In “Islamiclegalbench: Evaluating LLMs Knowledge and Reasoning of Islamic Law Across 1,200 Years of Islamic Pluralist Legal Traditions” (arXiv), Ezieddin Elmahjub (Qatar University) and others observe, “As millions of Muslims worldwide turn to LLMs like GPT, Claude, and DeepSeek for religious guidance, a critical question emerges: Can these AI systems reliably reason about Islamic law? This paper introduces IslamicLegalBench, the first multi-school benchmark for evaluating LLM performance across a broad range of Islamic jurisprudence (fiqh). Drawing from 38 foundational legal texts spanning 1,200 years and seven schools of jurisprudence, we create 718 evaluation instances across 13 tasks, manually collected and organized by complexity, from basic recall to sophisticated reasoning including legal rationale identification (‘illah), analogical application (qiyās), and cross-school synthesis.”
In “QV finder: an accurate Quran verse finder system” (Indonesian Journal of Electrical Engineering and Computer Science), Bashar Al-Rfooh (University of Jordan) and others “present QV finder, an artificial intelligence (AI)-powered system that utilizes a fine- tuned whisper-based automatic speech recognition (ASR) model specifically trained on diverse Quranic recitations for the whole Quran. In this paper, we present a robust pipeline for Quranic verse retrieval that bridges the gap between ASR technology and domain-specific linguistic complexity. The model supports both professional and normal reciters, even under noisy conditions.”
