A practical readiness framework for international schools in Sub-Saharan Africa
I spend a lot of time talking to school leaders about AI. Principals in Zimbabwe and South Africa, heads of school in Kuwait, curriculum directors in the UAE. The conversation almost always starts the same way: “We know we should be doing something. We just don’t know what.”
It’s not a lack of intelligence or ambition. These are people running complex institutions, managing multilingual communities, navigating international curricula, and doing it across infrastructure realities that range from fibre-connected campuses to schools where the Wi-Fi drops out twice a day. What they lack isn’t capability. It’s a map.
Most of the AI guidance available to schools right now was built for a different context. The CoSN Gen AI Maturity Tool – excellent work – is designed for US public school districts navigating FERPA and COPPA. The TeachAI Toolkit from ISTE and partners provides strong policy principles but no way for a school leader to assess where their institution actually stands. The EU’s SELFIE tool is the gold standard for school-level digital self-assessment, deployed in over 100,000 schools but it pre-dates generative AI and assumes a European context. The aiEDU AI Readiness Framework focuses on student and teacher competencies rather than institutional readiness.
None of these tools were designed for an international school in Sub-Saharan Africa.
That’s the gap I set out to fill.
The problem with “readiness”
Here’s what I’ve noticed in conversations with school leaders across West and East Africa: the word “readiness” itself creates anxiety. It implies a finish line, as if there’s a state of being “ready for AI” that you either are or aren’t. That framing is paralysing, especially when you’re operating with constrained budgets, variable connectivity, and a teaching staff with wildly different levels of digital confidence.
So let me reframe it. AI readiness isn’t a destination. It’s a diagnostic. It tells you where you are today, so you can make informed decisions about where to go next. A school with intermittent internet isn’t “unready”, it has an infrastructure reality that shapes which AI strategies make sense. A school where no teacher has used ChatGPT isn’t “behind”, it has a staff capacity gap that becomes the obvious first priority.
The question isn’t “are you ready?” It’s “do you know where you stand?”
Five dimensions, not one number
I built the AI Readiness Framework around five dimensions, each capturing a distinct aspect of how schools engage with AI. The idea is that a single readiness score is almost useless, what matters is the profile. Where are you strong? Where are the gaps? That’s what tells you what to do first.
1. Leadership & Governance Does someone own AI strategy at your school? Is there a designated person or team? Are AI decisions made through a clear process, or do individual teachers just figure it out on their own? This dimension asks whether leadership has created the conditions for AI to be adopted thoughtfully rather than chaotically.
2. Policy & Ethics Do you have a published AI acceptable use policy for staff? What about academic integrity guidelines for students? Is student data protection addressed when teachers use AI tools? Most schools I talk to in the region have informal norms but nothing written. That’s a risk, not because something will go wrong tomorrow, but because when it does, you want to be able to point to a framework you built deliberately.
3. Infrastructure & Resources This is where context matters most. The framework doesn’t assume 1:1 devices and gigabit internet. It asks: does your connectivity reliably support AI-powered tools? Do staff and students have adequate device access? Is there a budget, even a small one, for AI tools and training? A school running on mobile data hotspots can still score meaningfully here if they’re making smart decisions within their constraints.
4. Staff Capacity Have your teachers received any formal AI training? Do they feel confident using AI for lesson planning, feedback, or assessment? Can they critically evaluate AI outputs, spotting when ChatGPT confidently produces something inaccurate? This is often the lowest-scoring dimension in Sub-Saharan African schools, and it’s the one with the highest-leverage quick wins.
5. Curriculum & Pedagogy Is AI addressed anywhere in your curriculum? Are students learning with AI, about AI, and to evaluate AI, all three? Have your assessments been redesigned for an era where students have access to generative AI? This dimension is where the long-term work lives, but even small moves – piloting AI in one subject, redesigning one assessment – can shift the score.
