Keyword: «generative neural networks»

Modern continuing professional education (CPE) is undergoing active transformation under the influence of digitalization and the introduction of artificial intelligence technologies. The study of the capabilities of generative neural networks, such as ChatGPT, is of particular relevance to increase the flexibility, adaptability and personalization of the educational process. At the same time, existing studies mainly focus on higher or general education, while the specifics of CPE remain insufficiently studied. This necessitates scientific understanding of the vectors of integrating generative AI technologies into the CPE system aimed at adult learners with diverse professional contexts. The aim of this study is to identify and scientifically substantiate the key vectors of generative neural networks integration into the CPE system using ChatGPT as an example. The study is based on a combination of methodological approaches - systemic, competence-based and personality-oriented, as well as empirical data obtained from the analysis of educational practices and in-depth interviews with experts involved in the development and implementation of CPE programs. The leading research method used was qualitative analysis: content analysis, SWOT analysis and modeling. As a result of the study, five key vectors of ChatGPT integration into the practice of continuing professional education were identified: personalization and adaptive learning, automation of assessment and feedback, support for research and project-based activities, expanding the accessibility and inclusiveness of education, as well as professional development of teachers. Based on these areas, a systemic model of ChatGPT integration was proposed, including target, content, technological, organizational and pedagogical, assessment and regulatory components. The model also reflects the levels of teachers’ digital maturity in accordance with the UNESCO AI matrix (2024). The theoretical significance of the article lies in clarifying the pedagogical framework for the use of generative AI in the continuing professional education system and conceptualizing the model of its integration. The practical significance is associated with the possibility of using the developed recommendations for strategic planning, methodological support and regulation of the processes of introducing AI into the educational practice of CPE institutions.

The study of the effectiveness of the integrated application of modern intelligent information technologies in higher professional mathematical education is an urgent issue due to the emergence of digital assistants and generative neural networks with extensive didactic capabilities. The aim of the study is to analyze and evaluate the effectiveness of the digital teaching assistant model in the professional training of undergraduates in mathematics. The research methodology is based on systematic and technological approaches. Based on a systematic approach, the teacher's functions requiring automation (assignments verification, adaptive learning) have been identified, and intelligent information technologies with the potential to automate these functions have been identified based on a technological approach. Content analysis was used to compare student decisions with those of artificial intelligence. Empirical and statistical methods (experiment, observation, survey, chi-square criterion, clustering) were used during the research and to analyze its results. As a result of the study, the effectiveness of introducing a digital teaching assistant into the educational process at the Faculty of Computer Technology and Applied Mathematics was determined: the average score of the experimental group in academic performance increased from 3.48 to 4.0. It was found out that intelligent information technologies are most effective for differentiating tasks, visualizing complex concepts (algorithms, graphs). Based on experimental work, the effectiveness of using specific intelligent information technologies in teaching has been proven. The theoretical significance of the research lies in the fact that it contributes to the theory of digital didactics by systematizing approaches to automating teacher functions using intelligent information technologies. The practical significance lies in the fact that the developed digital teaching assistant model provides teachers with specific ways to automate routine functions, which frees up time for creative and individual work with students. The use of components of the digital teaching assistant model provides the teacher with modern digital tools both for designing learning materials and for organizing classes.

The relevance of the research is due to the active introduction of neural network technologies into educational practice and the need for their pedagogically sound use in teaching foreign languages. The proliferation of generative text and visual models expands the possibilities of creating electronic learning materials, but at the same time exacerbates the problem of teachers' methodological readiness for their conscious and purposeful use. In the context of the digital transformation of education, teachers are increasingly acting not only as users of digital tools, but also as designers of educational content, which requires systematic training and methodological support. The aim of the research is to develop and theoretically substantiate a model for training teachers to use neural network technologies in the design of linguistic e-learning materials in English. The research is theoretical in nature and it comprises competence-based, linguodidactic and systematic approaches, as well as modern concepts of the use of artificial intelligence in education and the development of digital and methodological competences of teachers. The research resulted in a structural model of teacher training including target, content, procedural and resultant components. The model focuses on the development of teachers' methodological readiness to use generative textual and visual neural networks in accordance with the goals of teaching English, types of speech activity and the level of students’ linguistic training. The article also presents a hypothetical scenario for the implementation of the model in the system of advanced teachers’ training, demonstrating a reproducible method of professional training without necessary experimental testing. The theoretical significance of the research lies in clarifying approaches to the development of teachers' methodological readiness for the use of neural network technologies in teaching foreign languages and in systematizing the teacher's activities in the context of the use of generative models. The practical significance of the work is determined by the potential of using the proposed model in the system of teacher education and advanced training of English language teachers, as well as in the development of methodological training programs in the context of the digitalization of the educational process.