Vol 24, No 1 (2026): ARTIFICIAL INTELLIGENCE IN SCIENTIFIC RESEARCH AND TEACHING THE RUSSIAN LANGUAGE

Modern linguodidactics is being fundamentally transformed because of the intensive use of artificial intelligence (AI) in education. This requires a comprehensive analysis of the current state and development trends in this field. The study is aimed at identifying the evolutionary directions and thematic structure of scientific discourse on AI in teaching Russian as a foreign language in 2022-2025. The research is based on publications from open resources, CyberLeninka and eLibrary. The methods of bibliometric and content analysis, frequency and terminological analysis based on TF-IDF, thematic modeling, terminological diversity assessment with the Shannon index, network analysis with the Louvain algorithm, and ForceAtlas2 visualization are applied. A clear evolutionary direction in discourse development has been identified, from describing tools and stating their capabilities (2022) through the integration of AI into learning tasks and scenarios (2023) and methodological optimization (2024) to focusing on motivation, learning personalization and critical analysis of socio-pedagogical consequences (2025). Network visualization of authors’ connections revealed a segmented structure with a core-peripheral organization and four key topics: the implementation of AI services, speech skills development, digital transformation of linguodidactics, and the methodological normalization of AI usage. Further development of the field is the transition from issues of technical capability to the studying productive ways to use AI tools in teaching Russian as a foreign language, adaptive learning systems, methods for assessing digital literacy and forming new types of language competence, including the ability to critically evaluate AI-generated content.

The authors perform the linguistic analysis of neural network modeling of the semantic field “Internet” on the material of available online Russian-language content. The relevance of the study is ensured by the quality and quantity of the linguistic material in the “big data” format and by an innovative methodological approach to its meta-description with neural network instruments. The study is aimed at giving a linguistic characteristic of neural network modeling of the semantic field “Internet” in Russian-language discourse. The material was Russian-language Internet content. The volume of the content had not been limited to obtain statistically representative metadata. This approach corresponds to the mainly declarative limitations of the Internet discourse functionality. Due to the focus on the “intelligent” algorithms for processing Internet content, such as basic for our research OpenAI project, the high referentiality of language data was ensured. The authors used a wide range of methods, from component analysis to discourse analysis, with modern neural network instruments. A two-dimensional neural network modeling was carried out with cluster and stratum analysis of language units associated with the conceptual phenomenon Internet. The conducted research demonstrated the potential of neural network modeling techniques to study the semantic field “Internet”. The modeling identified and verified a wide range of language units whose speech functionality was associated with the conceptual phenomenon Internet as the core of the corresponding semantic field. The results obtained are promising; we can confidently implement the neural network modeling patterns tested in this study into linguistic practice. This, in turn, will develop the paradigm of linguistics, modernize methodological approaches to language functioning, and identify and qualify speech innovations.

The virtual turn in linguistics after the inclusion of artificial intelligence (AI) as a fundamentally new type of actor in human communication forms new directions in the study of language. The relevance and novelty of the research are manifested in formulating and predicting possible ways of modern communication development with the significant role of AI. The aim of the study is to show that the distinction between humanitarian and neuro stylistics is the category of emotionality inherent in humanitarian stylistics. With the help of general scientific methods of observation and generalisation, author’s scientific reflection, meta-analysis of secondary data and intuitive-logical prediction, the role of the category of emotionality in human and neural network communication was shown. The material for analysis was the answers of the Russian neural network Yandex GPT to the prompts given by the author of the study and the results of the two-factor linguistic experiment conducted with students (stylistic experiment) and with artificial intelligence (generative experiment). We revealed that AI can imitate, name, and analyse emotions, but it does not feel them itself. AI cannot feel complex simultaneous emotions, it algorithmically decomposes them into separate feelings and thus destroys human feelings. We proved that emotionality, which plays a crucial role in human communication, is reduced in the structure of AI linguistic personality. The study predicts the further development of modern communication, which will increasingly involve AI, and raises the question of human control over such communication.

