Science for Education Today, 2026, vol. 16, no. 4, pp. 72–99
© Melnikova L. V., Garkusha N. A., Mukhina Y. N., Vojtik N. V., 2026
UDC: 
37.03+004.81+316.77

The specifics of optimizing regular reflective activities by means of using neural network support: Evaluating the quality of cognitive load redistribution in favor of meaningful analysis

Melnikova L. V. 1 (Tyumen, Russian Federation), Garkusha N. A. 1 (Tyumen, Russian Federation), Mukhina Y. N. 2 (Tyumen, Russian Federation), Vojtik N. V. 1 (Tyumen, Russian Federation)
1 University of Tyumen
2 Tyumen State University
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Abstract: 

Introduction. This paper examines the insufficient theoretical development of regular reflection in master’s programs to enhance the efficiency of project-based foreign language teaching. The research focuses on the identified contradiction between the need to evaluate the effectiveness of neural network support for optimizing reflective activities and the lack of theoretically sound criteria for assessing its impact on the quality and effectiveness of oral communication. The lack of data on the methods of organizing and, ultimately, the effectiveness of reflective practices using neural network support does not allow us to judge the feasibility of using AI tools. Therefore, the aim of this study is to analyze the potential and identify the specifics of the influence of neural network optimization on the effectiveness of regular reflective activities to improve communicative competence within project-oriented learning.
Materials and Methods. The study is based on a systems-activity approach, which allows us to consider reflective activity as an integral component of the holistic pedagogical process and neural network optimization as a tool that guides reflection. This research analyzes reflection as a mechanism for understanding how to solve communicative problems, and the results of applying various reflection modes are assessed from the perspective of a cognitive-task approach. A typology method is used to identify and organize types of reflective activities by revealing essential characteristics and grouping them based on communication types. The empirical method was implemented by introducing regular reflective activities into the structure of classes within the ‘Foreign Language for Academic Purposes’ course. Experimental data were processed using mathematical statistics and comparative analysis to provide a scientific interpretation of the dynamics of the recorded results. Testing, validation, and cognitive analysis of a number of available neural networks were conducted to identify and subsequently evaluate the compliance of neural network-generated texts with the stated research objectives and to select the most appropriate and productive AI tool for optimizing the reflection process.
Results. The study revealed the specific impact of neural network optimization on the potential and effectiveness of regular reflective activities when assessing communicative activities in project-based learning. Analysis of the experimental data identified criterion differences in the results of reflective acts with and without using neural network support. Based on communication modes, the distinctive features of various types of reflective activities – linear, horizontal, and written – were identified and classified. It was found that neural network optimization is primarily applicable to guided reflection. The results of a cognitive analysis of available neural network products are summarized, revealing that the specific features of neural network optimization depend on the neural network architecture. DeepSeek is emphasized to provide the greatest variability and semantic accuracy in generating reflective supports. The authors note that the quantitative difference is a reduction in teacher preparation and reflection time, while the qualitative difference is a redistribution of cognitive load in favor of content analysis.
Conclusions. The study concludes that neural network support enables a systematic approach to regular reflection, optimizing both teacher preparation time and postgraduate students’ learning, demonstrating the potential for improved performance using a guided reflection mode. The typology of reflections simplifies the selection of prompts for generating reflective supports, further contributing to the optimization of reflective activities. More sustainable learning outcomes are achieved by combining the guiding function of AI tools with the teacher’s ability to provide education, ethical assessment, and encourage analytical thinking skills, critical analysis, and awareness in choosing self-education trajectories.

Keywords: 

Neural network optimization; Communicative competence; Typology of reflective activities; Guided reflection; AI tools; Project-oriented method.

