Maria V. Kosareva

The relevance of this research is determined by the need for the digital transformation of teacher education and the search for effective models for training student teachers capable of working professionally in the new digital reality. Methodological training, as the core of a teacher's professional development, is of particular importance. It requires updating of content and technologies considering the didactic potential of neural network tools. The aim of the article is to provide a theoretical justification and a substantive description of promising directions for using neural networks in the methodological training of preservice mathematics teachers. The leading research method was a pilot study, implemented at the Institute of Mathematics, Physics, and Computer Science of V.P. Astafyev KSPU and at the Faculty of Mathematics of A.I. Herzen RSPU, with the participation of 48 students and 11 mentor teachers. During the pilot study, a semi-structured interview was used, which made it possible to identify the educational effects of integrating neural network tools into the methodological training process. The main results of the study: three strategic directions for using neural networks are substantiated and described – creating a "digital twin of a classroom," using neural networks as a simulator for testing lesson fragments, and using an AI simulator for complex pedagogical situations; a differentiation of the methodological competences of a preservice mathematics teacher is developed based on the criterion of the optimal environment for their formation (digital, traditional classroom, real pedagogical practice); pedagogical risks (substitution of live communication, devaluation of empathy) are identified, and a compensatory strategy of "augmented practice" is proposed. The theoretical significance of the article lies in substantiating the role of neural networks as an "intellectual opponent" and a "simulator of pedagogical situations," expanding scientific understanding of the possibilities for the digital transformation of methodological training. The practical significance is that the identified directions can be directly implemented into the educational process of pedagogical universities to modernize the content of methodological training and create educational and methodological support for a new generation.