Keyword: «artificial intelligence in education»

This article discusses the possibility of using artificial intelligence to train future specialist teachers of preschool children in a multilingual educational environment in foreign language classes. Integration of technical means into the educational process based on artificial intelligence is possible both for students and teachers. Innovative applications serve as auxiliary tools in the educational environment, contributing not only to increasing the effectiveness of learning, but also to optimizing pedagogical processes. This article is devoted to the practical application of AI in teaching English at the preschool department. The use of AI can facilitate the professional activity of a foreign language teacher, reducing the time for preparing for classes, developing and checking assignments, and also helps to increase the educational and professional motivation of students and develop interest in their future profession.

This article describes the development of the "SmartTeach" educational web platform, designed to address key issues in education. The platform utilizes technologies such as Next.js, Tailwind, Nest.js, ReactFlow, and TypeScript to create flexible educational courses and implement a grading system and student support based on artificial intelligence. Particular attention is paid to mechanisms for individualizing learning through "Learning Trajectory v2.0", the "BLu" rating system, and the smart feature "AI Help!". The article analyzes the platform's potential to enhance interaction among students, teachers, and parents.

The article is devoted to the analysis of the ethical and pedagogical challenges that arise when integrating neural network technologies into the educational process. The key problems are considered: a decrease in the level of critical thinking, the role of opaque algorithms in the formation of competencies, the place of digital inequality in access to neural network tools, difficulties in maintaining academic honesty. As their problems, recommendations are offered for identifying cheating, training in the use of neural network resources and the formation of a culture of responsible use of neural networks. The results are relevant for general teachers, subject teachers.

The relevance of the study is driven by the intensive digital transformation of the education system, which reveals a systemic shortage of teaching staff capable of not only technically mastering but also methodically integrating artificial intelligence (AI) technologies into their professional practice. This problem is confirmed by the results of a diagnostic experiment, which found that 89.4% of preservice teachers have an introductory and initial level of AI competence. The aim of the research was to develop and theoretically substantiate a specialized laboratory practicum project designed to develop a holistic digital pedagogical competence in students – a complex quality combining technological skills, methodological flexibility, a reflective stance, and readiness for innovation. The methodological foundation comprised competency-based and systemic approaches, implemented through an original two-tier assignment model. The instrumental level focused on mastering basic AI tools (generative neural networks, speech recognition and synthesis platforms, dialog systems and chatbots), while the competency level focused on the pedagogical design and testing of AI solutions in simulated learning situations. The substantive content of the practicum is presented through four interconnected modules: fundamentals of prompt engineering, development of electronic educational resources using AI, application of speech technologies (ASR/TTS) for creating an inclusive environment, and design of dialog systems (chatbots) for organizing feedback and self-assessment. The main result of the study is the developed laboratory practicum project, which ensures a gradual transition from the acquisition of technical skills to the development of the capacity for pedagogical design. A key feature of the model is the mandatory reflection on each technical action, encompassing the justification of its didactic appropriateness, analysis of potential ethical risks, and design of specific scenarios for using AI in real school practice. The theoretical significance of the work lies in the development and scientific substantiation of an integrated competency model that synthesizes the technical and pedagogical aspects of preservice teacher training, thereby contributing to the advancement of pedagogical education methodology in the context of digitalization. The practical significance lies in the fact that the implementation of this practicum in the university's educational process allows students to purposefully develop not only confident digital skills but also critical soft skills: ethical reflection, methodological flexibility, and sustained readiness for innovative professional activity.