Development of the Russian as a foreign language teachers’ methodological competence in the artificial intelligence era

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Introduction

Аrtificial intelligence (AI) is gradually but steadily penetrating various areas of the education system and are actively used: a) by specialists in education management to automate certain routine processes and in analytical work (Selwyn, Hillman, Bergviken-Rensfeldt, 2023a; Selwyn et al., 2023b; Siddiqui, 2024); b) by teachers in preparing for classes and organizing the educational process (Bogomolov, Dunaeva, 2023; Sysoyev, 2023; Dziuba, 2024); c) by students when doing homework and participating in extracurricular interaction with AI tools to solve educational tasks (Kozhevnikova, 2022; Baichorova, 2025). Over the past few years, a group of works appeared in the scientific literature; their authors reveal the linguistic and didactic potential of generative AI tools in foreign language teaching in general and in teaching Russian as a foreign language in particular. The scientists studied such issues as teaching pronunciation with AI-based web applications (Sysoyev, Ivchenko, 2025), teaching lexical and grammatical material through foreign language interaction with chatbots and using AI corpus technologies (Grudeva, Alexeeva, 2020; Dziuba, 2024; Sysoyev, Filatov, 2024), the development of students’ oral speech skills through speaking with voice assistants (Al-Kaisi, Arkhangel’skaya, Rudenko-Morgun, 2019; Nefedov, 2023; Liu, 2023), the development of written skills in dialogical and monological speech in foreign language interaction with AI tools (Çakmak, 2022; Sharadgah, Sa’di, 2022; Guo, Wang, 2023; Huang et al., 2023). Many of the above works contain empirical data demonstrating the effectiveness of AI tools in language teaching, as well as their obvious limitations.

Scientific papers show that a wide range of AI-based technological solutions is being used in education, from universal generative neural networks to specialized AI tools designed to solve specific professional tasks; the latter reflect the characteristics of the professional activities in a particular scientific field. Proposing innovative teaching methods, scientists emphasize that AI technologies can be integrated into the traditional process of teaching Russian as a foreign language, not replacing the teacher, but expanding the range of additional opportunities for students’ extracurricular language practice. The feedback that students receive from AI must be subject to reflection and critical analysis.

Thus, AI-based technological solutions in the educational process have objective advantages.

Firstly, AI can take on some organizational and methodological tasks (Klobukova, Mayorov, Kochetkova, 2025; Baichorova, 2025): developing exercises for forming language skills, communication tasks for the comprehensive development of students’ speech, and tests, adapting educational texts, or drawing up lesson plans (or parts of lessons). Teachers can use the time saved to tackle other equally important and meaningful tasks.

Secondly, AI creates additional opportunities for students to continue developing their language and speech skills and studying Russian history and culture outside of class. This linguistic and pedagogical potential of AI is particularly relevant in teaching Russian as a foreign language abroad, when students are outside an authentic language environment and do not have the opportunity to communicate with native Russian speakers.

Thirdly, students simultaneously develop their academic autonomy skills while interacting with AI tools to solve educational tasks. When traditional approaches to education change due to developed AI technologies, these abilities to autonomous learning will prepare students for personalized AI-based learning (Sysoyev, 2025).

At the same time, despite the obvious advantages of integrating AI into education in general and teaching Russian as a foreign language in particular, new problems arise that the education system has not faced or has not faced to such an extent. These problems are as follows:

1. the widespread use of AI plagiarism in academic sphere with students claiming authorship of AI-generated materials (Cotton, Cotton, Shipway, 2023; Sysoyev, 2024). Many students use generative AI without permission to complete assignments, tests, and exams, write essays, project works, and other works that they are supposed to do themselves.
2. AI hallucinations. When there is a lack of necessary information due to limited access to databases, AI invents it. When AI generates texts with sociocultural content to tell foreign students about Russian culture, AI hallucinations can provide them with false factual data, and when students do their research work, to falsification of results and forgery.
3. AI bias in the information provided. Generative AI tools are based on large language models (LLM). For example, the American neural network ChatGPT from OpenAI operates on the basis of an English-language LLM; the Russian neural networks GigaChat from Sber and YandexGPT from Yandex operate on the basis of a Russian-language LLM. The content of text corpuses processed by LLMs is characterized by a certain ideological bias, a specific interpretation of socio-cultural and historical facts and events in a certain community, this bias will be transferred to AI-generated material. For instance, here are different responses of neural networks operating on different LLMs to a query about the inventor of the electric light bulb: ChatGPT says it was Thomas Edison; GigaChat — Alexander Lodygin. There are many similar examples. Students’ lack of factual knowledge and inability to critically perceive feedback from AI can form incorrect and distorted ideas about Russian history, culture, and society.

