arXiv — NLP / Computation & Language · July 28, 2026 · 3 min read

Mwando: Leveraging AI to Preserve and Teach shiKomori

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Computer Science > Computation and Language

arXiv:2607.23481 (cs)

[Submitted on 26 Jul 2026]

Title:Mwando: Leveraging AI to Preserve and Teach shiKomori

Authors:Naira Abdou Mohamed, Haidar Nassur Said Ali, Mohamed Hazra, Naoufal Mohamed Soibira, Roushnaty Ali Yamani

View a PDF of the paper titled Mwando: Leveraging AI to Preserve and Teach shiKomori, by Naira Abdou Mohamed and Haidar Nassur Said Ali and Mohamed Hazra and Naoufal Mohamed Soibira and Roushnaty Ali Yamani

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Abstract: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.

Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2607.23481 [cs.CL]
	(or arXiv:2607.23481v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2607.23481 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Naira Abdou Mohamed [view email]
[v1] Sun, 26 Jul 2026 06:11:11 UTC (1,425 KB)

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