A teacher and an AI tutor after school.
A World Bank randomized trial studied a six-week, teacher-supported after-school English program in Edo State, Nigeria, where first-year senior secondary students used Microsoft Copilot powered by GPT-4.
- Maturity
- Demonstrated, stage 3 of 4
- Support
- 1 source · institution
- Evidence detail
- How we know ↓
Students need responsive practice and feedback, but individualized tutoring is often unavailable in public-school settings.
Earlier education technology evidence often came from wealthier settings or depended on specialized, costly software.
Students used a generative-AI tool for curriculum-aligned English grammar and writing work after a teacher introduced the lesson and guided the sessions.
What was shown, and what wasn’t
Shown
The randomized evaluation reported a 0.31 standard-deviation improvement on its assessment covering English topics, AI knowledge, and digital skills after the six-week intervention.
Not shown · limits
- The intervention was short and teacher-supported, so it does not establish effects of unsupervised chatbot use.
- The central assessment was designed for the study; it does not establish long-term retention or broader academic outcomes.
- Results from Edo State and English instruction may not transfer directly to other subjects, languages, or school systems.
Bright editorial interpretation
The finding concerns a designed human-and-AI learning setting in a public-school context, rather than assuming chatbot access by itself creates learning.
Still open
Do gains persist, transfer to other learning, or change school completion?
What teacher preparation and infrastructure are necessary?
How can students retain agency when the system can generate answers?
How we know1 source · checked 2026-09-07 · no corrections
Original sources
- From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria ↗ · institution
Institutions: World Bank · Edo State Secondary Education Board · SmartEdge
- Maturity
- Demonstrated
- Source published
- 2025-05-19
- Captured
- 2026-09-07
- Last source review
- 2026-09-07
- Editorial method
- AI-assisted source review
Bright compared this account with the linked original and supporting sources and kept reported, budgeted, projected, and observed claims distinct. Bright did not independently audit the underlying records.
Maturity describes the tested or operational setting. Confidence describes support for the particular claim; one does not determine the other.
Revision & correction history
2026-09-07 · The finding concerns a designed human-and-AI learning setting in a public-school context, rather than assuming chatbot access by itself creates learning.
No corrections recorded.