The above-mentioned and many other problems of AI integration into education require prompt solutions from the teacher. By delegating some of their traditional functions to AI, modern teachers get additional functions because of the need to competently structure the educational process in the new paradigm of the “teacher-artificial intelligence-student” triad. In this regard, it is particularly important to consider the issue of developing the methodological competence of teachers of Russian as a foreign language in the field of AI.

The aim of the study is to develop a course program for the formation of methodological competence of teachers of Russian as a foreign language in the field of AI and to test its effectiveness in experimental training.

Achieving this goal involved solving the following tasks:

• to determine the structure and content of the methodological competence of teachers of Russian as a foreign language in the field of AI;
• to develop a professional development course program for teachers with the aim of developing their methodological competence in the field of AI;
• to train teachers according to the course program, analyze, and interpret the data obtained.

Methods and materials

The study used 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 analysis were scientific articles, AI-based lesson fragments and assignments developed by participants during the experimental online course. The study included experimental training aimed at developing the methodological competence of teachers of Russian as a foreign language in the field of AI. It was an online professional development course on the Moodle platform of Derzhavin Tambov State University. The participants were teachers of Russian as a foreign language (N=48) from Russian universities. The students took part in the experimental training on a voluntary basis at the invitation of the Scientific Center of the Russian Academy of Education at Tambov State University named after G.R. Derzhavin. The course (72 academic hours) included nine topics, which were grouped into three thematic blocks (Sysoyev, 2025), reflecting aspects of the methodological competence of teachers of Russian as a foreign language in the field of AI: 1) prompt engineering, 2) teaching and assessment, 3) organization of the educational process. The subject-specific content of the course did not include two aspects of competence related to a) the motivation of teachers to use AI in teaching Russian as a foreign language and b) the further AI-based professional development of teachers. The reasons are as follows: (1) teachers participated in experimental training on a voluntary basis and were already motivated to master the methodology of teaching Russian as a foreign language based on AI; (2) professional development is considered in a longitudinal aspect, and its changes cannot be measured within the framework of a short-term professional development course.

The study included three stages. At the initial stage, course participants were asked to complete a test with nine creative tasks, each corresponding to one of the course topics. Teachers were asked to develop a methodology for teaching written monologue skills based on the GigaChat neural network, to develop teaching materials using AI for a lesson fragment for learning new vocabulary, etc.

At the formative stage of the experiment, teachers took a six-week professional development course. Each topic contained video lectures by the course instructor. Teachers listened to the lectures and completed practical assignments to master aspects of methodological competence in the use of AI and develop methods for teaching language aspects and types of speech activity based on AI technological solutions.

At the control stage of the experiment, the teachers took the same test with creative pedagogical and methodological tasks that they had taken at the ascertaining stage.

Each test task was assessed separately according to a five-point scale. Student’s t-test was used as the method of statistical data processing. Mathematical calculations were performed using IBM SPSS Statistics 21 software.

Results

The study identified key aspects of methodological competence in teaching Russian as a foreign language in the field of AI: a) prompt engineering, b) teaching and assessment, c) organization of the educational process. The proposed aspects were the basis for developing three thematic content blocks of the course on formation of methodological competence in the field of AI.

The “Prompt Engineering” block is aimed at helping teachers master the rules for composing queries (prompts) for LLMs to obtain the necessary and most accurate feedback, develop the skills to correctly and accurately formulate prompts, and teach the participants the basics of prompt engineering.

In the “Training and Control” block, teachers learn how to use AI tools to create lesson plans and develop training (exercises, communication tasks, and case studies) and assessment materials for the development of phonetic, lexical, and grammatical speech skills, oral and written dialogical and monological speech skills, and students’ research skills.

The “Organization of the Educational Process” block is focused on problem-based learning and step-by-step methods for building language skills and developing students’ speech abilities by integrating AI-based technological solutions into extracurricular practice in Russian as a foreign language.

Statistical data processing showed that a significant increase of p < 0.05 was observed in all aspects monitored during the experimental training. This means that the course participants were able to develop methodol ogical competence in the field of teaching Russian as a foreign language based on AI technologies in all the proposed aspects. At the same time, various average values x in test questions suggest that the aspects are not equally important for teachers. It depends on the students with whom teachers work, their level of proficiency in Russian as a foreign language, the focus of the training program, and the linguistic and pedagogical potential of available AI-based technological solutions.

