AI Readiness for Schools: A Practical Framework for Africa

Last reviewed: October 2026.

Most schools are not behind on AI. They just do not know where they stand. When school leaders ask me where to start, the answer is almost never “buy a tool”. It is: find out what you already have, what is missing, and what to do first. This page sets out the framework I use to do that, how to score a school, what the first large set of results from Ghana showed, and how to turn a score into a plan.

If you want the evidence first, read What 28 AI readiness assessments reveal about schools in Ghana, the full write-up of the results summarised below.

Why readiness is a diagnostic, not a finish line

AI readiness is not about having the latest technology. It is whether a school has the leadership, policy, staff capability, curriculum thinking and infrastructure to use AI ethically, effectively and sustainably. A readiness score tells you where you are today so you can decide where to go next.

That framing matters in African and Global South contexts. A school with intermittent internet is not “unready”. It has an infrastructure reality that shapes which AI strategies make sense. A school where no teacher has used a chatbot is not “behind”. It has a staff capacity gap that becomes the obvious first priority.

I built the framework after years of working with schools across Sub-Saharan Africa and the Middle East, because the existing tools did not fit. The CoSN Gen AI Maturity Tool is designed for US public school districts. The TeachAI Toolkit provides policy principles but no way to assess where a school stands. The EU’s SELFIE tool is excellent for digital self-assessment but 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 was built for schools that make their own decisions about AI, in places where connectivity, budgets and languages vary.

The five dimensions

A single readiness number is almost useless. What matters is the profile: where you are strong, where the gaps are, and therefore what to do first. The five dimensions work together, and strength in one cannot compensate for weakness in another.

1. Leadership and governance

Does someone own AI at your school? Is there a named person or team, and a clear process for AI decisions, or do individual teachers work it out alone? In most schools these decisions happen by default, not by design. This is where most schools should start.

2. Policy and ethics

Do you have a written AI acceptable use policy for staff, academic integrity guidelines for students, and a position on student data when teachers use AI tools? A student submits AI-generated coursework. A teacher uses AI to write report comments. A parent asks what data a tool is accessing. Can you answer these today?

3. Infrastructure and resources

This is where context matters most. The framework does not assume one device per learner or fast broadband. It asks whether connectivity reliably supports the tools you use, whether staff and students have adequate access, and whether there is a budget, even a small one, for tools and training. A school with 20 shared devices and a clear plan is in a better position than one with 200 devices and no strategy.

4. Staff capacity

Have teachers received any AI training? Do they feel confident using AI for planning and feedback, and can they critically evaluate what it produces? This is often a low-scoring dimension and the one with the highest-leverage quick wins. A 2025 Gallup and Walton Family Foundation survey found that 68% of US teachers had received no training on AI tools, and the gap is likely wider in many international school contexts.

5. Curriculum and pedagogy

Is AI addressed in your curriculum? Are students learning with AI, about AI, and to evaluate AI? That third one, critical thinking about AI outputs, is the skill students need most and are taught least. Have any assessments been redesigned for a world where students have generative AI? Small moves, such as piloting AI in one subject or redesigning one assessment, can shift this score.

How to score your school

The self-assessment has 20 questions and takes about five minutes. It produces a percentage for each dimension, an overall readiness level and a visual profile.

Score	Level	What it means
0-40%	Foundation Building	Focus on governance and policy first. The basics aren’t in place yet, but that’s fixable.
41-60%	Early Adoption	Build staff capacity while strengthening infrastructure. You’ve started, but it’s uneven.
61-80%	Active Implementation	Scale what’s working and address remaining gaps. Your foundations are solid.
81-100%	Transformation	Lead innovation and share your learning with others. You’re setting the pace.

The numbers are not precise. This is a self-assessment, not an audit. But a score creates a conversation. When a leadership team sees that governance scores well but staff capacity is low, the path forward becomes obvious. I recommend that more than one person in a school completes it: differences between the head, a deputy and a teacher are often as revealing as the score.

Take the AI Readiness Assessment, or download the framework document below.

