Authors’ names:
Nadezhda I. Almazova, Liudmila P. Khalyapina – Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Abstract:
The article analyzes the transformation of the professional training of linguist-mediators in the context of the growing integration of neural network technologies into foreign language education. The relevance of the study stems from the rapid digitalization of language education and translation, which has led to a fundamental reassessment of traditional models of professional training. The increasing role of artificial intelligence (AI) not only as a tool for automating translation but also as a means of constructing meaning in intercultural communication has made this issue particularly pressing. Under these conditions, the preservation and adequate transmission of the Russian cultural code — a system of values, meanings, and symbols that defines national identity — has become a central concern. Recent empirical studies point to systemic limitations of contemporary language models in conveying culturally specific constructions, humor, irony, implicit meanings, and precedent phenomena, creating the risk of diminishing cultural specificity in machine translation. The authors examine how conceptions of the Russian cultural code are being transformed under the influence of AI-driven language education. They argue that the use of foreign neural network models trained predominantly on Western text corpora may contribute to the erosion of national cultural identity, since such systems lack internal mechanisms for recognizing and preserving Russian cultural realities, everyday practices, and culture-specific value orientations. Against this background, the article emphasizes the need to rethink the professional role of the linguist-mediator: rather than serving merely as a transmitter of meaning, the linguist-mediator should become an expert interpreter capable of evaluating and refining the output of neural machine translation, bridging cultural gaps, and preserving the semantic depth of the source message
| Section | CROSS-CULTURAL COMMUNICATION. TOPICAL ISSUES IN EDUCATION |
| DOI: | 10.47388/2072-3490/lunn2026-74-2-142-158 |
| Downloads | 4 |
| Key words | linguist-mediator; neural network foreign language education; cultural code; translation activity; artificial intelligence; intercultural communication |