On 13 October 2026 I gave a keynote at the PAIDEIA International Conference in Buenos Aires, with UNICEF Argentina, to government representatives from all 24 of Argentina’s jurisdictions. The question behind the talk is one I hear most weeks: where should we start with AI in education?
The honest answer is that AI is already in education. Teachers, students and parents are using new tools in their own ways. Schools are building their own approaches, and governments are running pilots alongside system-wide initiatives. The question now is how to make that use useful, responsible and sustainable.
I am a strong advocate for AI in education, and I use it in my own work every week. Used well, it could help us understand each learner more deeply, give teachers targeted support and give leaders better information for decisions. The potential is greatest for learners who have not been served well by traditional methods. But none of that is guaranteed. In many cases, the evidence from classrooms simply is not there yet, although there are early signs of value in back-office work.
The PISA 2025 results point the same way. Students who used AI to draft their writing assignments did less well than those who did not. But frequent users did better when they used AI to help them learn and had also been taught to judge the quality of what it gives them. The teaching and judgement around the tool matter as much as the tool.
Here are four lessons from my work with leaders and educators at Cambridge, and what they mean for a federal system.
1. Start with the educational need
The first thing we do in the HP Cambridge Partnership for Education EdTech Fellowship is step back from the technology. Leaders arrive with limited resources and a huge, growing choice of tools. We put the brochures and demo videos to one side and ask what needs to improve in their system.
Learners needing more timely feedback, a ministry needing clearer data on student progress and teachers leaving because their days are overwhelming are different problems. Each needs its own diagnosis, including its root cause, informed by data and by the views of teachers and students.
Only then do we ask how AI could help, and whether a simpler, cheaper or more established approach would do better. Better teaching materials, a change in workflow, more professional development or stronger coordination may be the right answer. AI should earn its place. We also ask what must stay human-led: decisions about a learner’s progression, wellbeing, assessment or access to support need meaningful human responsibility.
Finally, define success before you start. Agree what evidence would lead you to continue, adapt, pause or stop, and put in place the systems to collect and review it. That gives leaders a far stronger basis for procurement than a list of features. The AI readiness framework for schools uses the same logic at school level.
2. Make pilots part of a learning system
A good pilot is small enough to reveal problems and serious enough to produce meaningful evidence. A demonstration in ideal conditions may show that a product works technically. It tells us much less about whether schools across a system can use it well. I want to see approaches tested in schools that are not yet getting strong results, that struggle with teacher retention or that serve more vulnerable communities. Otherwise we widen the gaps we are trying to close.
In one pilot we are working on, the evaluation asks whether teachers plan deliberately for the roles of the teacher, the learner and AI, and whether students develop stronger critical thinking, curiosity and metacognition. It looks at how workload changes and whether wellbeing is protected, and compares participating classes with classes continuing their current approach. Login counts and time in the system cannot answer those questions. Did learning improve? Was the use accessible? Were there differences between learner groups? What did it really cost to implement well?
Every pilot should end in a genuine choice: proceed, adapt, pause or stop. Scaling up is one possible path, not the default. Evidence should change the next decision, rather than justify the last one.
3. Treat human judgement as infrastructure
Cambridge’s Prompt the future: Humans at the heart of education argues that people bring what AI cannot: knowledge of their context, ethical responsibility, relationships, reflection and the ability to judge when the right answer is not obvious. That judgement decides where AI adds value, how its output is read and when it should not be used at all.
So the question is not only whether a tool works. It is whether the people using it have the knowledge, authority and confidence to question it, adapt its use and stay accountable for the outcome.
I saw this in the Future Educator programme with the Kuwait Foundation for the Advancement of Sciences, where Cambridge and Educate Ventures Research worked with about 50 STEM educators. Teachers moved from exploratory or efficiency-driven use of AI towards more deliberate design. One described the change as moving from curiosity to clarity. A mathematics teacher needed students to tell two concepts apart, used one AI tool to create questions and another to turn them into a game. The game was not the point. The teacher had found a precise need, chosen tools for different jobs and stayed responsible for checking the result. There is more on designing this kind of training in AI professional development that changes practice.
Closer to Argentina, Uruguay’s Plan Ceibal is a public digital ecosystem, and Cambridge is at the start of a teacher-development programme there, again with Educate Ventures Research. It is too early for results, but the design choice is worth borrowing: build teacher judgement inside a system that already exists.
Design matters just as much when students use AI. In a science task, students first draw a process in their own words. A tool may then suggest a missing step, and each student must accept or reject it and say why. If the tool writes the explanation, you have assessed the tool. If the student judges the suggestion, you have assessed the student. I write more about this in assessment in the AI era.
Cambridge’s Shaping AI Policy and Practice in English Language Teaching recommends involving teachers and learners in the design, procurement and evaluation of AI tools. We have found the same across subjects and stages. Teachers also need permission to adapt or decline a use when their judgement requires it.
4. Leaders must support, and review with teeth
If we expect people to exercise judgement, leaders must create the conditions: approved tools and uses, clear expectations, time to learn, technical support and safe routes for raising concerns. That applies to back-office staff as much as to teachers.
Review is not the last administrative step after adoption. It informs the next decision. If a supplier changes how a tool handles data, adds a new AI feature, or evidence shows it is unsafe or ineffective, institutions need the contractual and practical ability to restrict, suspend or withdraw it. Adoption should never be irreversible.
Coherence without uniformity
For a federal system like Argentina, the challenge is how jurisdictions can learn together while responding to different priorities, infrastructure and communities.
A common national spine can set out educational purpose, rights, minimum safeguards, shared competency expectations and common evidence questions. Jurisdictions then build their own pathways within it. The spine does not need to prescribe one product. A shared commitment to safe, human-led use might mean younger learners meet AI only through an adult, while older learners use approved tools under defined conditions, and higher education and vocational settings set expectations suited to their disciplines.
I have seen this work in the Digital Education Leadership Programme, co-developed by Cambridge, UNICEF and the Global Partnership for Education. It brought together more than 40 senior ministry officials and UNICEF staff from 17 countries. Delegates applied shared frameworks to their own contexts through workshops, peer exchange and a practical capstone, and kept responsibility for local adaptation. Some used total-cost-of-ownership thinking to weigh maintenance, training and infrastructure alongside the purchase price. Others strengthened monitoring and evaluation, teacher development or national roadmaps. Diversity was treated as a source of evidence, not a barrier. (More on my work on the programme.)
The goal is not identical activity. It is dependable quality, local ownership and a way for learning to travel across the system.
Three pages to start on Monday
If I were working with one jurisdiction next Monday morning, I would not start by choosing a platform. I would start with three pages:
- The educational need you will actually support this year. One need, not a catalogue.
- The boundary that stays human: assessment decisions, safeguarding and anything that changes a student’s path.
- The evidence you will look at in six months, and who has the authority to pause the work if that evidence is poor.
Those pages give jurisdictions something comparable to exchange without forcing the same implementation. Our task is not to change education in the face of AI tools. It is to shape AI around the future we want education to create.
Thank you to PAIDEIA and UNICEF Argentina for the invitation to take part in this national conversation.
