Curricular alignment in AI-mediated language teaching: An exploratory study on Russian as a foreign language

Bastian Fuchs

doi:10.54475/jlt.2026.002

Authors

Bastian Fuchs Rhenish Friedrich Wilhelm University of Bonn https://orcid.org/0009-0001-9560-2834

DOI:

https://doi.org/10.54475/jlt.2026.002

Keywords:

GenAI, AI-mediated language teaching, curriculum design, CEFR, RFL

Abstract

The adoption of generative AI tools in language education has provided teachers with new opportunities to personalize learning, design syllabi, create and modify content, and offer their learners more detailed feedback (Chaika et al., 2025; Kohnke et al., 2023; Wan & Moorhouse, 2025). However, most implementations remain decentralized and are often driven by individual teachers’ practices rather than institutional strategy, and frequently lack a solid theoretical or pedagogical foundation. This is particularly true for languages other than English, where empirical research is still limited. Without explicit curricular alignment, AI-generated materials can fall short of instructional goals, leading to missed opportunities for meaningful and sustainable integration. This article addresses these gaps by exploring how GenAI can assist teachers in lesson planning and content creation in alignment with CEFR-based curricular requirements. Drawing on the author’s own teaching experience with a university-level Russian as a foreign language course (B1.1), it illustrates how AI tools like ChatGPT can be used to generate a syllabus, level-appropriate vocabulary lists, learning materials, text type templates, and communicative tasks. In addition to practical applications, the article critically reflects on the evolving role of teachers in AI-mediated classrooms, advocating for a human–AI co-teaching approach instead of the commonly perceived “AI versus teacher” dichotomy still prevalent in professional discourse. By combining a review of current research with classroom-based insights, this article offers a practice-oriented exploration of GenAI’s potentials and challenges for curriculum-aligned language teaching. While the case study focuses on Russian at the university level, the observations can be applied to other languages and contexts. The article is intended for language instructors and curriculum developers working within CEFR-based frameworks, especially in higher education and language learning centers.

Author Biography

Bastian Fuchs, Rhenish Friedrich Wilhelm University of Bonn

Dr. Bastian Fuchs is Research Assistant and Instructor of Russian at the Language Learning Centre of the Rhenish Friedrich Wilhelm University of Bonn, Germany. He is responsible for the curriculum design of language courses. His research and professional interests include the implementation of the CEFR in higher education language programs, the integration of generative AI in language teaching and learning, bilingualism and heritage linguistics, and the teaching of Russian as a foreign and heritage language.
Email: bastian.fuchs@uni-bonn.de

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