This study evaluates the impact of a program leveraging large language models (LLMs) for virtual tutoring in secondary education in Nigeria. Using a randomized controlled trial, the program deployed Microsoft Copilot (powered by GPT-4) to support first-year senior secondary students in English language learning over six weeks. The intervention demonstrated significant improvements of 0.31 standard deviations on an assessment that included English topics aligned with the Nigerian curriculum, knowledge of artificial intelligence and digital skills. The effect on English, the main outcome of interest, was of 0.23 standard deviations. Cost-effectiveness analysis revealed substantial learning gains, equating to 1.5 to 2 years of 'business-as-usual' schooling, situating the intervention among some of the most cost-effective programs to improve learning outcomes. An analysis of heterogeneous effects shows that while the program benefits students across the baseline ability distribution, the largest effects are for female students, and those with higher initial academic performance. The findings highlight that AI-powered tutoring, when designed and used properly, can have transformative impacts in the education sector in low-resource settings.
| Repository name | URI |
|---|---|
| Reproducible Research Repository (World Bank) | https://reproducibility.worldbank.org |
Paper exhibits were reproduced on a computer with the following specifications:
• OS: Windows 11 Enterprise, version 24H2
• Processor: Intel(R) Core(TM) Ultra 7 165U (2.10 GHz)
• Memory available: 32.0 GB
Runtime: 5 minutes
Data is forthcoming in the World Bank Microdata Library and included in the reproducibility package.
| Author | Affiliation | |
|---|---|---|
| Martin Elias De Simone | World Bank | mdesimone@worldbank.org |
| Federico Hernan Tiberti | World Bank | ftiberti@worldbank.org |
| Maria Barron Rodriguez | World Bank | mbarronrodriguez@worldbank.org |
| Federico Manolio | World Bank | fmanolio@worldbank.org |
| Wuraola Mosuro | World Bank | wmosuro@worldbank.org |
| Eliot Jolomi Dikoru | World Bank | edikoru@worldbank.org |
2025-12-05
| Location | Code |
|---|---|
| Nigeria | NGA |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
| Name | URI |
|---|---|
| Modified BSD3 | https://opensource.org/license/bsd-3-clause/ |
| Name | Affiliation | |
|---|---|---|
| Martin Elias De Simone | World Bank | mdesimone@worldbank.org |
| Reproducibility WBG | World Bank | reproducibility@worldbank.org |
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Reproducibility WBG | DECDI | World Bank - Development Impact Department | Verification and preparation of metadata |
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