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Abstract: This article presents a methodological model for integrating artificial intelligence (AI) into teaching Chinese students how to critically read authentic Russian newspaper texts. The model aims to address the lack of specific methods solutions for implementing AI in teaching Russian as a foreign language (RFL). The relevance of the study stems from China’s “AI + Education” strategy, which reflects the global trend towards digitalization and personalization of the education process. The model is developed within the context of reform of foreign language teaching in China, according to which language is conceived not as an end in itself but as a tool for professional intercultural communication. The aim of this study is to design and test a methodological model focused on developing media literacy, critical thinking, and intercultural competence among future Chinese specialists in Russian studies. The study is based on a pilot experiment conducted at Xiamen University, which employed the Chinese neural network DeepSeek as a key AI tool. Philology students of B1+ level, representatives of the “Generation Z,” used AI to support their comprehension, interpretation, and critical analysis of Russian newspaper texts. The suggested model is based on the principle of meaningful “human-machine cooperation,” in which AI does not replace learning activity but serves as a tool for cognitive scaffolding. The author combines the Russian traditions of teaching newspaper text reading with innovative digital tools. The practical significance of the study is realized through the creation of two didactic instruments: 1) a typology of 11 categories of learning tasks related to the functional roles of AI (“Media Analyst”, “Cultural Mediator”, “Digital Opponent”, etc.); and 2) a task-marking system aimed at regulating academic integrity. The article also analyzes the linguodidactic potential and identifies the limitations of the DeepSeek neural network in the context of teaching RFL. The key results include high quality restructuring of instruction time in favor of intellectually intensive tasks, increased spontaneous student speech, and development of a responsible attitude towards AI. The model can be adapted for use in both Chinese and Russian universities.

About the author(s) :

Yulia A. Antonova

Xiamen University (Xiamen, People’s Republic of China)

ORCID ID: https://orcid.org/0000-0002-3248-288X

Publication Timeline:

Date of receipt: 30.12.2025; date of publication: 31.03.2026

References:

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