As artificial intelligence becomes increasingly embedded in everyday life and work, the conversation around its role in education is shifting. While much attention has focused on how AI can support teachers and reduce workload, a bigger question is emerging: should we be teaching students how to understand and use AI themselves? This blog explores why AI literacy matters — and what it could mean for the future of the UK curriculum.
What is AI Literacy?
Artificial intelligence is becoming a regular feature of everyday life. Whether it’s asking a voice assistant for help, using AI-powered apps to write emails, or relying on algorithms to suggest music, directions, or even news — AI is everywhere. At the same time, we’re seeing headlines about AI replacing jobs in industries ranging from retail to finance to design.
This shift raises an important question: Are we equipping young people with the skills they need to thrive in an AI-driven world? It may be time to rethink what we’re teaching in schools — not just in terms of tools, but in how we prepare students for future careers shaped by automation and technology.
That’s where AI literacy comes in.
AI literacy is about more than just using artificial intelligence. Iit’s about understanding how it works, how to use it responsibly, and how to question and monitor its outputs. Teaching AI literacy means helping students recognise where AI is being used, assess the quality of its decisions, and consider its ethical and societal implications.
With AI literacy, students gain the confidence and critical thinking skills needed to navigate a world where AI is not just a tool, but a central part of how we live, work, and learn.
Rethinking Education for an AI-Driven World
Across every subject area, students are being taught skills and methods that artificial intelligence can now often perform more efficiently. From essay writing to problem-solving and even creative thinking, many of the traditional cognitive abilities once seen as uniquely human are being replicated — and in some cases outperformed — by AI systems.
This shift poses a critical question: Are we preparing young people for the world as it is, or the world as it’s becoming?
As AI becomes embedded in everyday work and life, some academics are proposing that is now not enough to teach students how to do things the way they’ve always been done. Instead, we need to consider whether school curricula should be adapted to prioritise AI literacy — giving students the understanding, skills, and critical perspective they need to live and work alongside these powerful technologies.
Economist Daniel Susskind argues that up to a third of education time — at both school and university — should be devoted to teaching students how to use AI tools effectively. He stresses that this should go beyond simply learning how to prompt tools like ChatGPT. Students need to understand how AI systems work, their limitations, and their potential risks. Without this deeper knowledge, they risk becoming passive users of technology, rather than active participants in shaping its future.
Much of the existing curriculum is still focused on developing cognitive skills like logical reasoning, critical thinking, and written communication. But as AI continues to demonstrate its ability in these areas — and even in creativity, once thought to be a human-only domain — we must ask whether it’s time to shift focus.
Susskind suggests that the real innovation of the future will come not just from humans, but from AI-driven collaboration and machine-led discovery.
We are in the early stages of an AI revolution. Already, entry-level graduate jobs are being affected by automation, and forecasts suggest that the job market will undergo significant restructuring in the coming years. Some industries will evolve, others may disappear entirely.
Perhaps if we want young people to succeed in this changing landscape, we need to give them more than just traditional knowledge — there needs to be an education shift towards AI literacy, future-focused skills, and the confidence to adapt.
So What Could a New AI Literacy Curriculum Look Like?
The current UK curriculum is largely knowledge-based, designed around the idea that students must absorb facts, master core subjects, and retain information to succeed. But as artificial intelligence continues to evolve, some argue this approach is no longer fit for purpose.
Supporters of a curriculum rethink believe we should shift towards a more skills-based education model. The reasoning is simple: when AI tools can provide instant access to knowledge — and even summarise, apply, or evaluate it — the priority for human learners should shift.
Instead of focusing on memorising information, students should be developing adaptable, transferable skills that prepare them for an uncertain, fast-changing workplace. And at the heart of those skills is AI literacy.
A skills-focused curriculum would help students learn how to work with AI, how to think critically about its outputs, and how to use it responsibly. It would also foster the flexibility, creativity and problem-solving abilities that are increasingly valued in AI-augmented careers.
