Translation Challenges of the Qur'an and Opportunities of integrating AI Automation - Soulhi, Said (Universitas Muhammadiyah Surakarta, 2026)
Despite the growing number of English translations of the Qur'an, persistent linguistic, semantic, rhetorical, and theological challenges continue to limit accurate comprehension for non-Arabic readers. Existing studies largely address these challenges descriptively or through comparative translation analysis, yet they seldom explore how recent advances in Artificial Intelligence (AI), particularly reasoning-based Large Language Models (LLMs), can systematically respond to the multidimensional complexity of Qur'anic language. This gap becomes critical in light of semantic shifts, polysemy, phonological symbolism, rhetorical devices, and doctrinal diversity that conventional translation methods struggle to accommodate. This study employs a qualitative-analytical approach grounded in Qur'anic linguistics, translation studies, and computational linguistics. It critically examines representative examples from English Qur'an translations to identify recurrent translation challenges, including lexical asymmetry, rhetorical loss, semantic ambiguity, and theological bias. Building on these findings, the paper proposes a conceptual AI framework that integrates multimodal embeddings, knowledge graphs, Qur'anic phonology, classical exegesis, theological schools, and historical context (Asbāb al-Nuzul), supported by explainable and anti-hallucination AI mechanisms. The results demonstrate that AI-assisted frameworks, when guided by authoritative Islamic knowledge sources and transparent reasoning models, have the potential to significantly enhance translation accuracy, semantic depth, and rhetorical sensitivity. Rather than replacing human scholarship, AI is positioned as an augmentative tool that bridges linguistic gaps while preserving interpretive plurality. The international impact of this research lies in its contribution to global Qur'anic studies, interfaith understanding, and ethical AI development. By proposing a multidisciplinary, culturally sensitive AI model, this study offers a scalable pathway for improving Qur'an translation, interpretation, and accessibility for diverse global audiences.