Artificial Intelligence and Corruption in Education in Sub-Saharan Africa: An Analysis of Its Impact and Conditions for Effectiveness. Case Studies of Mali, Senegal, Ghana, and Rwanda

L'intelligence artificielle face à la corruption dans l'éducation en Afrique subsaharienne: une analyse de son impact et de ses conditions d'efficacité. Études de cas du Mali, du Sénégal, du Ghana et du Rwanda

Lassana Toure ()
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Lassana Toure: University of Segou

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Abstract: This article explores the impact of artificial intelligence (AI) on combating corruption in the education sector in sub-Saharan Africa through a comparative study of four countries: Mali, Senegal, Ghana, and Rwanda. Using a mixed-methods approach, the study relies on rigorously operationalized indicators, including the cheating rate (detected cases of fraud), the complaints rate (administrative appeals related to examinations, scholarships, or student placements), the academic success rate, and the structure of education expenditure (as a percentage of GDP). The AI Usage Index, constructed using Internet accessibility, the Online Service Index (OSI), and the E-Participation Index (EPI), makes it possible to measure the actual use of AI systems. The findings reveal strong correlations between AI usage and the digital maturity of public services, as well as a positive association with academic performance. Attenuating effects are also observed on cheating and complaints rates. A sensitivity analysis shows that targeted increases in digital infrastructure or education budgets can strengthen these correlations. The article concludes that AI can support more transparent and equitable educational governance, provided that it is integrated into a coherent, inclusive, and sovereign institutional ecosystem.

Keywords: Intelligence artificielle Gouvernance éducative Corruption dans l'éducation Usage des données numériques Afrique subsaharienne; Intelligence artificielle; Gouvernance éducative; Corruption dans l'éducation; Usage des données numériques; Afrique subsaharienne (search for similar items in EconPapers)
Date: 2026-10-20
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