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.
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.
What Lefa is trying to do
Lefa AI describes itself as a national process to help South Africa make sense of what AI means for basic education, from Grade R to Grade 12, and to shape a response that is informed, collaborative and grounded in local context. Over ten months it brings together schools, communities, researchers, policymakers and organisations through rapid research, dialogue, webinars, roundtables and accessible resources. The premise on the Lefa AI site is simple: AI is moving faster than schools, systems and policy can adapt, and South Africa’s existing challenges of inequality, language diversity, teacher workload and uneven access to resources make delay expensive.
I like that framing because it does not start from a product. Most AI conversations in education begin with a tool and work backwards to a use. Lefa starts with what schools are already experiencing and asks what responsible use would look like here.
The argument I made on the panel
The point I wanted on the table early was that answers are not the same as learning. A tool that gives a learner the answer can remove exactly the effort that produces understanding. AI helps when it is designed to keep productive struggle in the task, to pitch support at the edge of what a learner can do with help, and to leave the teacher in charge of judgement about what counts as good work.
That has practical consequences for anyone designing or buying AI for schools:
- Ask what the learner still has to do. If the tool does the thinking, it is a shortcut, not a tutor.
- Ask where the teacher sees the work. Support that happens privately between a learner and a chatbot is hard to assess and easy to misuse.
- Ask what evidence exists. A good design story is useful, but it is not proof that learning improved.
The Kuwait example I used is a case in point. Teachers on the Future Educator programme applied AI inside their own subjects, which is the right design choice, but there is no published learning-outcome data yet, so I described it as a design story and nothing more.
Why the questions here are local
Language is the clearest example. South Africa has eleven official languages, and most AI models are trained mainly on English text produced elsewhere. A tool that works well for an English-medium school in a well-resourced suburb may be far less useful for a learner whose home language is isiXhosa or Sepedi. The same is true of infrastructure: a system that assumes constant broadband and a device per learner describes only part of a bimodal school system.
Teacher capacity matters just as much. Teachers are already stretched, and AI training that adds workload without removing any will not last. The most useful uses are the ones that save teachers time on preparation and assessment while keeping their professional judgement visible.
What to watch next
This was the first of three webinars in the series, and Lefa also runs roundtables and publishes research and resources as it goes. If you are working on AI in South African schools, the series is a good place to test your assumptions against what teachers, researchers and officials are actually seeing. For a practical starting point at school level, the Ghana readiness findings show the same pattern I expect here: tools arriving faster than the policies and people needed to use them well.
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.
Related resources
