Eskwai for Students: Generative AI Assistant for Legal Education in Ghana
arXiv cs.CL·George Boateng, Philemon Badu, Patrick Agyeman-Budu, Samuel Ansah, Evans Atompoya, Evan Igwilo, Lord Baah, Frederick Abu-Bonsrah, Victor Wumbor-Apin Kumbol
4d agoQuick Take
Eskwai for Students is a generative AI tool enhancing legal education for Ghanaian law students.
Key Points
- Developed as a retrieval augmented generation system.
- Utilized by 3.1K students over 30 months.
- Raises ethical concerns regarding AI use in education.
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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