The Rhetorical Representation of Divine Light in Qur'an 24:35: A Comparative Study of Muhammad Abu Musa's Interpretation and ChatGPT Analysis - Sugeng Hariyadi; Talqis Nurdianto; Aqdi Rofiq Asnawi (Prodi Pendidikan Bahasa Arab, Universitas Muhammadiyah Tangerang, 2026)
This study aims to compare the interpretation of the rhetorical simile of Divine Light in the thirty-fifth verse of Sūrat al-Nūr as presented by Muhammad Abū Mūsā, a contemporary scholar of Arabic rhetoric, and the analysis produced by ChatGPT as an artificial intelligence-based system. The study focuses on comparing their approaches to interpreting the contextual relationship of the verse within Sūrat al-Nūr and their analysis of the elements of the compound simile (tashbīh murakkab) based on the principles of ʿilm al-bayān. Employing a descriptive-comparative library research method, this study uses Abū Mūsā’s work on the parables of Sūrat al-Nūr as the primary human analysis source, while the AI-based analysis was generated through ChatGPT 5.5 OpenAi and verified by comparison with relevant academic studies. The comparison was organized around two main aspects: the interpretation of the verse’s context and the structure and rhetorical function of the Divine Light simile. The findings reveal that both analyses agree that Divine Light symbolizes divine guidance and is closely connected to the thematic structure of Sūrat al-Nūr. However, they differ in their approaches to meaning construction. Abū Mūsā’s analysis begins from the broader context and objectives of the surah, particularly by relating the simile to the preceding legal discourse, whereas ChatGPT emphasizes the symbolic, spiritual, and educational dimensions of the rhetorical image. The study concludes that artificial intelligence can serve as a valuable supporting tool for enriching Qur’anic rhetorical studies, but it cannot replace the contextual depth and rhetorical appreciation provided by human scholars. This research contributes to the field of Digital Humanities by examining the potentials and limitations of Large Language Models (LLMs) in analyzing Qur’anic rhetoric.