Keyword: «natural language processing»

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.