Authors
- Eliseev Anton V.
- Martynov Daniil V.
- Ryabikova Diana L.
- Shunin Ivan V.
Annotation
The article is devoted to substantiating and describing a methodology for training future computer science teachers in the application of generative neural networks in designing individual educational trajectories for students. The methodology considers generative neural networks as a tool for pedagogical design and supporting educational activities. A methodological model, modular training content, typical professional tasks, and an approach to experimental validation are proposed. It is demonstrated that the methodologically correct integration of generative neural networks enhances the individualization of computer science teaching, improves the quality of feedback and the variability of educational materials, while maintaining students’ agency.
How to link insert
Eliseev, A. V., Martynov, D. V., Ryabikova, D. L. & Shunin, I. V. (2026). METHODOLOGY FOR TRAINING FUTURE COMPUTER SCIENCE TEACHERS IN THE APPLICATION OF GENERATIVE NEURAL NETWORKS IN DESIGNING INDIVIDUAL EDUCATIONAL TRAJECTORIES OF STUDENTS Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", № 2 (76), 78. https://doi.org/10.24412/2072-9014-2026-276-78-94
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