Others go even further — suggesting that education should now deliberately distinguish between human intelligence and artificial intelligence. That is, rather than trying to compete with machines in areas they are mastering (like logical analysis, information recall, or structured writing), schools should focus on developing the capabilities that are uniquely human. These include empathy, ethical reasoning, emotional intelligence, and complex social interaction — qualities that AI still cannot replicate meaningfully.
But while this sounds promising in theory, it raises difficult questions in practice. Can emotional intelligence truly be “taught” in a classroom? How do you assess empathy or social understanding? These challenges highlight why AI literacy must go hand-in-hand with a deeper rethinking of what education is for — not just what it includes.
As the nature of work and life continues to shift, the curriculum may need to evolve from something that teaches what to think, toward something that empowers students to understand how to think, how to collaborate with technology, and how to be human in a digital age.
Why Knowledge Still Matters in the Age of AI
As the push for a more skills-based curriculum grows, it’s easy to assume that knowledge has become less important — that facts can be outsourced to AI, and learners should instead focus solely on how to apply and adapt. But this view risks overlooking a critical truth: skills don’t exist in a vacuum — they depend on knowledge.
Using AI effectively is no different from working in any other complex field: in order to use the tools well, you must first understand the concepts behind them. It’s knowledge that empowers skill.
For instance, using an AI to analyse data still requires a grasp of maths, logic, and subject-specific understanding to interpret the results accurately and critically.
There’s also a flaw in the argument that we should stop teaching knowledge that won’t be directly “useful” in future careers. Much of what we learn in school doesn’t map neatly onto job roles — but that’s not the point. The process of learning knowledge matters just as much as the content. It trains us in problem-solving, analytical thinking, and the ability to make connections — the very traits that AI cannot replicate.
A knowledge-rich curriculum is also a powerful driver of creativity. True innovation doesn’t come from a vacuum — it comes from combining ideas, making comparisons, and drawing from a bank of information, stories, and concepts. These are things that only a deep, well-rounded education can provide.
Beyond academics, a strong education helps nurture soft skills like determination, self-belief, and resilience — the real currency for lifelong success. A curriculum rich in knowledge helps nurture these skills through creating an autonomous learning environment. And ironically, to get the best out of today’s fast-thinking AI tools, learners must first learn how to think for themselves.
So Does the Curriculum Need to Change to include AI Literacy?
There’s little doubt that the rise of AI will require adjustments to the way we educate. At a minimum, AI will need to be more explicitly incorporated into the computing and computer science curriculum, where many of the tasks currently taught, such as writing code, are already being automated by AI systems. But this doesn’t necessarily mean we need to throw everything out and start again.
As political commentator Stephen Bush has noted, the English computing curriculum already strikes a healthy balance by helping students understand how coding works, not just teaching them how to do it. With some careful tweaks, AI literacy learners could also be introduced to what the systems can and cannot automate, giving them a more realistic sense of its capabilities and limitations.
The key point is this: knowledgeable people are always going to get more out of new technologies. Whether it’s prompting an AI to write code, analyse data, or generate ideas, those with deeper subject knowledge will be better equipped to steer the system and evaluate its output. A knowledge-rich curriculum remains essential, not despite AI, but because of it.
When it comes to wider curriculum reform, it’s unlikely we’ll see a wholesale shift towards emphasising only the things that make us uniquely human, such as emotional intelligence or creativity. While those traits are increasingly valuable, they can’t easily be “taught” or measured — and they can’t replace the foundational knowledge that underpins confident and effective learners.
Ultimately, to get the best out of AI, humans need to understand the systems they are using. That starts with a strong foundation in knowledge. If anything, the most significant shifts in curriculum may come later in higher education, where students have already developed that base, and are ready to specialise in AI-related fields and emerging career paths.
In short, the curriculum doesn’t need to be radically rewritten — but it does need to evolve to include AI literacy. The challenge for policymakers, educators and school leaders is to strike the right balance between preserving what works and preparing students for the opportunities and complexities of an AI-driven future.
