Teaching interpreting in the Chinese-Russian language pair using the case method with artificial intelligence technologies

The article is devoted to the theoretical substantiation and practical use of artificial intelligence (AI) in teaching interpreting in the Chinese-Russian language pair using the case method. The aim of the research is to develop an algorithm for applying the case method with the inclusion of AI technologies in the training of interpreters in the Chinese-Russian language pair. The article examines the theoretical basis and practical application of AI technologies in teaching interpreting and analyzes the didactic possibilities of neural networks in preparation for various translation scenarios. The scientific novelty of the research lies in the fact that the principles of using AI for the professional training of interpreters were identified and substantiated. The potential of AI in the training of translators and its didactic capabilities were determined. Examples of educational cases with the integration of AI tools to improve the efficiency of foreign language teaching are presented. The criteria for an objective assessment of interpreting performed by students as part of their work with cases were defined and refined. The algorithm for working with AI tools in the learning process is described, as well as examples of tasks and criteria for assessing skills. The paper contains examples of tasks and algorithms for working with AI tools at different stages of translator training. The research found that AI-based technologies improve the quality of translator training, increase student motivation and stress resistance, allow students to show more independence in preparation, and simulate real professional situations.