Eskwai for Students: Generative AI Assistant for Legal Education in Ghana
This study introduces Eskwai for Students, a retrieval-augmented generation (RAG) system designed to assist Ghanaian law students with legal education. Leveraging a curated database of over 12K case laws and 1.4K legislation, the system was deployed in a 30-month longitudinal study with 3.1K students making 32K queries. The evaluation assessed its helpfulness and analyzed query types, raising ethical concerns.
[2605.15380] Eskwai for Students: Generative AI Assistant for Legal Education in Ghana
[Submitted on 14 May 2026]
Title:Eskwai for Students: Generative AI Assistant for Legal Education in Ghana
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Abstract:Recent advances in generative AI have shown their potential to be leveraged for legal education. Yet, work on the development and deployment of such systems for legal education in the Global South is limited. In this work, we developed Eskwai for Students, a generative AI assistant to help law students with their legal education. Eskwai for Students is a retrieval augmented generation (RAG) system that provides answers to a wide range of legal questions for law students grounded in a curated database of over 12K case laws and 1.4K legislation in Ghana. We deployed Eskwai for Students in a longitudinal study of 30 months (2.5 years) used by 3.1K law students in Ghana who made 32K queries. We evaluated the helpfulness of our AI, and provided insight into the kinds of queries law students submit to this generative AI tool, which raises some ethical concerns. This work contributes to an understanding of how law students in the Global South are using generative AI for their studies and the ways it could be leveraged responsibly to advance legal education.
Comments: 10 pages. Accepted at the 27th International Conference on Artificial Intelligence in Education (AIED 2026)
Subjects:
Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2605.15380 [cs.CL]
(or arXiv:2605.15380v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.15380
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: George Boateng [view email] [v1] Thu, 14 May 2026 20:10:32 UTC (2,088 KB)
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