<![CDATA[The growing use of AI translation has increased the need to evaluate whether generative models can produce grammatically accurate translations for underrepresented languages such as Malay. English-to-Malay translation remains challenging because Malay syntax requires accurate verb affixation, modifier placement, phrase ordering, and noun phrase + verb phrase (FN + FK) alignment in Standard Malay. This study examines the syntactic and lexical accuracy of ChatGPT-5 in translating English news texts into Standard Malay, with a focus on FN + FK structures. Grounded in Generative Transformation Theory and Nik Safiah Karim’s Malay grammar framework, the research analyzes how underlying English sentence structures are transformed into Malay surface structures. Using a qualitative descriptive linguistic design supported by descriptive error analysis, authentic bilingual sentences from the Borneo Bulletin were analyzed across social, economic, technological, cultural, and sports domains. The samples were selected from news sentences containing FN + FK structures, translated using a standardized ChatGPT-5 prompt, coded according to syntactic error categories, and validated through review by two qualified linguists and Malay grammar specialists. Findings show that ChatGPT-5 generally preserves complex sentence structures, including verb affixation, modifier placement, and phrase order, while accurately translating culturally embedded expressions and technical terms, such as mock cheque, Turnaround, and king of fruits. Minor syntactic deviations were observed, particularly in morphological mapping, modifier sequencing, and lexical narrowing, but they did not significantly affect the meaning. The study demonstrates ChatGPT-5’s potential as a supportive AI tool for multilingual translation and highlights the value of syntactically informed evaluation. These insights inform both the theoretical understanding of Malay syntax in AI translation and practical applications for professional, academic, and media contexts.]]>
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