ARTIFICIAL INTELLIGENCE-BASED ORAL TRANSLATION: PROBLEMS OF LINGUOCULTURAL AND PRAGMATIC ADAPTATION

Abstract

This article examines the problems of linguocultural and pragmatic adaptation in artificial intelligence-based oral translation. It analyzes how modern AI systems process spoken language and the extent to which they can preserve cultural meaning, communicative intention, and contextual appropriateness. The study highlights that although artificial intelligence significantly improves translation speed and accessibility, it still faces limitations in interpreting culturally bound expressions, speech acts, and implicit meanings. The findings suggest that effective oral translation in the digital era requires a hybrid approach that combines machine efficiency with human interpretative competence