<![CDATA[This study examines the translation of jinās and related sound-based rhetorical patterns in the Qur’an by comparing the Arabic source text with the English translations of Abdullah Yusuf Ali and Arthur J. Arberry. It investigates how phonological form, semantic meaning, morphological structure, rhythm, and discourse function are preserved, transformed, or lost in translation, while exploring the limited contribution of generative artificial intelligence (AI) to rhetorical translation analysis. Adopting a qualitative comparative design, the study integrates classical Arabic rhetoric, Qur’anic discourse analysis, and comparative translation analysis. Four purposively selected cases are examined: Q.S. al-Fajr [89]: 22 (ṣaffan ṣaffan), Q.S. al-Lail [92]: 5 (aʿṭā/ittaqā), Q.S. al-Duha [93]: 9–10 (taqhar/tanhar), and Q.S. al-An‘am [6]: 26 (yanhawna/yanʾawna). ChatGPT is employed as a human-supervised exploratory instrument to compare linguistic forms and generate alternative formulations, with all outputs evaluated against the Arabic text, classical rhetorical categories, contextual meaning, and established translation principles. The findings indicate that rhetorical translation involves multidimensional reconfiguration rather than a simple opposition between preservation and loss. Across the four cases, semantic relations are generally retained to a greater extent than their original phonological organization, although the degree and form of transformation vary. The study proposes four heuristic categories of rhetorical change: phonological loss, rhythmic loss, morphological or formal loss, and phonological-semantic restructuring. It further distinguishes compensatory approximation as a potential translation strategy rather than a category of loss. Experimental AI-generated formulations illustrate that increased sound similarity may introduce semantic or contextual distortions, underscoring the need for human validation. Contribution: The study contributes a framework for analyzing rhetorical change across languages and demonstrates how generative AI can support exploratory linguistic analysis without replacing scholarly interpretation or the methodological authority of the researcher.]]>
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