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The article is devoted to a new paradigm of considering the professional communicative competence of the language teacher in keeping with the changing approach to the description of the content of language education, which is reflected in the documents of the Council of Europe, in particular the latest Companion Volumes to the Common European Framework of Reference for Languages (CEFR) from 2018 and 2020. These documents indicate a transition from four traditional language skills: listening, reading, speaking and writing (which laid the basis for the Russian language teaching tradition) to four modes of communication: reception, production, interaction and mediation. The reasons for and the essence of these changes are described in the first part of the article. Then the article summarizes the leading theories in the study of the language teacher’s professional communicative competence in Russia and presents a revised definition of the professional communicative competence. After that it provides definitions in Russian of the four modes of communication, as well as activities and strategies within them. When considering each mode of communication, their manifestation in the context of the language teachers’ target language use domain is described. In conclusion, the article presents a functional model of communicative activities of reception, production, interaction and mediation as part of the language teacher’s professional communicative competence.

The article discusses the strategies for using artificial intelligence (AI) in the context of providing written corrective feedback in language education. It provides an overview of the latest research in this area, starting from the most significant publications in the field of Russian neurodidactics and international conferences dedicated to the use of AI in education, to practical examples of using chatbots for feedback in language teaching. The article emphasizes the transformation of the role of language educators through the application of artificial intelligence, highlighting the importance of focusing the teacher on the essential aspects of students’ communicative development while delegating the technical aspects of proofreading written assignments to artificial intelligence. The application of such technologies changes the ways feedback is provided, allowing for a more in-depth linguistic analysis and the creation of more extensive comments on written works. Examples of specific requests made to artificial intelligence when marking assignments and examples of their implementation are provided. In addition, the article summarizes the strengths and weaknesses of using AI technologies for assessment and draws conclusions about its role in providing written corrective feedback. The article also highlights the need for a thoughtful integration of AI into the educational process and calls for further research on the impact of artificial intelligence technologies on the development of students’ written productive language skills.