Keyword: «artificial intelligence in education»

The relevance of the problem under study is determined by the profound changes in the general education system caused by digital transformation. The current stage of societal development requires a revision of existing models of teacher training, as traditional digital literacy of teachers has ceased to meet the challenges of the times. The lack of systemic understanding of how exactly the teacher education system should be transformed makes this study timely and relevant. The aim of the article is to provide a theoretical justification and substantive disclosure of key areas for the development of new professional competences of preservice teachers in the context of the digitalization of education, as well as to analyze the changing role of the teacher in the modern educational ecosystem. The article is of a theoretical nature. The methodological basis of the study is formed by a systems activity-oriented approach, allowing us to consider teacher training as a holistic process of developing readiness for innovative activity, and a competence-based approach, ensuring the identification and structuring of key digital pedagogical competences. The study uses the method of theoretical analysis and synthesis of scientific literature on the issues of digitalization of education, as well as the method of systematization and classification of current empirical research data (for 2025-2026). This study explores the evolution of the "teacher digital competence" concept from a technical level to digital pedagogical competence, encompassing methodological, research, and creative components. Four priority areas for training preservice teachers are identified: mastering artificial intelligence technologies, working with big data to personalize learning, designing individual educational trajectories, and creating interactive educational content. The transformation of the professional role of the teacher is substantiated, now acting not so much as a transmitter of knowledge, but as a facilitator, architect of the digital educational environment and curator of the digital culture of students. The theoretical significance of this study lies in clarification and systematization of the conceptual and categorical framework related to digital teacher training, as well as in the development of a conceptual model for the transition from digital literacy to digital pedagogical competence. The practical significance lies in the fact that the research tenets and conclusions presented in the article can be used in the design and modernization of key professional educational programs in pedagogical training areas.

This article presents an experimental study of the influence of a specially designed didactic prompt on the level of students' learning proficiency in the course "Additional Chapters of Mathematical Analysis". The prompt is considered a tool for pedagogical support of independent learning activities, providing a step-by-step organization of problem-solving, the actualization of theoretical principles, and the development of self-assessment skills. The relevance of the research problem is determined by the need to develop new tools for pedagogical support of students' independent work in the context of the active integration of artificial intelligence technologies into the educational process, especially when studying complex mathematical disciplines that require developed abstract thinking and stable learning skills. The aim of the article is to experimentally evaluate the influence of a specially designed didactic prompt on the level of students' learning proficiency in the course "Additional Chapters of Mathematical Analysis". The leading research method was a pedagogical experiment with the formation of control and experimental groups and the application of repeated measurements of learning outcomes. Statistical data analysis was performed using parametric (Student's t-test) and non-parametric (Wilcoxon test) methods, as well as effect size calculation. The obtained results indicate that the use of the didactic prompt leads to a more pronounced increase in the learning proficiency levels of students in the experimental group and to the growing number of students with higher levels of mastering the educational material. This allows us to consider didactic prompts as an effective tool for developing sustainable learning skills in the context of integrating artificial intelligence technologies into the teaching of mathematical disciplines. The theoretical significance of the work lies in developing ideas about the didactic potential of prompt engineering in relation to teaching mathematics and substantiating the possibilities of using structured text instructions as a tool for pedagogical support of students' independent work. The practical significance of the study is determined by the possibility of applying the methodology of prompt-oriented preparation in teaching mathematical cycle disciplines that require intensive independent work of students. The proposed approach does not require complex technical solutions and can be easily scaled within various educational programs.

The article is devoted to the problem of the transformation of the role of generative neural networks in mathematics lessons: from a tool of hidden cheating to a didactic partner. Based on an empirical study (76 students in grades 6-10, Glazov, MBOU "Secondary School №. 3"), it is shown that the vast majority of schoolchildren already use neural networks to complete tasks. However, they do not blindly copy the answers, but check or redo them. A clear age trend has been revealed: among sixth graders, the proportion of "cheating" students exceeds the proportion of those who study with the help of AI, while by the 10th grade the situation is reversed. A methodological system of four strategies has been proposed and tested: "Critical reviewer", "Noisy task", "AI tutor" and "Methodical duo". It has been experimentally proven that the implementation of this system makes it possible to significantly increase the proportion of students who constructively use the neural network, compared with the control group. The methodological transition from prohibitive tactics to a partnership strategy is substantiated. The results can be directly used by math teachers at school.

The article analyzes the psychological consequences of the active introduction of generative neural networks (ChatGPT, DeepSeek, Alice AI) into the process of teaching English in secondary schools. The authors introduce and substantiate the working construct of “cognitive dependence”, describing the pathological delegation by a student of cognitive operations (translation, generation of grammatical structures, spell-checking) to an external algorithm. Changes in the structure of educational motivation are examined: the displacement of intrinsic interest by extrinsic reinforcement (points, levels) and a decrease in frustration tolerance. Based on theoretical analysis, three key symptom complexes are identified: the illusion of competence, atrophy of language intuition, and speech anxiety in “live” dialogue. Criteria for distinguishing between adaptive use of AI and clinically significant forms of dependence are proposed.