Dar es Salaam, June 2025 — a pivot in policy and practice
When UNESCO officially handed Tanzania its national AI Readiness Assessment during the Africa Internet Governance Forum in Dar es Salaam in June 2025, it was more than a report drop: it was an explicit invitation to move from experimentation to national strategy. The assessment laid out where Tanzania stood on compute, data governance, capacity and ethics, and it mapped concrete steps for integrating artificial intelligence into public services — education foremost among them. The moment captured how quickly debates about AI in African classrooms have moved from isolated pilots to high-level policy decisions with funding and training attached.
National strategy, national guidelines
That shift is visible on government platforms. The Tanzanian Ministry of Education, Science and Technology released a National Digital Education Strategy (2025–2030) and accompanying National Guidelines for Artificial Intelligence in Education that commit the state to expand ICT infrastructure, build teacher competencies and require institutions to develop their own AI-use frameworks. The strategy spells out pragmatic measures: digital content aligned with national curricula, capacity-building for teachers and administrators, and safeguards around data and student privacy. These documents anchor the country’s broad digital ambitions — from primary schools to universities — and explicitly situate AI as a tool for personalised learning and administrative efficiency rather than a replacement for teachers.
From policy to classroom: a staged rollout
Homegrown edtech and low‑tech realities
Ubongo, a Dar es Salaam–based edutainment organisation, has scaled learning-focused radio and TV programmes and interactive SMS activities across East Africa; its track record in reaching children who lack broadband gives it credibility as a partner for national rollouts. Platforms that minimise bandwidth — SMS, USSD, radio and TV broadcast — remain essential because large segments of students access learning on feature phones or shared devices. Similarly, hardware projects that package connectivity, storage and local servers into rugged kits have demonstrated how to bring digital lessons to off-grid schools. Those low‑tech design choices make the difference between pilots that stay in urban labs and systems that can work at national scale.
Skills, teachers and the long arc of training
At the heart of Tanzania’s approach is the classroom teacher. UNESCO and national partners are supporting projects to develop teacher digital competencies and to contextualise international AI competency standards for local curricula and teaching practice. These initiatives recognise that introducing AI‑driven tools without equipping teachers to interpret analytics, adapt content and preserve critical pedagogy would risk hollowing out learning rather than improving it. The training programs emphasise not only how to use apps and platforms, but how to read learning-data dashboards, design adaptive lesson sequences and ensure assistive technologies actually expand access for marginalised learners.
Ethics, language and cultural relevance
Tanzania’s guidelines do not treat ethics as an afterthought. UNESCO and African regional policy work has emphasised human‑centred AI, and local debates have highlighted the cultural costs of importing one-size-fits-all learning models. African scholars and practitioners caution that AI systems trained on data from the Global North can erase or misrepresent local knowledge, and that algorithmic choices embed values as much as they encode utility. Preserving language diversity and cultural context — Swahili and dozens of local languages — is therefore an explicit policy objective. Officials and civil society actors argue that AI in education must surface local narratives and pedagogies, not replace them.
Continental alignment and compute constraints
Tanzania’s moves occur against a continental push to make AI an engine of development. The African Union adopted a Continental AI Strategy in 2024 and has since encouraged member states to harmonise regulation, invest in regional compute hubs and prioritise skills and data sovereignty. The AU’s agenda matters for Tanzania because the most pragmatic route to locally relevant AI — shared datasets, regional model training, common governance standards — requires cross‑border cooperation and pooled investment. Yet Africa’s limited share of global AI compute and talent means that deliberate choices are needed about where to build capacity, who controls models and how benefits are distributed.
Barriers that remain
- Infrastructure: power and reliable broadband are still uneven — without consistent connectivity, many AI learning tools will remain aspirational.
- Affordability and devices: many learners still rely on shared phones or community radios; expensive tablets or cloud-only services risk widening inequality.
- Data governance: collecting and using pupil data at scale requires clear rules, local oversight and trustworthy storage to avoid misuse.
- Teacher workload and incentives: uptake depends on practical workflows — teachers must see immediate classroom gains rather than additional administrative burden.
Local pilots and technology choices — favouring SMS, broadcast and on-premise servers — are practical responses to these constraints, but the scale-up will require coordinated investment and long-term maintenance budgets.
Why Tanzania matters for Africa’s skilling ambitions
Tanzania is not a special case so much as an early adapter whose choices will ripple across the region. If a country with diverse geographies and infrastructure challenges can stitch together teacher training, low‑bandwidth content, clear governance and regional cooperation, it will demonstrate a path other countries can follow. Conversely, rushed deployments that ignore language, data privacy or teacher agency could produce superficial gains that collapse once pilot funding ends. The AU’s strategy and UNESCO’s technical support create an enabling environment; the rest depends on implementation discipline — training, procurement practices, and sustained financing.
What comes next
Over the next 18 months the key observable markers will be: the rate at which teacher upskilling programs complete their first cohorts; concrete procurements for hybrid offline/online infrastructure in rural districts; the publication of institutional AI-use frameworks by schools and universities; and whether regional compute or data‑sharing hubs begin to materialise under AU coordination. Donors, private partners and governments are lining up project funding now; the critical test will be whether that money is channelled into durable systems — local content creation, open‑source tools and teacher support — rather than discrete technology buys.
The stakes are high. For a continent where a majority of the population is under 25, the promise of AI in education is not abstract: it is a practical lever for skilling, employment and entrepreneurship. Tanzania’s experiment — whether it proves resilient or reveals new pitfalls — will influence how the next generation learns to work with intelligent systems, and whether AI becomes a tool for widening opportunity or widening inequality.
Sources
- UNESCO — AI Readiness Assessment and AI in Education project materials
- African Union — Continental Artificial Intelligence Strategy (2024)
- Tanzania Ministry of Education, Science and Technology — National Digital Education Strategy 2025–2030 and National Guidelines for AI in Education
- UNESCO Core project record: Strengthening Teachers' Digital Competencies and AI integration in Tanzania
- University of Dar es Salaam — reporting and research on digital transformation and AI in Tanzanian education