For citation:
Melnikova L. V., Garkusha N. A., Mukhina Y. N., Vojtik N. V. The specifics of optimizing regular reflective activities by means of using neural network support: Evaluating the quality of cognitive load redistribution in favor of meaningful analysis. Science for Education Today, 2026, vol. 16, no. 4, pp. 72–99. DOI: http://dx.doi.org/10.15293/2658-6762.2604.04
References: 
  1. Barkova V. V., Mamylina N. V., Uvarina N. V., Bechiev S. S., Vorozheikina A. V., Savchenkov A. V. Professional education as a way to discover the multi-dimensional integrity of a human. Science for Education Today, 2024, vol. 14 (1), pp. 96-123. (In Russian) URL: https://elibrary.ru/qrdadx DOI: https://doi.org/10.15293/2658-6762.2401.05
  2. Dmitrieva Y. A., Korobova S. Y., Gorskikh A. G. Predicting students' performance in project-based learning: A substantiation of an interdisciplinary study. Psychology. Psychophysiology, 2023, vol. 16 (4), pp. 19-32. (In Russian) URL: https://elibrary.ru/bdgkoc
  3. Maliashova A. Yu., Gadelshina S. V. Project-based teaching methods as a factor in the development of students’ readiness for project activities. Pedagogy and Psychology of Education, 2024, no. 4, pp. 90-101. (In Russian) URL: https://elibrary.ru/fuhutf DOI: https://doi.org/10.31862/2500-297X-2024-4-90-101
  4. Crespí P., García-Ramos J. M., Queiruga-Dios M. Project-based learning (PBL) and its impact on the development of interpersonal competences in higher education. Journal of New Approaches in Educational Research, 2022, vol. 11 (2), pp. 259-276. DOI: https://doi.org/10.7821/naer.2022.7.993
  5. Xu Y., Liu W. A project-based learning approach: A case study in China. Asia Pacific education review, 2010, vol. 11 (3), pp. 363-370. DOI: https://doi.org/10.1007/s12564-010-9093-1
  6. Haatainen O., Aksela M. Project-based learning in integrated science education: Active teachers' perceptions and practices. LUMAT: International Journal on Math, Science and Technology Education, 2021, vol. 9 (1), pp. 149-173. DOI: https://doi.org/10.31129/LUMAT.9.1.1392
  7. Listiana L., Rosyidah F., Daesusit R., Hamdani A. S. Fostering metacognitive skills and learning motivation through hybrid learning with innovative learning strategies. International Journal of Cognitive Research in Science, Engineering and Education, 2025, vol. 13 (1), pp. 335-348. DOI: https://doi.org/10.23947/2334-8496-2025-13-2-335-348
  8. Nhat T. N. M., Hien H. T. T. REFLECT: enhancing Vietnamese EFL undergraduates’ pronunciation proficiency. Asian-Pacific Journal of Second and Foreign Language Education, 2025, vol. 10 (1), pp. 25. DOI: https://doi.org/10.1186/s40862-025-00333-0
  9. Glotkina A. A. On the combination of coaching technologies and the method of keeping reflective journals while studying a foreign language in non-linguistic universities. Bulletin of Pedagogical Sciences, 2024, no. 6, pp. 96-104. URL: https://elibrary.ru/ufrhrs DOI: https://doi.org/10.62257/2687-1661-2024-6-96-104

10.Nurjati N., Syahria N., Khan A. K. B. S. ChatGPT-Based Reflective Feedback to Improve EFL Graduate Students’ Academic Writing for Publication. Journal of Languages and Language Teaching, 2025, vol. 13 (4), pp. 1802-1816. DOI: https://doi.org/10.33394/jollt.v13i4.14858

11.Arefian M. H., Çomoğlu I., Dikilitaş K. Understanding EFL teachers’ experiences of ChatGPT-driven collaborative reflective practice through a community of practice lens. Innovation in Language Learning and Teaching, 2026, vol. 20 (2), pp. 318-333. DOI: https://doi.org/10.1080/17501229.2024.2412769

12.Cotton D. R. E., Cotton P. A., Shipway J. R. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 2024, vol. 61 (2), pp. 228-239. DOI: https://doi.org/10.1080/14703297.2023.2190148

13.Sullivan M., Kelly A., McLaughlan P. ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 2023, vol. 6 (1), pp. 31-40. DOI: https://doi.org/10.37074/jalt.2023.6.1.17

14.Abdelhalim S. M. Using ChatGPT to promote research competency: English as a Foreign Language undergraduates' perceptions and practices across varied meta-cognitive awareness levels. Journal of Computer Assisted Learning, 2024, vol. 40 (3), pp. 1261-1275. URL: https://elibrary.ru/xvuyao DOI: https://doi.org/10.1111/jcal.12948

15.Asmolovskaya M. V., Valeyev A. A. Development of reflexive readiness of students to use interactive technologies in the process of teaching a foreign language. Pedagogical Review. 2021, no. 4, pp. 177-187. (In Russian) URL: https://elibrary.ru/ebcvft DOI: https://doi.org/10.23951/2307-6127-2021-4-177-187

16.Tikholaz T. M., Kazakova I. V., Kocheulova O. A. Reflexivity as a condition of psychological well-being and job satisfaction. Alma Mater (Bulletin of Higher School), 2025, no. 1, pp. 53-58. (In Russian). URL: https://elibrary.ru/NYACII DOI: https://doi.org/10.20339/AM.01-25.053

