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AI literacy in African schools: from policy ambition to classroom learning

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Ghana’s proposed primary curriculum offers a useful case for considering what children should learn about AI, how schools should assess it, and what teachers need to make it work.

A curriculum commitment worth examining

Artificial intelligence literacy deserves a place in school education. Its value depends on whether it helps children understand technology, make informed decisions and take responsibility for their work. Curriculum policy should make those capabilities visible in what pupils learn and how their learning is assessed.

Ghana offers a concrete starting point. In July 2026, its curriculum review committee presented proposed revisions for kindergarten to Primary Six that explicitly included AI literacy, computational thinking, coding and digital citizenship. The direction is reinforced by Ghana’s National AI Strategy 2025-2035, which calls for primary-level data science and coding, practical use of existing AI, ethics education and teacher training.

There is an important qualification. An Education Ministry statement reported on 10 August said the revised curriculum was still undergoing review before formal adoption. The syllabus documents examined here are drafts. They show an intended direction, not evidence of completed implementation.

What Ghana’s drafts make visible

The Computing draft proposes a progression across primary school. Basic 1, the first year of primary school, introduces smart devices and technologies that respond automatically. Basic 2 explicitly introduces AI-enabled tools. Later years address benefits and limitations, ethical use and practical application. By Basic 6, learners are expected to evaluate AI-generated outputs and use AI in projects and problem-solving.

This progression matters because recognising a tool, using it and evaluating its output demand different kinds of understanding. A pupil who can obtain an answer from a chatbot has not necessarily learnt how to judge that answer.

The accompanying Citizenship Education draft develops related learning about privacy, misinformation, digital footprints and verification. Those connections provide a basis for teaching AI within the wider responsibilities of digital participation.

There is also a continental policy connection. The African Union’s Continental AI Strategy includes action on AI skills in schools and on information integrity, media and information literacy. Ghana is therefore a useful national example within a broader agenda. It should not be treated as representative of every African education system, or as proof that a single model will work everywhere.

A practical definition of AI literacy

For school purposes, AI literacy should include a basic understanding of how systems work, sufficient to question their outputs and recognise their limits. Children can explore how some systems learn patterns from examples, how the examples selected affect results, and why an automatic response should not be confused with human understanding.

Practical use belongs within that learning. Pupils should be able to explain why they chose an AI tool, what assistance it provided and what they still needed to do themselves. Evaluation should include checking claims against evidence, identifying relevant omissions and deciding whether an output is suitable for the task.

Responsible participation needs equally concrete treatment: protecting personal information, acknowledging assistance and considering how generated content might affect other people. These are proposed teaching priorities. Ghana’s strategy provides supporting direction through its commitments to data protection, cybersecurity, fairness, cultural protection and wider AI ethics.

There should also be room for a justified decision not to use AI. A learning task may require independent practice, personal expression or reasoning that the pupil needs to develop. Choosing the appropriate level of assistance is part of the competence.

Make judgement visible in assessment

Consider an illustrative upper-primary activity. A teacher provides a short AI-generated account of a local river alongside an appropriate textbook passage or other trusted reference. Pupils identify checkable claims, compare the sources and explain which parts of the generated account they would retain, correct or question.

The teacher assesses the quality of the checking and the reasons given. Pupils could then produce their own account, explaining any assistance used. The activity brings reading comprehension and source evaluation into the same task as AI literacy.

It can be undertaken using printed, teacher-prepared materials, without requiring pupils to open personal chatbot accounts. Ghana’s Citizenship Education draft explicitly allows digital citizenship teaching without actual devices or internet access. That provision does not remove the need for practical technology experience, but it gives teachers an entry point for developing judgement before direct tool use.

The example is a proposed activity, not a reported classroom trial. Its effectiveness would need to be tested rather than assumed.

Protect foundational learning

A serious objection is that schools already face pressing literacy, numeracy and curriculum demands. Ghana’s own review was prompted partly by concerns about foundational learning and teacher feedback on overload. Adding AI content without making choices about time and teaching could deepen those problems.

The response should be selective integration. An AI-related task should have a clear subject-learning purpose. Checking a numerical answer should require pupils to use a method they have been taught. Reviewing a generated passage should involve understanding its meaning and evidence. Where the activity distracts from those goals, it should be changed or left out.

Integration still requires explicit teaching about AI. Otherwise, children may practise reading or calculation without learning why an AI system produced a particular response, what its limits are or how to use it responsibly. Curriculum designers need to identify what is specific to AI and where existing subjects can support it.

Give teachers the means to teach it

Teacher preparation is central to this proposal. Ghana’s curriculum review recommended phased, adequately resourced training, and its AI strategy calls for work with Ghana Education Service ICT coordinators to train teachers.

The practical requirement is broader than a demonstration of popular tools. Teachers need examples linked to curriculum outcomes, assessment criteria, guidance on pupil data and clarity about which activities are appropriate for their learners. Resources should accommodate different levels of connectivity and allow local adaptation.

Local relevance must also be taken seriously. Ghana’s strategy launch emphasised systems that reflect Ghanaian languages and cultural contexts. For learning materials, that means examining whose language, knowledge and experience an example represents, rather than assuming that an imported output is suitable because it is fluent.

The policy ask is therefore specific: define an age-appropriate progression, fund teacher development alongside it, and assess what pupils understand and can justify. Ghana’s proposed curriculum provides material for that discussion. The next task is to test how those intentions work in classrooms, revise what proves unhelpful and protect the subject learning they are meant to support.

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ChatGPT won’t fix education systems. But it has become shorthand for AI.

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ChatGPT is a useful starting point for a conversation about AI in education. It gives people something concrete to try: ask a question, draft an explanation, adapt a resource. The difficulty comes when that experience becomes the basis for an institutional strategy.

For a ministry, education publisher or NGO, the question is how those capabilities fit into the work of delivering education. Who approves a generated resource? What information can staff share with a provider? How will a translated explanation be checked? What happens when a funded pilot ends?

My view is that ChatGPT-class tools can make useful contributions across that work. But the programme around them needs as much attention as the technology. Without it, institutions risk expanding production faster than their capacity to check what they produce.

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What AI means for Mzansi’s classrooms

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On 10 September 2026 I joined the first of three national webinars in Human Studios’ Lefa AI series, What AI Means for Mzansi’s Classrooms. The session was Navigating AI, Learning and Safety in South African Basic Education. Lefa is a ten-month national initiative convened by Human Studios, with UNICEF South Africa and the DG Murray Trust. It is a sense-making process, not an adoption campaign: research, dialogue and practical conversation while South African schools are already using AI, and while national guidelines are still being written.

Niall McNulty speaking on the Lefa AI webinar, What AI Means for Mzansi's Classrooms, September 2026
Still from the Lefa AI webinar, 10 September 2026.

I was a panellist on the AI and learning conversation. Shirley Eadie facilitated. My contribution was practical examples, with a limit I wanted on the table early: AI does not improve learning because it provides answers. It helps when it is designed to preserve productive struggle, work within a learner’s zone of proximal development, and leave teachers in charge of judgement. One public example was Cambridge’s Future Educator programme with KFAS in Kuwait, where STEM teachers applied AI through their own subjects rather than sitting through a tour of tools. That programme has no published learning-outcome data, so I offered it as a design story, not as proof of attainment.

Lefa is worth paying attention to because the South African questions are local: eleven official languages, a bimodal school system, teacher capacity that is already uneven, and tools trained somewhere else. If you work in a school, a ministry or an education organisation here, start with the Lefa AI site.

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