A Shallow Echo: Artificial Intelligence and the Semantic Flattening of the Qur'an

ABSTRACT Scriptural Arabic relies on highly intentional word choices, employing apparent synonyms and near‐synonyms that convey distinct semantic values based on their specific textual placement. Historically, computational translation has struggled to reproduce these precise textual boundaries. Addressing this issue, the present investigation assesses how effectively current artificial intelligence renders these cognitive and near‐synonymous pairs into English. Anchored in an established semantic model of lexical relations, this study operationalizes its evaluation through specific diagnostic parameters: connotative divergence, contextual licensing, collocational restrictions and semantic prosody. A curated sample of verses was assembled to evaluate both Neural Machine Translation (NMT) engines and Large Language Models (LLMs), applying strict prompt constraints and overlap checks to mitigate the retrieval of memorized human translations. The selection focuses on six established pairs of terms: khawf and khashyah (fear/awe), sabil and ṭarīq (metaphorical way/physical path), jasad and jism (lifeless body/living form), ḍiyāʾ and nūr (radiance/light), maṭar and ghayth (punitive rain/merciful rain), alongside khalaqa and jaʿala (creation ex nihilo/appointment). Machine‐generated texts were subsequently compared with recognized scholarly interpretations and authoritative exegetical works (tafsīr). The findings demonstrate that modern algorithms neutralize these lexical variations. Instead of providing context‐sensitive equivalents, the programs default to statistically frequent English terms, thereby producing semantically impoverished texts and compromising semantic accuracy. By detailing the specific types of connotative data that escape computational recognition, this paper clarifies the present limitations of applying automated processing to religious texts. This analysis consequently provides a definitive foundation for evaluating future programming iterations in the field of automated scriptural rendering.