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Good practice Imported

Rwanda National Day of AI — cascading AI teacher training across all 30 districts

Rwanda · Kigali · See the Rwanda profile · See the Kigali profile

Evidence: Descriptive / self-reported Top 28% 60/100 · Ask Evidence Copilot about this practice

Rwanda's MINEDUC and REB partnered with Day of AI (MIT RAISE) to train 150 AI champions who cascaded training to 5,000+ teachers across all 30 districts by December 2025; complemented by Africa's first national AI education cloud and an Anthropic/ALX learning companion.

150 teachers
AI champion master teachers trained directly by MIT RAISE (2025)
5,000+ teachers
Teachers reached via cascading training (by December 2025)
30 districts (all of Rwanda)
Districts covered (first training round, by December 2025)
up to 2,000 people
Additional teachers/civil servants enabled for AI training via Anthropic/ALX partnership (ongoing)
Rwanda National Day of AI — cascading AI teacher training across all 30 districts

Details

Maturity
Scaling
Promoter
Rwanda Ministry of Education (MINEDUC) / Rwanda Education Board / Day of AI (MIT RAISE)
Period
2025–
Keywords
government, education, teacher-training, ai-literacy

Context

Rwanda's Ministry of Education (MINEDUC) and Rwanda Education Board (REB) partnered with Day of AI and MIT RAISE to launch a national AI teacher-training programme in July 2025, using an explicitly cascading model.

Objectives

The programme aims to achieve system-wide AI literacy training for teachers, enabling them to explain AI concepts, integrate AI tools responsibly into lesson design, and guide students to build AI solutions addressing community challenges.

Activities

150 master teachers ('AI champions') were trained directly by MIT RAISE researchers, then fanned out across all 30 districts of Rwanda to train fellow educators, using Day of AI's MIT RAISE curriculum adapted for the Rwandan context. A complementary Anthropic–Government of Rwanda–ALX partnership deploys Chidi, an AI learning companion built on Claude, with Rwanda's education and ICT ministries enabling AI training for up to 2,000 additional teachers and civil servants. Rwanda is also preparing Africa's first AI-optimised national teaching and learning cloud, developed with U.S. ed-tech partners, capable of personalising instruction in Kinyarwanda and English.

Results

By December 2025, the first round of cascading training had reached more than 5,000 teachers, making Rwanda one of the first countries globally to achieve system-wide AI literacy training for teachers.

Conclusions

The policy framework — the National AI Policy (2023) and the Foundational Learning Strategy (2024–2029) — provides sustained institutional backing, but student-facing learning-outcome data from the trained cohort have not yet been published.

Implementation
Indicative cost
High (€500k–€5M) — Delivered via partnerships with MIT RAISE, Anthropic and ALX and U.S. ed-tech partners for the national AI education cloud; no public cost figures found in source.
Time to results
Long (> 3 years) — Programme launched July 2025; first training round completed by December 2025; national AI education cloud and further teacher training planned as next phases, within the 2024–2029 Foundational Learning Strategy horizon.
Staffing & skills
150 'AI champion' master teachers trained directly by MIT RAISE researchers, who then trained fellow educators, MINEDUC and Rwanda Education Board (REB) as programme owners, Anthropic and ALX as partners for the Chidi AI learning companion

Conditions for success

  • Cascading train-the-trainer model enabling rapid national coverage across all 30 districts
  • Curriculum grounded in Day of AI (MIT RAISE) content, adapted for the Rwandan context
  • Sustained policy backing via the National AI Policy (2023) and Foundational Learning Strategy (2024–2029)
  • Planned localisation of the national AI education cloud in Kinyarwanda and English

Common failure modes

  • No student-facing learning-outcome data has been published yet — the available evidence concerns teacher training completion, not pupil attainment
  • No independent ethics review or data-governance framework for the classroom AI tools has been published

Where it fits

Governance type
national ministry of education with international NGO/tech partners
Scale
national (all 30 districts)
Income level
low-income (Rwanda)

Commonly funded by

National / regional programmes

Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.

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Data sources

Where this practice's information was retrieved from, and when.

Attachments

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