Application of Artificial Intelligence Technologies in Teaching Russian Language to Chinese Students

The relevance of the study is associated with the need to improve the methodology of teaching Russian to Chinese students in connection with the expansion of trade contacts between Russia and China and modern trends in education. The novelty of the work lies in the lack of research on the use of artificial intelligence in the process of teaching Russian to Chinese students. The purpose of the work is to consider the theoretical foundations of teaching Russian to Chinese students using artificial intelligence technologies, such as a description of general and specific didactic principles of teaching, an assessment of cultural and technical barriers in this pedagogical process, an indication of the specifics of using chatbots in teaching Russian and the importance of correctly formulating requests to the chatbot (prompts). The article presents the results of a survey of Chinese students of the Institute of Foreign Languages, Changchun Polytechnic University, which revealed factors that contribute to increasing motivation for learning foreign languages in general and the Russian language in particular and determined the role of modern teaching methods, in particular the use of chatbots. The problem of using chatbots capable of supporting a pedagogical experiment in Russian and Chinese is indicated. It primarily consists in the absence of such chatbots that can be used to implement high-quality written and oral communication in the process of teaching Chinese students Russian. The specifics of the model of teaching Chinese students Russian based on a polymodal and intercultural approach are determined. It is noted that the fundamental rules for formulating prompts considered in publications and on websites are general didactic. Of the specific didactic principles of formulating prompts, it is proposed to consider the principle of improving the linguistic competence of Chinese students in the field of the Russian language. It is revealed that the most problematic in using the prompt engineering method in teaching Russian to Chinese students is the formulation of prompts taking into account the specifics of lexical and cultural material.

1. Trotsuk SN. Neural networks as a pedagogical tool in teaching Russian as a foreign language. Barkhudarov Readings, 2024;1:118−122. (in Russian)

2. Ive J. Leveraging the potential of synthetic text for AL in mental healthcare. Frontiers in Digital Health. 2022; 4. https://doi.org/10.3389/FDGTH.2022 1010202/BIBTEX

3. Muktadir GM. A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting). 2023. arXiv:2310.04438

4. Sahoo P et al. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. 2024. arXiv:2402.07927v1

5. White J et al. A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. 2023.

6. arXiv:2302.11382

7. Schulhoff S et al. The Prompt Report: A Systematic Survey of Prompting Techniques. 2024.

8. arXiv:2406.06608v3