The integration of artificial intelligence (AI) into the process of teaching Russian as a foreign language (RFL) created new additional functions for teachers. These are associated with the need to competently structure the educational process within the new paradigm, “teacher - artificial intelligence - student” triad. In this regard, methodological AI competence of RFL teachers is becoming particularly relevant. The aim of the study is to develop a course program for building AI competence among RFL teachers and to test its effectiveness through experimental training. The research employed the following methods: analysis of scientific literature, experimental training, modeling of the educational process using AI tools, generalization of experience, and observation. The materials for the analysis included scientific articles, lesson fragments and assignments developed with AI technologies by the participants of the experimental online course. As a result, the following key aspects of the methodological AI competence for RFL teachers were identified: a) prompt engineering, b) teaching and assessment, and c) organization of the educational process. The results of the empirical study revealed varying levels of mastery and relevance of various course topics among students. The modules on prompt engineering and the development and assessment of pronunciation skills using AI posed the greatest challenges. At the same time, students successfully and easily mastered topics related to planning and developing AI-based teaching materials for Russian as a Foreign Language (RFL), developing and assessing lexical and grammatical skills, developing oral and written communication skills (both dialogic and monologue) using AI, conducting AI-based research, and constructing the educational process within the “teacher - artificial intelligence - student” triad. A promising direction for this study is the creation of models for the systematic implementation of AI tools in the teaching of Russian as a foreign language.

In the era of digital transformation, the integration of artificial intelligence (AI) technologies into the educational process, including foreign language teaching, is particularly relevant. Modern AI tools open new opportunities for creating adapted learning materials, that promote individualized learning and increase its effectiveness. The aim of this study is to identify the methodological potential of the AI platform Suno in developing Russian listening skills among Vietnamese students at the beginner level. As empirical materials, the authors used songs generated with the help of the “singing AI” Suno. The main research methods include pedagogical observation at a series of practical Russian language classes where AI-generated songs (AI-songs) were used, a survey, subsequent analysis, and synthesis of the collected data. The study shows that AI-songs stimulate learners’ motivation to study Russian, improve the perception of spoken language, and enhances interest in the culture of the target language country. The survey results confirmed the methodological potential of the Suno AI platform in Russian language teaching. The authors conclude that “singing” AI technologies such as Suno can become an effective tool to support the development of learners’ listening skills provided that these technologies are applied purposefully, not chaotically, and are pedagogically grounded. Suno’s “singing AI” creates an interactive, emotionally rich language environment where listening skills are developed with increased motivation, learner autonomy, and creative engagement in learning Russian.

The relevance of the study is determined by the need to develop new methods and approaches in a relatively young field of axiological linguistics, and to understand the term ‘dominant cultural value’, which has been recently introduced into scientific use by Professor V.I. Karasik. The moral and ethical guidelines of Russian society, reflected in the value attributes of the concept “God,” shape the national-ethnic consciousness and self-determination of Russian language speakers. The aim of the study is to reconstruct the model of the concept “God,” reflecting the views of Russian speakers on faith in God as one of the most important social values and to analyze the attitudes and behavioral stereotypes of Russian people embedded in the value attributes of this concept. The material used includes explanatory and etymological dictionaries of the Russian language, Russian National Corpus, 54 proverbs and sayings with the component God selected with a comprehensive sampling method from paremiological dictionaries, and the lyrics of contemporary Russian-speaking singers. The aim was achieved with the help of descriptive, linguistic-cultural, and linguistic-axiological methods. The ambivalent features of the concept “God” are revealed; on the one hand, God represents the supreme supernatural power, and on the other, he is a person possessing authority. The etymon of the concept verbalizer, or the name of the concept is an Old Indian root meaning ‘lord, bestower of wealth’. This is reflected both in the conceptual and figurative features of the concept under study; God represents the source of light, the highest degree of goodness, and a force that governs human life and predetermines human destiny. The analysis of proverbs and sayings with the component God, and Russian-language lyrics identified the value content of the concept “God,” which determines the behavioral attitudes of Russian speakers. A promising direction for research is the reconstruction of the concept “God” in other linguistic cultures compared with the Russian linguistic culture.