arXiv：cs.CL· Suhaila Mohammed, Abdelaziz Serour, Allison Lahnala·· 3 小时前
ILM：一款 AI 驱动的故事教学工具
ILM: An AI-Powered Storytelling Educational Tool
AI 导读
ILM 是一个面向《先知故事》的交互式教育平台，结合阿拉伯语 NLP、知识图谱与基于检索的问题生成。平台由 KG Constructor Engine 识别实体与叙事关系并生成可视化故事地图，多语言检索管线则从原文提取段落生成选择题与开放式理解题，开放式答案由 LLM-as-a-Judge 对照检索段落与参考答案判定正确性。
正文
Abstract: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 this http URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12064 [cs.CL] |
| (or arXiv:2610.12064v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12064 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Allison Lahnala [view email]
[v1]
Thu, 8 Oct 2026 14:44:32 UTC (1,754 KB)
来源：arXiv：cs.CL · arxiv.org