Large Language Models in Teaching Russian as a Foreign Language: A Review (2023–2025)

Abstract

This review of research synthesizes how large language models (LLMs) are being used to teach Russian as a foreign language (RFL) across three research traditions – in Russia, English-language contexts, and China – covering January 2023 to October 2025. The article draws on 55 peer-reviewed and practitioner sources to identify recurring themes, benefits, risks, and implementation conditions. Across settings, the literature reports five recurring benefits: (1) expanded out-of-class practice via chatbots and conversational agents; (2) personalization that supports learner autonomy and differentiation; (3) development of all four skills and cultural competence through multimodal, task-based activities; (4) faster formative feedback on writing and grammar through exemplars and targeted drills; and (5) access to authentic, level-adaptable materials. Reported risks are comprised of factual and grammatical errors, weak pragmatic or cultural fit, over-reliance that may erode independent strategies, unequal access and data-privacy constraints, and unsettled assessment practices in AI-rich classrooms. Regional emphases differ: Russian studies foreground linguodidactic integration and teacher oversight; English-language scholarship brings into focus ethics, critical AI literacy, and customized tutoring; Chinese research advances human–AI co-teaching and “virtual + real” classroom models. Practical guidance follows: design AI-mediated tasks with explicit objectives and boundaries, train verification habits (cross-checking, source attribution, corpus-based checks), and pair AI feedback with human review. Research priorities include Russian-specific models and prompting resources, longitudinal evidence on learning outcomes, validated rubrics for AI-assisted work, and teacher professional development. Overall, LLMs can enhance RFL when their use is deliberately orchestrated and educators remain the cultural and pedagogical anchor.