Beyond Hype and Skepticism: AI in African Education

This post reflects themes from my article “Beyond skepticism: Question marks surrounding AI and AIED policies in Africa” in Computers and Education Open. It is a short, plain-language companion to that work.

Conversations about artificial intelligence in African education tend to collapse into one of two postures. The first is hype: AI will leapfrog broken systems, personalise learning for everyone, and solve teacher shortages at a stroke. The second is skepticism: AI is a distraction imported from elsewhere, unsuited to local realities and likely to widen the gaps it claims to close.

Both postures share a flaw — they answer the question in general, when the honest answers are all specific.

The question marks that matter

Rather than asking “is AI good or bad for African education?”, it is more useful to ask a series of concrete questions that policy has to face:

• Whose data, whose infrastructure? AIED assumes connectivity, devices, and data pipelines that many institutions do not have — and cannot assume will arrive on the timeline the technology promises.
• Readiness for what? A system can be declared “AI-ready” while lacking the everyday conditions — reliable power, trained staff, maintenance budgets — that turn readiness into use.
• Policy for whom? Borrowed policy frameworks encode assumptions about institutions and learners that may not hold locally.

A third path

The alternative to hype and skepticism is not a compromise between them. It is a commitment to context: policy that is explicit about local constraints, honest about what AI can and cannot do under those constraints, and evaluated against outcomes that matter to the systems adopting it. That is the direction my research on AI adoption and readiness in higher education tries to make concrete.