Systems. 2020;5(108):50–61. EDN: IYJJLE. DOI: 10.31799/1684-8853-2020-5-50-61
17. Andretta J. R., Worrell F. C., Mello Z. R. Predicting educational outcomes and psychological well-being in adolescents using time attitude profiles. Psychology in the Schools. 2014;51(5):434–451. DOI: 10.1002/pits.21762
18. Virtanen T. E., Vasalampi K., Torppa M., Lerkkanen M.-K., Nurmi J.-E. Changes in students’ psychological well-being during transition from primary school to lower secondary school: A person-centered approach. Learning and Individual Differences. 2019;69:138–149. DOI: 10.1016/j.lindif.2018.12.001
19. Donnelly J. E., Hillman C. H., Castelli D., Etnier J. L., Lee S., Tomporowski P., Lambourne K., Szabo-Reed A. N. Physical activity, fitness, cognitive function, and academic achievement in children: A systematic review. Medicine and Science in Sports and Exercise. 2016;48(6):1197–1222. DOI: 10.1249/MSS.0000000000000901
20. Decker S. L., Hale J. B., Flanagan D. P. Professional practice issues in the assessment of cognitive functioning for educational applications. Psychology in the Schools. 2013;50(3):300–313. DOI: 10.1002/pits.21675
21. Vernon P. A. Der Zahlen-Verbindungs-Test and other trail-making correlates of general intelligence. Personality and Individual Differences. 1993;14(1):35–40. DOI: 10.1016/0191-8869(93)90172-Y
22. Antonenko P. D., van Gog T., Paas F. Implications of neuroimaging for educational research. Handbook of Research on Educational Communications and Technology. Springer, New York, NY, 2014:51–63. DOI: 10.1007/978-1-4614-3185-5_5
23. Wu C. L., Lin T. J., Chiou G. L., Lee C. Y., Luan H., Tsai M. J., Potvin P., Tsai C. C. A systematic review of MRI neuroimaging for education research. Frontiers in Psychology. 2021;12:617599. DOI: 10.3389/fpsyg.2021.617599
24. Xu J., Zhong B. Review on portable EEG technology in educational research. Computers in Human Behavior. 2018;81:340–349. DOI: 10.1016/j.chb.2017.12.037
25. Hramov A. E., Maksimenko V. A., Pisarchik A. N. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021;918:1–133. DOI: 10.1016/j.physrep.2021.03.002
26. Ahmad P., Qamar S., Rizvi S. Q. A. Techniques of data mining in healthcare: A review. International Journal of Computer Applications. 2015;120(15):38–50. DOI: 10.5120/21307-4126
27. LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015;521(7553):436–444. DOI: 10.1038/nature14539
28. Alpaydin E. Introduction to machine learning. London, England, MIT press; 2020. 683 p.
29. Mayer-Schönberger V., Ingelsson E. Big Data and medicine: A big deal? Journal of Internal Medicine. 2018;283(5):418–429. DOI: 10.1111/joim.12721
Review
For citations:
Fedorov A.A., Kurkin S.A., Khramova M.V., Hramov A.E. Neurotechnology and artificial intelligence as key factors in the customization of the lifelong learning route. Informatics and education. 2023;38(3):5-15. (In Russ.) https://doi.org/10.32517/0234-0453-2023-38-3-5-15