INTEGRATION OF GENERATIVE ARTIFICIAL INTELLIGENCE INTO RUSSIAN LANGUAGE TEACHING IN CHINESE HIGHER EDUCATION: OPPORTUNITIES, RISKS AND PROSPECTS

Abstract

The article examines the integration of generative artificial intelligence into Russian language teaching in Chinese higher education. The purpose of the study is to systematize typical AI-assisted scenarios in university Russian courses and to identify their opportunities, risks and development prospects. The research is based on theoretical analysis and methodological generalization of published cases involving large language models, including DeepSeek, ERNIE Bot, Kimi and Doubao. It is shown that generative AI can support lesson planning, graded materials, dialogic practice, writing and translation feedback, and individualized learning routes. At the same time, the article emphasizes risks connected with factual errors, unstable Russian-language output, academic integrity, data protection and the possible substitution of real communication by simulated dialogue. The author argues that AI should be used as a teacher-guided didactic environment rather than as an autonomous instructor.