发表机构
McMaster University（麦克马斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ILM是一款结合阿拉伯语NLP、KG和LLM的交互式教育平台，用于辅助多语言环境下伊斯兰先知故事的结构化学习、理解与评估。
AI 中文摘要
数字技术让伊斯兰叙事更易获取，但现有平台对这些故事的结构化学习和理解支持有限，尤其是在阿拉伯语和多语言环境下。我们推出ILM，一款针对先知故事的交互式教育平台，结合阿拉伯语自然语言处理、结构化知识表示和基于检索的问题生成。管理员审核通过的阿拉伯语叙事会由知识图谱（KG）构建引擎处理，该引擎识别实体和叙事关系并将其存储为结构化知识，使学习者能通过可视化故事图探索故事，并回答从KG生成的基于实体和关系的问题。此外，多语言检索管道会从原始叙事中检索相关段落，生成选择题和开放式理解题。对于开放式问题，作为评判者的大语言模型（LLM）会根据检索到的段落和参考答案评估学习者的回答，以确定正确性。该平台还将《古兰经》内容作为独立的丰富层，允许为选定的叙事补充有来源支持的信息。通过结合结构化知识和基于段落的检索，ILM支持阿拉伯语和多语言内容的叙事探索、理解和评估。该系统证明了将结构化知识表示与基于检索的生成相结合以支持伊斯兰叙事交互式学习的可行性。演示可在此http URL获取。
英文摘要
Digital technologies have made Islamic narratives more accessible, but existing platforms provide limited support for structured learning and comprehension of these stories, particularly in Arabic and multilingual settings. We present ILM, an interactive educational platform for Stories of the Prophets that combines Arabic natural language processing, structured knowledge representation, and retrieval-based question generation. Admin-approved Arabic narratives are processed by a Knowledge Graph (KG) Constructor Engine that identifies entities and narrative relationships and stores them as structured knowledge, enabling learners to explore stories through a visual story map and answer entity- and relation-based questions generated from the KG. Separately, a multilingual retrieval pipeline retrieves relevant passages from the original narratives to generate multiple-choice and open-ended comprehension questions. For open-ended questions, an LLM-as-a-Judge evaluates learners' answers against the retrieved passages and reference answers to determine correctness. The platform also incorporates Quranic content as a separate enrichment layer, allowing selected narratives to be supplemented with source-supported information. By combining structured knowledge with passage-based retrieval, ILM supports narrative exploration, comprehension, and assessment across Arabic and multilingual content. The system demonstrates the feasibility of combining structured knowledge representation and retrieval-based generation to support interactive learning of Islamic narratives. A demo is available at anonymous.4open.science/r/mml-5FCF.