Why a scored assessment matters
One of the design decisions I’m most deliberate about is that the framework produces a scored output, a percentage per dimension, an overall readiness level, and a visual profile. This isn’t because the numbers are precise (they aren’t, it’s a 20-question self-assessment, not an audit). It’s because a score creates a conversation.
When a school leader sees that their Leadership & Governance dimension scores 75% but their Staff Capacity scores 35%, something clicks. The path forward becomes obvious: you don’t need to do everything at once. You need to invest in your teachers. And that clarity, knowing what to do first, is what turns anxiety into agency.
The EU SELFIE tool taught me this. SELFIE’s power isn’t in its data precision; it’s in the conversation it generates among school leaders, teachers, and students when they compare their perspectives. I wanted to bring that same principle to AI readiness, adapted for school leaders making decisions in contexts where they often feel they’re operating without a compass.
The Sub-Saharan African context
Let me be specific about why international schools in this region need their own approach.
Infrastructure is variable, not absent. The narrative that African schools lack technology is outdated and unhelpful. Mobile internet adoption across Sub-Saharan Africa hit 46% in 2023 and is projected to exceed 70% by 2030. Many international schools in Accra, Lagos, and Nairobi have solid connectivity. Others don’t. A useful framework accounts for both without treating constrained infrastructure as failure.
The talent pipeline is real but undertrained. Only 24% of secondary school teachers in the region have been trained to use digital tools in their teaching. But that’s a training gap, not a talent gap. The teachers I’ve worked with in West Africa are resourceful, creative, and hungry to learn. They need structured support, not condescension.
Governance is autonomous. International schools make their own decisions. They’re not waiting for a ministry directive on AI policy. This is both an advantage (you can move fast) and a risk (you can move fast in the wrong direction). A readiness framework gives school boards and leadership teams the structured thinking they need to make deliberate choices.
Data protection is evolving. Many countries in the region lack comprehensive data protection legislation. That doesn’t mean schools can ignore the issue, it means they need to build their own ethical frameworks, often ahead of national regulation. The Policy & Ethics dimension of the framework addresses this directly.
The decolonial question is real. Most AI tools are English-first, trained on Global North data, and embed assumptions about what “good education” looks like. A school leader in Accra adopting AI without thinking critically about these dynamics risks importing a worldview along with the technology. I don’t claim the framework solves this but it makes the question visible by asking whether AI is transforming pedagogy rather than just automating existing practice.
What to do on Monday
If you’re a school leader reading this and thinking “right, so where do I start?”, here’s the practical version.
If you haven’t done anything yet: Start with governance. Designate someone, even as a part-time responsibility, to own AI strategy. Add AI as a standing item on your leadership meeting agenda. Create a simple decision-making process for when a teacher or department wants to adopt a new AI tool. You can do all three of these within two weeks, with no budget.
If you’ve started but feel scattered: Run the assessment. Get your five-dimension profile. Look at your lowest-scoring dimension and pick one quick win from it. Share it with your leadership team. This turns “we should probably do something” into “here’s what we’re doing and why.”
If you’re further along: Look at your curriculum and assessment practices. Are your assessments still designed for a world where students couldn’t access generative AI? That’s your strategic priority, and it’s where the real transformation happens.
Putting it into practice
I’m presenting this framework at Cambridge International Schools Day in Accra later this month, a conference for Cambridge school leaders from across Ghana and West Africa. In the breakout session, every participant will complete the assessment on their phone or laptop, see their school’s readiness profile, and build an action plan with specific milestones before they leave the room.
It’s the kind of audience I built this for: experienced school leaders, diverse contexts, real constraints, and zero patience for theory that doesn’t translate into Monday morning decisions. I’ll share what comes out of it.
Niall McNulty is AI Product Lead at Cambridge University Press & Assessment and a researcher in educational technology at the University of Cape Town. He works on AI in education across Sub-Saharan Africa and the Middle East. The AI Readiness Framework is being developed as an interactive self-assessment tool and a detailed framework document.
Get in touch: linkedin.com/in/niallmcnulty/