Keyword: «artificial intelligence in language education»

The relevance of studying the impact of artificial intelligence (AI) technologies on teaching foreign language to students of non-language departments is determined by the rapid development of modern AI technologies and the insufficient clarity regarding their influence on the effectiveness of education. The aim of this study is to assess the nature and boundaries of the influence of AI technologies on the effectiveness of foreign language teaching to students of non-language departments and to develop recommendations for their careful use. The study is based on methods of analysing scientific literature on the topic, systematization, content analysis, and data synthesis to develop recommendations for using AI in foreign language education. The research involves the systematization of theoretical aspects of AI use in foreign language teaching, identifying positive and negative factors influencing the effectiveness of teaching students who do not major in linguistics. It highlights that AI has significant and, in some cases, extensive potential for positive impact. The results of content analysis of publications on AI in education are described, revealing key trends and contradictions and identifying positive, negative, and neutral factors influencing foreign language education for non-language majoring students, which define the conceptual boundaries of its impact on educational effectiveness. Recommendations are proposed for the careful use of AI in foreign language teaching to students of non-language departments, aimed at enhancing the effectiveness of education and minimizing potential risks. The theoretical significance of the research lies in the systematization of issues related to the use of AI in foreign language education and assessing its impact on the effectiveness of teaching students of non-language departments. The practical significance lies in the potential application of the developed recommendations by foreign language teachers to improve the efficiency of teaching students of non-language departments with the use of AI technologies.

In the context of the digital transformation of education and the mass spread of foreign language learning, it is important to use artificial intelligence technologies in such a way that they enhance rather than replace the activity of teachers and learners. Without a well-designed methodology and clear rules of use, there is a growing risk of reduced learner autonomy, loss of academic integrity, and a widening gap between the theory and practice of language teaching. The aim of the article is to substantiate and describe a model for the effective use of artificial intelligence technologies to enhance classroom-based acquisition of a second foreign language, implemented as an adaptive methodological ecosystem that strengthens learner activity and preserves the primacy of human contribution. The study is based on a theoretical and methodological analysis of works on the theory of second language acquisition, intelligent computer-assisted foreign language learning, formative assessment, and self-regulated learning. These strands are synthesized into practical tools that take into account Russian educational standards and multilevel descriptions of communicative competence. The article proposes an adaptive ecosystem of scaffolding support using artificial intelligence technologies, built around five interrelated components: sequencing of lesson stages, scaffolding support, target skills, safeguards, and self-regulation, as well as a synergy mechanism that structures iterative interaction cycles between the human user and the digital assistant. The model introduces an intervention threshold for artificial intelligence technologies that does not exceed one fifth of the volume of auxiliary materials and presents a ready-to-use toolkit: an alignment map of the digital assistant’s roles across lesson stages, a policy for the use of artificial intelligence with a prompt log, formative assessment rubrics, lesson scenarios, a risk and resilience matrix, an implementation checklist, and monitoring indicators covering vocabulary retention, fluency of oral speech, error frequency, text coherence, teacher checking time, and the footprint of artificial intelligence use. Theoretical significance lies in clarifying the role of artificial intelligence technologies as an instrument of metacognitive support that does not substitute for genuine language use. Practical significance consists in providing a methodological toolkit that helps save teacher time, improve the quality of formative feedback, protect academic integrity, and adapt the model to different disciplines, proficiency levels, and classroom formats.