Large language models (LLMs) are increasingly used for knowledge-intensive question answering, including religious and legal questions. Islamic knowledge is a particularly demanding setting: answers are expected to be grounded in authoritative sources, citations must be exact, Arabic varieties differ substantially from the language of classical sources, and legitimate jurisprudential disagreement must be represented rather than collapsed into a single answer. This survey reviews the emerging field of Islamic LLMs and trustworthy Islamic AI. We organize the literature around Arabic NLP and Arabic-centric LLMs, Islamic NLP resources, Qur'anic question answering, Islamic knowledge benchmarks, retrieval-augmented generation, Islamic legal reasoning, inheritance reasoning, hallucination evaluation, and trustworthiness. We argue that fluency in Arabic is not sufficient for Islamic AI. Reliable systems require curated sources, retrieval and verification modules, citation-aware generation, madhhab-aware reasoning, human expert evaluation, and benchmarks that measure not only answer accuracy but also faithfulness, source validity, and reasoning quality. The survey concludes with a research agenda for hallucination-resistant Islamic AI systems.

翻译：大语言模型（LLMs）越来越多地被用于知识密集型问答，包括宗教和法律问题。伊斯兰知识是一个特别具有挑战性的领域：期望答案基于权威来源，引用必须精确，阿拉伯语变体与古典来源语言存在显著差异，且合法的教法学分歧必须得到体现，而非简化为单一答案。本综述回顾了伊斯兰大语言模型与可信伊斯兰人工智能这一新兴领域。我们围绕阿拉伯语自然语言处理与以阿拉伯语为中心的大语言模型、伊斯兰自然语言处理资源、《古兰经》问答、伊斯兰知识基准、检索增强生成、伊斯兰法律推理、遗产继承推理、幻觉评估以及可信度等主题组织文献。我们认为，仅具备阿拉伯语流利度不足以支撑伊斯兰人工智能。可靠的系统需要精心筛选的来源、检索与验证模块、感知引用的生成、感知教法学派的推理、人类专家评估，以及不仅衡量答案准确性，还衡量忠实性、来源有效性和推理质量的基准。本综述最后提出了针对抗幻觉伊斯兰人工智能系统的研究议程。