Evaluating Terminological Consistency in AI-Generated English–Arabic Political Translation

Abstract

Machine translation has become more fluent and contextually accurate with recent advances in artificial intelligence. However, terminological consistency has been underexplored, particularly in political and electoral discourse where lexical repetition and conceptual precision are critical for cohesion and clarity. This study investigates terminological consistency in AI-generated English–Arabic political translations produced by ChatGPT and Google Gemini Advanced. The translations were generated and analyzed between January and June 2026 using the systems’ default settings to ensure comparability and avoid potential variations resulting from user-configured parameters. The study employs a mixed-methods corpus-based approach. The study analyzes 30 political and electoral texts with 60 recurring key terms. Quantitative analysis measures the degree of consistency in the form of stability percentages, and qualitative analysis studies lexical variation and its effect on discourse cohesion and clarity. The adequacy and consistency of the translation were checked against a reference translation based on the United Nations Development Programme (UNDP) Arabic Lexicon of Electoral Terminology. The results indicate that ChatGPT achieved higher terminological consistency than Google Gemini. ChatGPT’s lexical equivalents for repeated political and electoral terms were more stable than its Gemini counterpart, which showed more lexical variation, especially in context-sensitive terms such as campaign, electoral law, and judicial review. The study concludes that terminological consistency should be considered as a separate dimension of translation quality and emphasizes the importance of terminology control and human post-editing in AI-assisted political translation