FROM LINGUISTIC TRANSFER TO COGNITIVE RECONSTRUCTION: A MODEL FOR DEVELOPING HIGHER-ORDER THINKING IN TRANSLATOR TRAINING IN THE AGE OF ARTIFICIAL INTELLIGENCE

Abstract

The article examines the evolution of approaches to translator training in the context of artificial intelligence development, proposing a model for the shift from traditional linguistic transfer to cognitive reconstruction of text. The author substantiates the need to develop higher-order thinking in future translators, one that is capable of critical analysis, creative interpretation, and adaptation in a dynamic digital environment. The study analyzes limitations of classical teaching methods that focus on lexico-grammatical equivalences and demonstrates that modern requirements include the ability to uncover hidden meanings, take cultural codes into account, anticipate the target audience’s reception of the text, and interact effectively with neural network tools. Special attention is paid to the role of artificial intelligence as a catalyst for change: machine translation is transformed from an object of proofreading into a tool of symbiosis that requires post-editing skills, contextual adequacy assessment, and the recognition of systemic algorithmic errors. The authors propose specific pedagogical methods to develop cognitive flexibility—project-based assignments, case study analysis, metacognitive strategies, and role-playing games—that cultivate the ability to justify translation decisions, work in teams, and manage complex intercultural communications. The importance of integrating interdisciplinary knowledge (cultural studies, perceptual psychology, communication theory) and digital literacy is emphasized, including the critical evaluation of online resources and big data. The research results indicate the formation of an autonomous professional identity among students, combining linguistic erudition with analytical skills and cultural sensitivity. In conclusion, it is asserted that the proposed model equips specialists with the ability to balance AI’s technological capabilities and the creative-interpretive nature of translation, while maintaining responsibility for semantic accuracy and ethical considerations in the era of globalization.