Neural network as a procedural innovation in digital ed ucational environment

   Introduction. Higher education digitalization reveals a gap between current level of faculty’s digital competencies and increased demands for organizing educational processes in digital environments. Many instructors struggle to use modern digital tools and technologies, reducing educational effectiveness. The authors argue for integrating artificial intelligence into educational activities, presenting both theoretical foundations and ways of practical implementation of an approach that combine autodidactic/self-learning principles with modern AI capabilities. The article emphasizes personalized learning schemes enabled by a digital Assistant that mediates between Large Language Models (LLMs) and faculty members pursuing professional development. Thus, it is the Assistant that enables procedural innovation by organizing educational content into meaningful chunks. These modules provide verified, structured content with flexible navigation, feedback, and adaptive learning schemes.

   Purpose setting. The article aims to develop and validate a model for higher education faculty professional development in a digital educational environment that enhances their digital competencies and promotes effective integration of digital technologies in modern university education.

   Methodology and methods of the study. The authors used analysis of scientific publications and regulatory documents; professional development process modeling; comparative analysis of existing training programs; faculty surveys; expert evaluation; statistical processing of the results; modeling pedagogical phenomena under uncertainty, combining into an united conceptual framework a systematic approach to analysis of the process of teacher’s professional development; competency-based approach to assessment of their professional development; and personalized approach to building individual learning trajectories for each student.

   Results. 1. Developed and tested faculty development model in digital environment, including: diagnostic component; content module; technological component; evaluation and resultative unit. 2. Identified key faculty digital competencies: digital tool proficiency (82 % achieved basic level); creation of digital content (65 % reached intermediate level); organization of online interaction (75 % demonstrate confident mastery). 3. Statistically confirmed model effectiveness: 43 % increase in digital literacy; 38 % increase in student’s satisfaction with teaching quality; 56 % increase in use of digital technologies in teaching. 4. The bank of methodological materials and digital tools for faculty has been created.

   Conclusion. The Digital Assistant simplifies teacher’s training: it serves as an intermediary between the learner and the language model, creating conditions for dialogue and independent formation of «living knowledge».

   The main conclusion is that the neural network should not replace learning itself.

   Using the Assistant helps to avoid the «temptation» of simply copying ready-made answers from the web. Instead, it transforms interaction with AI into a constructive dialogue of co-creating original content. This releases one’s own creative potential by delegating technical, non-creative tasks to the Assistant.

1. Nenashev M. B., Dakhin A. N. Teaching Communication or Our Practice of Communications. Public Education, 2024, no. 8, pp. 75–87. (in Russ.)

2. Nenashev M. B., Dakhin A. N. Motivation in Action or Activity as Motivation. Pedagogy, 2024, vol. 88, no. 12, pp. 72–82. (in Russ.)

3. Dakhin A. N. Education Projects and Education Through Projects. Pedagogy, 2016, no. 2, pp. 3–8. (in Russ.)

4. Lotman Yu. M. Dynamic Model of Semiotic System. Works on Sign Systems (Sign Systems Studies), 1978, vol. 10, pp. 18–33. (in Russ.)

5. Saussure F. Course in General Linguistics. Edited by R. O. Shor. Moscow: Yurait Publ., 2019, 303 p. (Anthology of Thought). (in Russ.)

6. Gadamer H.-G. Truth and Method: Foundations of Philosophical Hermeneutics. Translated from German. General ed. and intro. article by B. N. Bessonov. Moscow: Progress Publ., 1988, 704 p. (in Russ.)

7. Senko Yu. V. Education is Always on the Eve of Itself. Pedagogy, 2004, no. 5, pp. 22–29. (in Russ.)

8. Verbitsky A. A. Theory and Technologies of Contextual Education : Textbook. Moscow: MPGU, 2017, 268 p. (in Russ.)

9. Rumelhart D. E. Schemata: The building blocks of cognition. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.). Theoretical issues in reading comprehension. Hillsdale, NJ: Erlbaum Publ., 1980, pp. 33–58.

10. Norman D. A. The psychology of everyday things. New York: Basic Books, 1988.

11. Barrows H. S., Tamblyn R. M. Problem-based learning: An approach to medical education. New York: Springer Publ., 1980.

12. Matyushkin A. M. Problem Situations in Thinking and Learning. Moscow: Prosveshchenie Publ., 1972, 241 p. (in Russ.)

13. Makhmutov M. I. Problem-Based Learning: Basic Theoretical Issues. Moscow: Pedagogika Publ., 1975, 238 p. (in Russ.)

14. Galperin P. Ya. Psychology of Thinking and the Theory of Gradual Formation of Mental Actions. Research on Thinking in Soviet Psychology. Moscow: Nauka Publ., 1966, pp. 236–277. (in Russ.)

15. Vygotsky L. S. Psychology. Moscow: EKSMO-Press Publ., 2000, 1008 p. (World of Psychology Series). (in Russ.)

16. Bernstein N. A. Physiology of Movement and Activity. Moscow: Nauka Publ., 1990, 494 p. (in Russ.)

17. Barbashina E. V. «Narrative Paradigm» by W. Fisher in the Context of Narrative Research Development. Philosophy of Science, 2023, no. 3 (98), pp. 57–70. (in Russ.)