发表机构
CP2BM Lab - Hassan II University; Ibn Tofail University; Sciences Po Grenoble（哈桑二世大学CP2BM实验室; 伊本·图法伊勒大学; 格勒诺布尔政治学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在利用人工智能保护和教授希科莫里语，通过构建知识库和采用多智能体架构实现。对500个查询评估显示在词汇和语法方面性能良好，定性研究展示了能力与局限，为低资源语言开发教育工具提供蓝图。
AI 中文摘要
本文介绍了Mwando，一个旨在支持科摩罗群岛语言希科莫里语教学与保护的虚拟教育助手。该系统通过由短语、谚语、词典和语法课程构建的知识库涵盖四种主要方言变体（希恩加齐贾语、希姆瓦利语、希恩泽瓦尼语和希马奥雷语）。结合向量搜索、知识图谱和网络搜索备用方案的多智能体架构实现了准确且上下文感知的响应。对500个查询的评估表明在词汇查找和语法解释方面性能强劲，而定性案例研究展示了其能力和当前局限性。这项工作是对希科莫里语进行计算支持的初步步骤，并为开发其他低资源语言的人工智能教育工具提供了蓝图。
英文摘要
This paper presents Mwando, a virtual educational assistant designed to support the teaching and preservation of shiKomori, the language of the Comoros Islands. The system covers the four main dialectal variants (shiNgazidja, shiMwali, shiNdzuani and shiMaore) through a knowledge base constructed from phrases, proverbs, dictionaries and grammar lessons. A multi-agent architecture combining vector search, a knowledge graph and web search fallback enables accurate and context-aware responses. Evaluation on 500 queries demonstrates strong performance on vocabulary lookup and grammar explanations, while qualitative case studies illustrate both capabilities and current limitations. This work represents an initial step toward computational support for shiKomori and provides a blueprint for developing AI-powered educational tools for other low-resource languages.
