Intercultural Communication Failures in AI-Generated Translation: An English-Uzbek Perspective

Purpose: This study aims to investigate intercultural communication failures in AI-generated translations between English and Uzbek, focusing on the preservation of pragmatic meaning, cultural nuances, and communicative intentions.Methodology: The research employs comparative, pragmatic, and discourse analysis methods to examine AI-translated idiomatic expressions, humor, politeness strategies, and culture-specific units. AI systems analyzed include ChatGPT, Google Translate, and DeepL.Results: The study found that AI-powered translation systems often rely on literal and structural translation strategies, which frequently lead to semantic distortion and pragmatic inaccuracies. As a result, intercultural misunderstandings and communication failures occur, especially between linguistically and culturally distant languages.Conclusions: Integrating intercultural competence, cultural mediation, and context-sensitive mechanisms into AI translation models is crucial to enhance pragmatic equivalence and sociocultural adaptation in multilingual communication.Limitations: This study primarily analyzes AI translations in English-Uzbek contexts and relies on qualitative methods; broader empirical testing across other languages and larger datasets is needed.Contributions: The study contributes to modern translation studies by elucidating the relationship between artificial intelligence, intercultural communication, and translation pragmatics, offering insights for the development of culturally aware and context-sensitive AI translation technologies.

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