Keyword: «personalized learning»

In the context of globalization and the digitalization of education, proficiency in English is a key skill for students in engineering fields. It provides not only access to up-to-date scientific and technical information but also enables successful professional communication. However, the use of authentic materials in the learning process, such as scientific articles, technical documentation, and professional podcasts, is associated with a number of challenges. The effectiveness of learning and student motivation can decrease due to the complexity of vocabulary, grammatical structures, and cultural contexts, which often exceed the capabilities of students with varying levels of language proficiency. The aim of this study is to explore and implement innovative methods for adapting authentic materials using artificial intelligence in teaching English at technical universities. The authors analyze the potential of modern technologies, including language complexity analysis, the use of generative neural networks to automate text simplification, create contextual exercises, personalize learning tasks, and visualize complex professional concepts. The study presents practical approaches to applying GPT-based tools that allow adapting educational texts to students’ language proficiency levels. The use of these technologies promotes individualized learning by considering the unique needs of each student, thereby increasing engagement and motivation to learn a foreign language. The results of an experiment conducted at the Russian University of Transport confirm the effectiveness of materials adapted with AI tools. Significant improvement in academic performance and communication skills was observed in groups where these technologies were applied. The study emphasizes that the integration of artificial intelligence into the educational process does not replace the role of the teacher but serves as a powerful auxiliary tool that optimizes the preparation of educational materials and supports the development of professionally oriented competences. The theoretical significance of this work lies in expanding the understanding of the capabilities of artificial intelligence tools in the linguodidactics of engineering disciplines, while the practical significance is found in the development and testing of innovative teaching methods that contribute to improving the quality of specialist training in the context of modern digital education.

The article addresses the current issue of exploring the comprehensive integration of digitalization into foreign language learning processes, in light of the implementation of the National Project "Education" and the Federal Project "Digital Educational Environment". It examines the prospects of using the Maximum digital textbook in teaching English at institutes within the Russian University of Transport (Moscow Institute of Railway Engineers). The Maximum textbook is an interactive platform comprising educational modules, multimedia content, and an automated task assessment system. The study focuses on evaluating the effectiveness of the Maximum digital textbook in teaching English to A1-A2 level students. To validate the hypothesis about its positive impact on motivation, communication skills, and digital literacy, the researchers analyzed learner-centered outcomes. The work combines theoretical analysis (a review of key trends in the use of digital textbooks in linguistic education) and experimental methods, including: project-based methodology applied to the "Communication" section; Likert-scale surveys, communication skills testing, and analysis of students’ reflective comments; quantitative evaluation based on criteria such as academic progress, level of autonomy, and quality of group discussions. The results confirm the hypothesis that the use of the digital textbook positively influences students’ motivation, independence, and ability to overcome communication barriers. Theoretical significance lies in validating the effectiveness of digital textbooks in educational practices. The findings can be practically applied to optimize linguistic education in technical universities. Future research is linked to further integration of artificial intelligence tools into the educational process.

The paper overviews practices of using AI-powered services by schoolteachers and faculty in their professional activities. The objective of the research is to analyze modern practices of using AI tools by Russian and foreign teachers of secondary and higher education and to identify the potential for scaling advanced solutions for Russian education system. To achieve this, the authors conducted systematic analysis of corresponding research papers, content analysis of regulatory documents, as well as comparative analysis of practical cases on the research issue. The analysis results indicate a trend of transition from the use of mono-functional AI services for solving separate educational tasks to the development and implementation of complex AI systems. The response for AI educational systems is aimed at ensuring personalization of learning based on learning analytics and teaching support (production of educational content, automation of assignment checking and feedback). Against this background, different countries observe asymmetric development of digital educational environment. The authors conclude that the reasons for the different intensity of AI tools use across countries are low level of AI literacy of teachers and their insufficient methodological support. Differences between Russian and foreign practices of implementing AI tools consists in different thematic focuses: the foreign context emphasis compliance with ethical standards, while the Russian context considers improving usability of AI–powered services. The theoretical significance of the research lies in the systematization and assessment of the didactic potential of AI tools. The practical significance is based on the fact that the research results allow the teaching community to discover the advantages and reflect on the challenges and risks when working with AI tools in classrooms.

Educational chat-bots are becoming important tools for supporting students’ independent learning. In case of teaching higher mathematics, the architecture of chat-bots becomes particularly significant: moving from rigid linear scenarios that ensure a strict sequence of topic study to more flexible navigational models. It allows students to independently build their own learning flows. However, fully free navigation is associated with the risk of fragmented and superficial assimilation of the material, disruption of the course logic, and loss of methodological integrity. This highlights the necessity of transitioning to a graph-based model that combines the structure of the course with the possibility of personalized choice and adaptation depending on the level of knowing the material. The aim of the study is to substantiate the methodological and technological feasibility of implementing a graph-based chat-bot architecture capable of taking into account semantic links between concepts, typical student's difficulties, and natural language queries. The theoretical part builds on contemporary research in the field of personalized higher mathematics education, the use of graph structures in pedagogy, and natural language processing technologies. The empirical study, conducted in 2024–2025 at the Yerevan branch of Plekhanov Russian University of Economics, included a comparison of the effectiveness of two types of educational chat-bots – those with sequential (linear) and free navigational organization. The collected data showed that chat-bots with free navigation flow were perceived as more convenient and structurally flexible, but they also came with the risk of insufficient learning of the material due to the lack of reliance on internal course logic. Based on the analysis of student feedback and theoretical premises, the necessity of introducing a flexible graph-based model has been substantiated. This model ensures not only variability in navigating the learning material but also contextual returns to key topics, adaptation of the pace and content of learning, and support for self-assessment processes. Such a model allows achieving a balance between freedom and logical coherence of the learning material, which is particularly important for disciplines with a hierarchical structure of concepts, such as mathematical analysis. The results of the proceedings can be used in designing digital solutions for teaching higher mathematics and in creating chat-bots aimed at personalizing the educational flow.

The article discusses the theoretical foundations and modern technologies of personalized reading instruction in foreign language education. It is shown that the existing solutions (adaptive platforms, intelligent simulators, recommendation systems) are mainly focused on training lexical and grammatical skills and rarely take into account the individual profile of reading activity. On this basis, an innovative model of personalized reading strategy in a foreign language is proposed, based on the compilation of a personalized selection of texts. The structure of the model, the selection formation algorithm, the mechanisms of text adaptation and the role of the teacher are described. Practical recommendations on the implementation of the model in the educational process in a foreign language lesson are formulated.