Keyword: «языковое образование»

An innovative learning environment (ILE) is an environment that is able to develop and get adjusted as and when there is an update on new and already known educational practices, thus being aimed at the future perspective. Access to quality language education is an indispensable condition for successful implementation for graduates in the future. Collaboration in the ILE offers all participants of the educational process flexibility, freedom of action, iniquitousness and a possible ongoing improvement in all types of language and speech activities. Our paper considers IOS within the learner-centered paradigm. The goals and objectives of the ILE have been defined, effective educational platforms allowing creating modern educational content and conducting classes at a qualitatively new level have been identified. The definition of the term ILE has also been expanded. In addition, we have conducted a study on estimating theoretical understanding of the ILE among foreign language teachers in an engineering university and the possibility of its creation. The purpose of the study was to validate the hypothesis that teachers do not have or have little idea of the possibilities of ILE for the qualitative mastery of students' language competencies has not been confirmed. However, a problem has been identified – insufficient attention is paid to the spatial organization of the ILE which is one of the main conditions for its implementation.

The relevance of this study stems from the need to integrate emotional intelligence into language education in the context of global digitalization. Modern requirements for linguists' training involve not only the development of language competences but also the cultivation of emotional intelligence as a key component of professional competence in the context of digital transformation in education. The study aims to develop, theoretically substantiate, and experimentally verify the effectiveness of a modular educational model designed to integrate the development of language competences and emotional intelligence of linguistics students in a digital educational environment. The research methodology is based on systematic and competency-based approaches, allowing us to consider social-emotional learning as a holistic process of developing professional and personal qualities. The study employed theoretical methods (analysis of scientific literature, modeling of the educational process) and empirical methods (pedagogical experiment, testing, surveys, expert evaluation). Five interconnected educational modules were developed and experimentally tested: "Global Emotions," "Human Stories," "Digital Humanity," "Emotional Mentorship," and "Community Service in English." Each module includes a system of exercises aimed at simultaneously developing language skills and emotional intelligence. The experimental verification of the model's effectiveness was conducted over one semester with 60 linguistics students. The experimental results demonstrated statistically significant differences between groups: participants in the experimental group showed substantial improvement in emotional intelligence indicators (17.9% increase) and language competences (24.9% increase) compared to the control group (5.0% and 10.1%, respectively). Qualitative analysis of the results confirmed improved emotional expression skills in written communication and enhanced intercultural empathy. The theoretical significance of the research lies in substantiating the relationship between emotional intelligence and language competences, as well as developing a conceptual model for their integrated development. The practical significance consists in creating and validating specific educational modules ready for implementation in higher linguistic education.

The relevance of researching the effectiveness of artificial intelligence (AI) technologies in foreign language learning for students of language majors is determined by the growing and intensifying integration of AI into the educational process, as well as the insufficient development of methodological foundations for its use. The aim of the study is to identify promising scenarios for integrating generative AI into the training of language-major students, considering the specifics of their education and the possible limitations of the technology. The research is based on methods of analyzing scientific literature, systematization, and data synthesis to develop recommendations for the application of AI in the education of language-major students. The study includes a review of modern approaches to the technologization of education, an analysis of digitalization trends, and the integration of AI in the training of Linguistics majors. Key opportunities and limitations of generative AI applications have been identified, including the automation of routine learning tasks, personalization, support for multilingual education, analysis of linguistic material, and its generation for educational purposes. Special attention is given to risks associated with AI dependence and misuse, particularly by students. The risks of potential errors in generated content and challenges in monitoring students' independent work using AI tools are also noted. Furthermore, a literature review revealed that students’ engagement with AI does not always have a positive impact on their readiness to apply acquired knowledge in practice. These challenges collectively necessitate a cautious and controlled implementation of AI in education. Recommendations have been formulated for the use of AI in training language-major students, aimed at balancing digital technologies with traditional teaching methods. The theoretical significance of the study lies in the systematization of approaches to AI application in language education and the assessment of its impact on the training of language-major students, considering the specifics of such preparation. The practical significance of the study consists in the development of methodological recommendations for educators, focused on the productive use of AI in the learning process while maintaining a high level of student training quality.