Picture an ordinary Tuesday morning in 2035. Your child is no longer a child. They are starting a career, running a small business, studying at university, or perhaps doing a mix of all three. Before breakfast, an AI agent has already summarized overnight messages, compared several options for a project, drafted a presentation, translated a conversation, checked a budget and generated working software from a short description.
None of this feels futuristic to them. It feels normal.
The interesting question is not whether artificial intelligence will be present in their life. It almost certainly will be. The more important question for parents is what will still be valuable when powerful AI is available to almost everyone.
That question changes how we should think about education today.
The skills market may move faster than the school system
We do not need science-fiction predictions to see the direction of travel. The World Economic Forum’s Future of Jobs Report 2025 estimates that 39% of workers’ existing skill sets will be transformed or become outdated between 2025 and 2030. Technology skills are rising quickly, but so are distinctly human capabilities such as creative thinking, resilience, flexibility, leadership and social influence.
For a child who is nine today, 2035 is not a distant abstraction. It is roughly the point at which university choices, first serious work, entrepreneurship and adult independence begin to matter. The tools they will use then may be as different from today’s tools as today’s smartphones are from the internet of the early 2000s.
Yet education is unusually slow to change. That is understandable. National curricula, assessment systems, teacher training and textbooks cannot be rebuilt every time a new technology appears. But AI is not simply another classroom tool. It is increasingly capable of doing the same kinds of cognitive tasks that schools have spent decades training students to perform.
If a machine can produce a competent essay, write working code, explain a physics problem, summarize a book and generate a business plan in seconds, then being able to reproduce those outputs is no longer the whole goal. Knowing when the output is wrong, what question is worth asking, what should be built, and whether the result is actually useful becomes far more important.
What becomes cheaper when everyone has an AI?
Imagine two young adults applying for the same opportunity in 2035. Both have access to excellent AI. Both can generate a polished document, analyze a spreadsheet and create a website. The difference between them is unlikely to be who can type the best prompt.
The stronger candidate may be the one who can define the problem before asking AI to solve it. They can tell weak evidence from strong evidence. They notice what a customer, employer or community actually needs. They can make a decision when the answer is uncertain. They can explain their reasoning to another person. They can combine several tools into something useful. And when the first attempt fails, they know how to learn what they are missing and try again.
These are not anti-technology skills. They are the skills that make technology valuable.
The uncomfortable implication is that some activities schools traditionally reward may become less differentiating. Memorizing information still matters because knowledge gives us context and judgment. Mathematics still matters. Writing still matters. Science still matters. But the value shifts from merely producing the expected answer toward understanding, evaluating, applying and creating with what you know.
The future is not “less learning.” It is deeper learning.
This is also where the education debate can go wrong. Preparing children for AI does not mean handing them a chatbot and abandoning fundamentals. In June 2026, the OECD and European Commission published an AI literacy framework for primary and secondary education that defines AI literacy around technical knowledge, durable skills and future-ready attitudes. It emphasizes understanding AI, critically evaluating its outputs, using it creatively and ethically, and being able to engage with and shape AI systems rather than simply consume them.
That is a useful distinction. A child who lets AI think for them may become less capable. A child who learns to think with AI — while remaining responsible for the judgment — can become much more capable.
The goal should not be to protect children from the tools they will eventually use. Nor should it be to automate childhood. The goal is to make sure the child remains the thinker in the room.
What should a 2035-ready education actually teach?
A future-ready curriculum still needs mathematics, languages, science, history and the other subjects that create a foundation for understanding the world. But it should deliberately build a second layer of capabilities that are harder to automate and useful across many possible futures.
Children should repeatedly practice how to teach themselves something new. They should learn how to find information, distinguish a reliable source from a persuasive-looking bad one, and turn research into knowledge they can actually use. They should work through messy problems where no answer is provided at the back of the book. They should make things — explanations, experiments, products, stories, small businesses, software, designs or solutions — and then judge whether those things are any good.
They should also learn to understand people. What does someone need? Why would they choose one solution over another? Can you communicate an idea clearly? Can you persuade without misleading? Can you collaborate with people whose strengths are different from yours?
And they should become comfortable with AI as an amplifier. Not a machine that completes the task while they watch, but a tool they can question, direct, challenge, combine with other tools and verify.
Personalization matters more when the destination keeps moving
There is another problem with preparing every child for the same future: there probably will not be one future. One student may become fascinated by biotechnology, another by game design, another by construction, another by entrepreneurship, another by art. AI is likely to affect all of those fields differently.
That makes personalized learning more than a convenience. A strong learning system should notice what a child already cares about, use those interests to pull them deeper into core subjects, expose them to new areas, and increase the difficulty as they become ready for more. The objective is not to lock children into an early specialization. It is to make them unusually good at learning.
The child who can learn a new tool, a new domain or a new way of working without waiting for someone to design a course for them has an advantage in almost any version of 2035.
A new generation of schools is being built around that assumption
This is the idea behind Future School, an AI-native learning program for children ages 5 to 18. It covers the subjects expected for each child’s age and school year, but the program is built around a broader premise: children also need to learn how to teach themselves, work through unfamiliar problems and make things that other people actually value.
Each child gets an individual path that can adapt to age, pace, languages and interests. Lessons can use topics the child already likes — games, football, animals, drawing or something entirely different — while still moving through the academic material they need. Parents can see daily marks, read the actual lessons and receive a weekly summary of where the child is doing well and what should come next.
It is designed to fit around conventional school for families that want an additional daily tutor, while homeschool families can use it for a much larger share of the learning day. One unusually practical feature is that correct work can earn screen time, turning something many parents already negotiate about into an incentive attached to learning.
More important than any single feature, though, is the direction: education built for a world in which AI is assumed to keep improving rather than treated as a temporary classroom disruption.
Parents do not need to predict 2035 correctly
No one knows exactly what AI will be able to do nine years from now. Some predictions will prove exaggerated. Others will probably look conservative in hindsight. Entire professions may change less than expected, while new kinds of work we do not yet have names for may become normal.
That uncertainty is not a reason to ignore the question. It is the reason to focus on skills that survive many different answers.
A child who can think critically, learn independently, communicate clearly, understand people, use powerful tools without surrendering judgment, and turn ideas into useful outcomes is not being trained for one particular job. They are being prepared to adapt when the jobs change.
For parents, that may be the more useful benchmark for education over the next decade. Instead of asking only whether a child is keeping up with today’s curriculum, ask a second question: are they becoming more capable of handling a world that will not stand still?
The answer does not require replacing everything children learn today. It requires adding what tomorrow will demand. Parents who want to see what that can look like in practice can explore future-ready education for the AI era and let their child try the approach for themselves.