The most difficult topics were prompt engineering and AI-based formation and control of pronunciation skills. Other topics did not cause any particular difficulties for the participants: planning and developing AI-based teaching materials for Russian as a foreign language, forming and controlling lexical and grammatical skills, development of oral and written dialogical and written monological speech skills based on communication with AI, conducting research based on AI, and organization of the educational process in the triad “teacher – artificial intelligence – student.”

Discussion

The results of the experimental training on the formation of methodological competence in the field of AI among teachers of Russian as a foreign language identify the following important aspects.

AI use in Russian as a foreign language teaching. The results of the experimental training show that many teachers of Russian as a foreign language have already developed AI competence in certain aspects and at a certain level. To a greater extent, this is directly related to the use of specific AI-based technological solutions in the educational process. At the same time, the relatively low average values for AI in student research work (x = 3.12) and prompt engineering (x = 3.39) indicate that not all aspects of AI competence are equally in demand among teachers and may be difficult to master. Let us consider in more detail the formation of each aspect of teachers’ methodological competence in the field of AI.

Prompt engineering. The study showed that prompt engineering had been causing the greatest difficulties for teachers (ascertaining section: x = 3.39; control section: x = 4.27). In teaching Russian as a foreign language, generative AI tools can provide users with different types of feedback: educational and social (for mastering Russian as a foreign language in oral or written interaction with a virtual interlocutor) (Sharadgah, Sa’di, 2022; Sorokin, 2024; Al-Kaisi, Arkhangel’skaya, Rudenko-Morgun, 2019), informational and referential (texts for developing speech skills and forming sociocultural competence), methodological (for the development of teaching materials by the teacher) (Klobukova, Mayorov, Kochetkova, 2025; Baichorova, 2025), analytical (for analyzing text corpora in research work) (Grudeva, Alexeeva, 2020; Sysoyev, Filatov, 2024), evaluative (for automated control of language and speech skills development) (Elnikova, 2020; Dziuba, 2024), conditionally creative (for students’ language practice) (Sysoyev, Filatov, Sorokin, 2024). At the same time, the quality of AI-generated materials depends on the accuracy of the request (prompt) to the AI. In this regard, teachers must master the art of prompt engineering themselves and teach students to formulate requests to AI competently and correctly. Figures 1–3 show examples of the development of scientific materials using AI tools.

The accuracy and completeness of the prompt determine the feedback from the AI. As shown in Figures 1–3, when developing teaching materials, the teacher needs to specify the level of Russian language proficiency, what exercises or tasks are to be created, how many examples to provide, etc. If necessary, the teacher can clarify and repeat their request. In addition, all feedback materials from AI should be critically analyzed and corrected if necessary.

Figure 1. Creation of an educational text on the topic “New Year in Russia” by the Twee neural network using specific active vocabulary, a specified volume, and targeting students with A2-B1 language proficiency
Source: screenshot taken by P.V. Sysoyev in the Twee neural network.

Figure 2. Creation of training exercises for mastering Russian passive voice using the Alice neural network
Source: screenshot taken by P.V. Sysoyev in the Alice neural network.

Figure 3. Example of a lesson plan (excerpt) generated by the DeepSeek neural network, dedicated to the celebration of Maslenitsa in Russia
Source: query made by P.V. Sysoyev in the DeepSeek neural network.

Training and control. The diagnostic assessment shows that AI is already part of the daily routine of many course participants. Many methodological works describe methods of AI-based teaching Russian as a foreign language.

The scientists discuss the formation of students’ phonetic skills with AI-based web applications (Sysoyev, Ivchenko, 2025), the development of students’ lexical and grammatical skills based on communication with chatbots and AI corpus technologies (Grudeva, Alexeeva, 2020; Dziuba, 2024; Sysoyev, Filatov, 2024), the development of productive foreign language speech activity in foreign language interaction with AI tools (Kozlovtseva, 2023; Nefedov, Ogryzko, 2023); the AI-based formation of students’ translation competence (Liu, 2025). Based on the analysis of the above and other works, this study proposes a list of AI-based technological solutions for teaching Russian as a foreign language (Table).

 List of AI-based technological solutions in teaching Russian as a foreign language

Source: compiled by P.V. Sysoyev.

In our opinion, the development of practical AI-based teaching methods for Russian as a foreign language should consider the following provisions.