17.Laktionova A. I. The relationship between the differential type of reflection and the components of resilience in different age groups. Institute of Psychology of the Russian Academy of Sciences. Organizational Psychology and Labor Psychology, 2020, vol. 5 (4), pp. 193-217. (In Russian) URL: https://elibrary.ru/wiooch DOI: https://doi.org/10.38098/ipran.opwp.2020.17.4.008

18.Topolskaya E. A. Development of reflective consciousness of students of universities of culture and arts. Bulletin of the Kemerovo State University of Culture and Arts, 2014, no. 28, pp. 230-235. (In Russian) URL: https://elibrary.ru/snezqx

19.Pokrovskaya S. E., Giris A. M. General indicator of positivity of professional self-attitude and reflexivity in the context of the modality of personal meaning of professional activity of teachers. Bulletin of Practical Psychology of Education, 2024, vol. 21 (4), pp. 129-139. (In Russian) URL: https://elibrary.ru/VEOSMM DOI: https://doi.org/10.17759/bppe.2024210412

20.Tikhomirova D., Tregubova A., Ternikov A. Gamification in education and demand for acquired skills: A systematic review. Educational Studies. Moscow, 2024, no. 3, pp. 151-179. (In Russian) URL: https://elibrary.ru/jbxmga DOI: https://doi.org/10.17323/vo-2024-17587

21.Khamidulin V. S. Development of a model of project-based learning. Higher Education in Russia, 2020,no. 1, pp. 135-149. (In Russian) URL: https://elibrary.ru/tclkte DOI: https://doi.org/10.31992/0869-3617-2020-29-1-135-149

22.Maliashova A. Yu., Gadelshina S. V. Project-based teaching methods as a factor in the development of students’ readiness for project activities. Pedagogy and Psychology of Education, 2024, no. 4, pp. 90-101. (In Russian) URL: https://elibrary.ru/fuhutf DOI: https://doi.org/10.31862/2500-297X-2024-4-90-101

23.Hanif S., Wijaya A. F. C., Winarno N. Enhancing students' creativity through STEM project-based learning. Journal of Science Learning, 2019, vol. 2 (2), pp. 50-57. DOI: https://doi.org/10.17509/jsl.v2i2.13271

24.Golubinskaya A. V., Viakhireva V. V. Fundamental theoretical conflicts in the science of critical thinking. Science for Education Today, 2024, vol. 14 (2), pp. 100-123. (In Russian) URL: https://elibrary.ru/eumihh DOI: https://doi.org/10.15293/2658-6762.2402.05

25.Deng Z., Ma W., Han Q.-L., Zhou W., Zhu X., Wen S., Xiang Y. Exploring DeepSeek: A survey on advances, applications, challenges and future directions. IEEE/CAA Journal of Automatica Sinica, 2025,vol. 12 (5), pp. 872-893. URL: https://elibrary.ru/yyoxry DOI: https://doi.org/10.1109/jas.2025.125498

26.Sysoyev P. V., Filatov E. M. ChatGPT in students' research: To forbid or to teach? Russian Language Studies, 2024, no. 2, pp. 300-317. (In Russian) URL: https://elibrary.ru/SOHSKZ DOI: https://doi.org/10.22363/2618-8163-2024-22-2-300-317

27.Budak Durmus F. Swot analysis of the use of ChatGPT in education. Journal of Educational Studies and Multidisciplinary Approaches, 2024, vol. 4 (2), pp. 121-137. DOI: https://doi.org/10.51383/jesma.2024.102

28.Thorp H. H. ChatGPT is fun, but not an author. Science, 2023, vol. 379 (6630), pp. 313. DOI: http://doi.org/10.1126/science.adg7879

29.Tian S., Huang S., Li R., Wei C. A prompt construction method for the reverse dictionary task of large-scale language models. Engineering Applications of Artificial Intelligence, 2024, vol. 133, pp. 108596. DOI: https://doi.org/10.1016/j.engappai.2024.108596

30.Bermus A. G. Benefits and risks of using ChatGPT in higher education: A theoretical review. Pedagogy. Theory & Practice, 2024, vol. 9 (8), pp. 776-787. (In Russian) URL: https://elibrary.ru/DPYUDU DOI: https://doi.org/10.30853/ped20240099

31.Sok S., Heng K. ChatGPT for education and research: A review of benefits and risks. Cambodian Journal of Educational Research, 2023, vol. 3 (1), pp. 110-121. DOI: https://doi.org/10.62037/cjer.2023.03.01.06

32.Baskara R., Mukarto M. Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 2023, vol. 7 (2), pp. 343-358. DOI: http://dx.doi.org/10.21093/ijeltal.v7i2.1387

Date of the publication 31.08.2026