Artificial Intelligence in Teaching Foreign Languages: Application Trends in Higher Education

In modern pedagogy of higher education, there is an active introduction of artificial intelligence into the educational process. Individual instructors, faculties, and entire universities are developing various approaches to its application, ranging from isolated exercises to full courses based on neural networks. Nevertheless, many questions remain controversial and unresolved. With the widespread use of neural networks and mobile applications, challenges arise in leveraging them to achieve pedagogical outcomes, particularly due to insufficient research on their impact on the quality of language training. During this phase of active adoption of new tools, it is essential to synthesize global experiences in their application. The purpose of this study is to analyze and classify current trends in the use of artificial intelligence in foreign language teaching in higher education. Examining both Russian and international experiences in combining technology with traditional teaching methods provides a comprehensive view of modern approaches in the field of teaching foreign languages. It is necessary to identify the advantages, risks, and promising directions of artificial intelligence implementation. Systematizing the use of new technologies and integrating them into the structured framework of higher education will help unlock their potentials while considering theoretical and linguodidactics aspects. When introducing new tools into teaching, it is necessary to consider prospective and existing challenges to prevent undesirable consequences in the advancement of new directions in the field of teaching. The research materials include publications on studies and practical applications of artificial intelligence in higher education pedagogy of foreign languages teaching in non-linguistic universities. A key feature of these materials is their international, multinational, and intercultural context. The selected articles were chosen based on thematic relevance, regardless of where the research was conducted. The classification of accumulated experience from the perspective of neural network application approaches is of particular interest. The research methods involve the selection, study, and systematic analysis of scientific publications and empirical studies on the specified topic, identified in the research materials. A comparative assessment of neural network tools and the generalization of pedagogical experiences in integrating them into curricula help to systematize accumulated knowledge and formulate development strategies. The study results in a classification of current trends in the linguodidactics of foreign language education, as well as a systematization of methods for optimizing learning through automated assessment, personalized tasks, and the development of communicative skills using neural networks and chatbots. In addition to classifying effective artificial intelligence applications, the study outlines negative aspects and influences of neural networks that require data accuracy control, plagiarism prevention, and the preservation of the lecturer’s role as a key participant in the educational process. Conclusion. Thus, the use of neural networks is a widely explored field in modern higher education with a diverse system of new approaches and methods. Hybrid learning models that combine traditional methods with existing educational technologies are recognized as promising directions. Special attention is given to the integration of artificial intelligence with virtual reality capabilities, despite the current labor-intensive and cumbersome nature of this technology.