Preparing Young Learners for an AI Future: Why Foundations Matter More Than Fear

We have become fixated on a familiar question: Will AI take our jobs?

It is the wrong question.

History shows us that every major technological shift – from the industrial revolution to the internet – has triggered the same anxiety. Roles changed, some disappeared, but far more emerged. AI will follow the same pattern.

The real issue is not whether jobs will exist. It is whether people – particularly those moving through further education, reskilling, or re-entering the workforce – will be ready for them. Right now, too many are not.

Across the UK, employers are clear. Qualifications still matter, but they are no longer enough on their own. What organisations increasingly prize are harder-to-measure capabilities: adaptability, problem-solving, initiative, and the ability to learn continuously. In other words, strong cognitive foundations combined with the right mindset.

This is where further education has a critical role to play.

From working with thousands of learners globally – including those transitioning into new careers and vocational pathways – one truth stands out: everything starts with core skills. Before AI tools, before automation, before technical specialisms, learners need confidence in mathematics and English.

Mathematics develops logic, structure and analytical thinking. English underpins communication, comprehension and clarity of thought. Without these, engagement with AI and digital tools risks becoming superficial – functional, but not transformational.

But the challenge does not stop there.

Technology and AI literacy must now be treated in the same structured way we approach core subjects. In FE, there can be a tendency to focus on immediate employability – teaching learners how to use specific tools or platforms. While valuable, this often skips a crucial step: understanding.

We would not expect a learner to succeed in advanced maths without grasping the fundamentals. Yet in AI, we often do exactly that – introducing tools before building conceptual foundations.

A more effective approach is phased and deliberate.

Learners should be supported to develop computational thinking through structured exposure to coding – not to turn everyone into software engineers, but to build problem-solving capability.

Starting with accessible languages such as Python, progressing into logic, data handling, and pattern recognition, and eventually engaging with real-world AI applications.

By that stage, learners are not simply using systems – they understand how they work, their limitations, and how to question their outputs.

That distinction is critical in a post-16 and workforce context.

Because coding is not just about code. It is about thinking: breaking problems down, testing solutions, iterating, and persisting when outcomes are uncertain. These are precisely the skills employers are demanding across sectors – from advanced manufacturing to healthcare, finance to education.

At the same time, AI literacy is no longer optional for adult learners or those in vocational pathways. AI already shapes recruitment, productivity tools, decision-making systems and workplace processes. Those who understand its principles can engage critically and use it effectively. Those who do not risk becoming passive users.

The risk is not that learners will use AI. It is that they will use it without understanding it.

For FE providers, this creates both a challenge and an opportunity. There is a need to go beyond short-term training in tools and instead embed deeper digital and cognitive capabilities into programmes – whether academic, technical or vocational.

This is particularly important for adult learners and those re-entering education. Many are navigating reskilling in response to labour market shifts. For them, building confidence in foundational skills alongside digital understanding is not just educational – it is economic.

There are also lessons to be learned globally. In high-performing education systems, strong foundations, consistent practice and a culture of high expectations underpin outcomes at all stages – including adult education. Learning is seen as continuous, not confined to early years.

The UK has significant strengths in its FE sector, but greater consistency in embedding these foundations is essential – particularly as AI reshapes job roles across every industry.

Because the future workforce will not be defined by a fixed set of skills.

AI will sit across professions – supporting, augmenting and in some cases redefining them. The advantage will lie not with those who simply use technology, but with those who can think computationally, adapt quickly, and apply knowledge in new contexts.

This is why technology literacy must now sit alongside reading, writing and arithmetic – not only in schools, but throughout the lifelong learning journey.

Not to force every learner into a technical career, but to ensure every individual understands the systems shaping the modern workplace.

Because the reality is this: we are not preparing learners for a single job.

We are preparing them for constant change.

And those who will thrive will not be those with the most certificates alone, but those with the strongest foundations – in thinking, in discipline, and in how they approach problems.

So rather than fearing AI, we should focus on what has always mattered.

Equip learners with strong core skills. Develop their ability to think independently. Build resilience and problem-solving capability. Then layer technology on top of that foundation.

Do that – and they will not just be ready for the future. They will shape it.

Dr Rashmi Mantri is founder of British Youth International College (BYITC) and Supermaths.