Pith Number

pith:OQ6GKRLY

pith:2026:OQ6GKRLYQMKYO2RPFAUMWNQ4PT

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Eskwai for Students: Generative AI Assistant for Legal Education in Ghana

Evan Igwilo, Evans Atompoya, Frederick Abu-Bonsrah, George Boateng, Lord Baah, Patrick Agyeman-Budu, Philemon Badu, Samuel Ansah, Victor Wumbor-Apin Kumbol

A generative AI assistant helps Ghanaian law students by answering questions using a database of local case laws and legislation.

arxiv:2605.15380 v1 · 2026-05-14 · cs.CL · cs.CY · cs.HC

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Claims

C1strongest claim

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.

C2weakest assumption

The assumption that the curated database of Ghanaian case laws and legislation is sufficiently comprehensive, accurate, and up-to-date to ground reliable answers to student questions, and that the RAG retrieval step will consistently surface the most relevant documents without introducing factual errors or omissions. This premise is invoked in the system description and deployment claims in the abstract.

C3one line summary

Eskwai for Students is a RAG-based AI system for Ghanaian legal questions, deployed over 30 months to 3.1K students who submitted 32K queries, with evaluation of helpfulness and identification of ethical concerns in usage patterns.

References

13 extracted · 13 resolved · 0 Pith anchors

[1] The Law Teacher57(3), 352–364 (2023) 2023

[2] Second Draft37, 1 (2024) 2024

[3] International Journal of the Legal Profession31(3), 323–348 (2024) 2024

[4] Jurimetrics Journal64, 111–134 (2024) 2024

[5] Law, Technology and Humans 6(3), 5–22 (2024) 2024

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Receipt and verification

First computed	2026-05-20T00:00:55.471889Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

743c6545788315876a2f2828cb361c7cd579673b6e010babd08bfc25347ea162

Aliases

arxiv: 2605.15380 · arxiv_version: 2605.15380v1 · doi: 10.48550/arxiv.2605.15380 · pith_short_12: OQ6GKRLYQMKY · pith_short_16: OQ6GKRLYQMKYO2RP · pith_short_8: OQ6GKRLY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OQ6GKRLYQMKYO2RPFAUMWNQ4PT \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 743c6545788315876a2f2828cb361c7cd579673b6e010babd08bfc25347ea162

Canonical record JSON

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    "submitted_at": "2026-05-14T20:10:32Z",
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