The AI Readiness Framework for Schools Download

What 28 schools in Ghana revealed

On 21 February 2026 I ran AI readiness workshops with school leaders at Cambridge International Schools Day in Accra. Twenty-four schools completed the self-assessment, giving 28 responses in total. The full analysis is worth reading; the headlines were:

The average readiness score was 47.8%, placing the typical school in the Early Adoption tier.
82% of schools sat in the bottom two tiers: 10 in Foundation Building (36%), 13 in Early Adoption (46%), 4 in Active Implementation (14%) and 1 in Transformation (4%).
Infrastructure and Resources was the strongest dimension (58.8%) and Policy and Ethics the weakest (35.4%), a 23 percentage-point gap.
89% of schools had either Leadership and Governance or Policy and Ethics as their weakest dimension.
The highest-scoring school reached 84%, yet even it scored lowest on Policy and Ethics, at 60%.

The tools are arriving faster than the frameworks to guide their use. In the same keynote I asked a room of about 200 school leaders how many had a written AI policy; a handful of hands went up. That pattern is not unique to Ghana. Surveys in the US and England have found the same governance gap.

The sequence that works: governance before devices

The Ghana data suggests the investment sequence in many schools is backwards. Hardware is tangible, fundable and visible to parents, so it comes first. Policy work is slow and unglamorous, so it waits. But without governance, every AI tool a school deploys is an unmanaged experiment. The sequence I recommend is:

Name an owner. Designate an AI lead, even part-time, and make AI a standing item on the leadership agenda.
Write down the rules. A short acceptable use statement for staff and students, and a position on student data.
Build confidence, not compliance. Start with a voluntary exploration group and short demonstrations before a formal programme.
Redesign one assessment. Ask what is still meaningful if students have AI, pilot it, and share what you learn.
Only then scale tools, choosing ones that match your connectivity, languages and budget.

Readiness under low connectivity

The framework is designed so that a school running on mobile data hotspots can still score meaningfully on infrastructure if it is making smart decisions within its constraints. In practice that means testing tools at peak hours before committing to them, scheduling shared device time, preferring tools that work on phones, and being careful about any tool that needs always-on broadband. Readiness is about deliberate choices, not the size of the server room.

From score to a 90-day plan

A score is only useful if it leads somewhere. Take your lowest-scoring dimension and pick three low-cost moves for the next term:

Leadership and governance: designate an AI lead; add AI to leadership meetings; create a simple decision flowchart for new tools.
Policy and ethics: adapt an existing acceptable use template; run a staff session on academic integrity; audit which tools are in use and what data they access.
Infrastructure and resources: test bandwidth with AI tools at peak hours; survey device availability; keep an inventory of tools already in use.
Staff capacity: start a voluntary exploration group; share one tool per staff meeting; partner with a school that is further along.
Curriculum and pedagogy: pilot AI in one subject this term; redesign one assessment; run a student AI literacy session.

Reassess at the end of the term. The aim is not a higher number for its own sake but a profile that is less lopsided.

Readiness at system level: school groups, districts and ministries

The same five dimensions work above the school. For a school group, district or ministry, leadership and governance becomes a question of who owns AI policy across the system; policy and ethics covers data protection and procurement; infrastructure covers connectivity and where data is hosted; staff capacity becomes a professional development plan at scale; and curriculum and pedagogy covers how AI literacy and assessment are handled in national or group curricula. Running the self-assessment across many schools at once, as in Accra, gives a system a shared picture of where to invest first, rather than a league table.

For system leaders, related writing includes Building AI readiness in the Global South and AI literacy in African schools. For what national strategies and curricula actually commit to, see AI Literacy in African Schools: What Curricula Promise and What Classrooms Need. For ministries planning system-wide tools, see Sovereign AI for Education Systems: A Practical Guide for Ministries. For assessment policy and practice, see Assessment in the AI Era: Adapting the AI Assessment Scale for African Classrooms. For building staff capacity, see How to Design AI Professional Development That Changes Practice.

Working with schools and education organisations

I run this work as keynotes, 90-minute readiness workshops and leadership sessions for conferences, school groups and education organisations. An engagement can include a readiness assessment for a leadership team or conference cohort, a workshop to interpret the results and build a plan someone owns, support for moving from informal use to written guidance, teacher professional learning, and assessment redesign.

See Speaking for recent sessions and Designing Assessments with AI for the assessment strand. To discuss a session, get in touch.

AI Readiness for Schools: A Practical Framework for African and Global South Contexts