First, the use of specific AI tools should be systematic rather than chaotic. Teachers and students should understand when, how often, and with what educational/research purposes a particular AI tool is used.

Second, students’ communication with AI tools aimed at forming language skills or developing speech skills should be conducted outside of class and correspond in terms of content (subject-thematic and language proficiency) to the course program.

Third, materials from students’ extracurricular interaction with AI tools should be discussed in class. There are many ways to use such materials, from the teacher checking for evidence of extracurricular work to small group discussions of AI feedback.

Fourth, when teaching to write essays in Russian, evaluative and corrective feedback from generative AI tools and changes students can make to their work are of particular interest.

Fifth, extracurricular practice with AI tools creates additional conditions for the further formation of language skills and the development of speech abilities. It should be integrated into traditional teaching methods, but not replace traditional forms of work, which have proven their effectiveness.

Conducting research work. AI-based technological solutions can be used by students in their research work. AI corpus technologies with texts of different genres, authors, and historical periods can be used in research in philology or linguistics, and methodological neural networks can be used in methodology of teaching Russian as a foreign language. The experiment show that the research potential of AI is generally not in demand among teachers of Russian as a foreign language (x = 3.12). This is because students’ research work on Russian as a foreign language is mainly carried out within the framework of bachelor’s, master’s, and postgraduate programs in Russian as a foreign language. Students of Russian philology or Russian as a foreign language teaching methodology have research as one of their learning activities. Students of the preparatory department who study Russian as a foreign language to continue studying in specialized fields are more interested in learning the language.

Organization of the educational process in the triad “teacher – artificial intelligence – student.” The integration of AI technologies into teaching Russian as a foreign language requires special skills in organizing the educational process in a blended learning format, where students’ extracurricular practice with specific AI-based technological solutions is integrated into traditional teaching methods (Molchanovskiy, 2014; Strelchuk, Kozhevnikova, Borchenko, 2023; Strelchuk, Yunxia, Yajun, 2024). The the ascertaining section showed that most teachers have a high level of ability to organize the educational process in the triad “teacher – artificial intelligence – student” (x = 4.70). This can be explained by the fact that teachers developed this ability during the COVID-19 pandemic and are competent to organize the educational process in a blended format.

Conclusion

The integration of AI technologies into teaching Russian as a foreign language provided teachers with additional functions due to the need for methodologically competent structuring of the educational process in the new paradigm of the “teacher-artificial intelligence-student” triad. In this regard, developing teachers’ methodological competence in the field of AI is particularly relevant at the present stage. The study identified the key aspects of methodological competence in the field of AI: a) prompt engineering, b) teaching and monitoring, c) organization of the educational process. The content of these components was used to develop a short-term professional development course for teachers of Russian as a foreign language who want to improve their methodological competence in the field of AI. The effectiveness of the proposed course was proven during experimental training. At the same time, empirical research showed that not all topics of the course were equally well understood and in demand among participants. The most difficult topics were prompt engineering, and AI-based formation and control of pronunciation skills. Other topics of the course that did not cause any particular difficulties included: AI-based planning and developing teaching materials for Russian as a foreign language, forming and controlling lexical and grammatical skills, developing oral and written dialogue skills based on AI practice, development of written monologue skills based on AI practice, conducting research based on AI, and organization of the educational process in the triad “teacher – artificial intelligence – student.” Teachers have recently developed the ability to use certain AI-based technological solutions as AI has become more widespread; they have also transferred ICT skills formed during the COVID-19 coronavirus pandemic.

The promising aspect of the research lies in the development of models for the systematic use of AI-based technological solutions in teaching Russian as a foreign language.

About the authors

Pavel V. Sysoyev

Author for correspondence.
Email: psysoyev@yandex.ru
ORCID iD: 0000-0001-7478-7828
SPIN-code: 2943-7230
Scopus Author ID: 8419258800
ResearcherId: I-6136-2016

Doctor of Pedagogy, Professor, Honored Scholar of Higher Education of the Russian Federation, Head of Russian Academy of Education Research Center

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2. Figure 1. Creation of an educational text on the topic “New Year in Russia” by the Twee neural network using specific active vocabulary, a specified volume, and targeting students with A2-B1 language proficiency
Source : screenshot taken by P.V. Sysoyev in the Twee neural network.

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3. Figure 2. Creation of training exercises for mastering Russian passive voice using the Alice neural network
Source: screenshot taken by P.V. Sysoyev in the Alice neural network.

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