July is typically the time for many of us in the education and skills space where we process results and focus on getting our learners and apprentices to their next destination, whether that’s within education or employment.

For many of us AI has begun to deliver efficiencies within our processes and also challenges with plagiarism (although that’s always been an issue) and the sharing of live examination questions and answers in real time. However, it’s also a time to reflect and look at how we might do things differently – and why.

As the UK positions itself as a global leader in artificial intelligence, the further education (FE) sector finds itself at a pivotal crossroads. The government’s AI Opportunities Action Plan, published earlier this year and the industrial strategy outlined a bold vision: to embed AI across public services, boost economic growth, and transform how people learn and work. But what does this mean for vocational education, and how can FE providers rise to the challenge?

AI is no longer a futuristic concept. It’s a present-day disruptor in a long line of disruption. For many years, the use of a ball point pen was frowned upon. From adaptive learning platforms to automated assessments, AI is reshaping how learners and apprentices engage with content; and how awarding organisations design it and educators deliver it. For vocational learners and apprentices, this gives us all the chance to open up new possibilities.

Can we create personalised learning pathways tailored to individual strengths and career goals and give them the ability to learn in a way that best enables them to reach their potential? For neurodiverse learners and apprentices this could be a gamechanger.

We’re already seeing the growth of AI-powered simulations for hands-on training in fields like healthcare, engineering, and digital media. We could do so much more to build mathematical skills with the right investment and imagination.

It also gives us the ability to provide real-time feedback and support which benefits educators in looking at their training and approaches and gives meaningful feedback to learners and apprentices, as well as potentially reducing barriers for neurodiverse and disadvantaged learners. It was good to note the industrial strategy backs this transformation with initiatives like TechFirst, a £187 million programme to bring AI and digital skills into classrooms and communities.

Despite the aspiration, the UK faces a significant AI skills shortage. The government has called for expanded education pathways into AI, including through FE and apprenticeships and not just higher education. This shift demands that we upskill all of us involved in designing and delivering education and skills, including employers, to confidently use and teach AI tools. We also need curriculum reform to embed digital literacy and data fluency across subjects. Hopefully this will be reflected within the curriculum and assessment review. Importantly learners and apprentices need to understand when to use AI, the risks involved and the need to interpret the information in a meaningful way – including accepting that it’s not always right (eh Grok!).

We also need to be able to modify qualification content and assessment at a quicker pace than we do currently, ensuring content is up to date and relatable. Stronger employerpartnerships will helpto align training with evolving industry needs, although employers also need investment into understanding AI.

We know the government is investing in training 7.5 million workers in AI skills by 2030 through industry partnerships, expanding apprenticeships and modular learning, including skills bootcamps and higher technical qualifications and creating AI growth zones and regional adoption hubs to support local innovation. But AI must not widen existing inequalities. As FE institutions embrace tech-driven learning, they must also champion inclusive design, mental health support and flexible access. Micro-credentials, hybrid models and modular qualifications can help learners of all ages and backgrounds thrive. It’s important that the industrial strategy’s lifelong learning entitlement, launching in 2027, will help people upskill and retrain throughout their careers with the right support and guidance.

However, that’s two years away. The FE sector has a unique opportunity to lead the UK’s AI revolution – not just by teaching technical skills, but by reframing education itself. That means looking to embed green and digitalskills into every vocational pathway. We need to look more at supporting dual professionalism, where educators combine industry expertise with pedagogical excellence. And we need to drive policy innovation, especially around funding, qualifications, and workforce development.

As we look ahead to the next academic year and beyond, the question isn’t whether AI will transform FE but how boldly we choose to shape that transformation.

Technology obviously does transform society and the economy, and it obviously does make certain skills more or less valuable in the workplace.

Think about marathon-running. Ancient Greek ‘day-runners’ like Pheidippides, the original marathon runner, were a vital part of the functioning of Greek city states. Their aim was to get a message from A to B as quickly as possible, and in a world with no combustion engine or telegrams or internet, if you needed to send a message you needed a day-runner.

Nowadays that is obviously not the case. If you need to send a message to a friend 26 miles away, you’d probably send a text message. If they needed a particular physical set of documents, you still wouldn’t hire a day-runner – you’d hire a taxi driver instead.

And yet, running marathons is incredibly popular and more people run the London marathon every year than there were ever day-runners. None of them run it to courier a message. The vast majority run it for reasons of personal development. A tiny fraction do run it to gain economic benefit – not from being messengers, but from winning races and providing entertainment which spectators are willing to pay for.

The difference between ancient Greek marathon runners and today’s marathon runners is at the heart of so much confusion about AI and its role in society and education.

• Students are baffled by teachers who want them to deliver a message via a marathon run when you could just send a text message.
• Teachers are baffled by students who want to get a taxi round a marathon course and expect a medal at the end of it.

So partly, this confusion is about what you think the purpose of education is.

• If you think the point of education is for students to develop their human potential, then the teacher is by definition right. In order to develop your human potential you can’t get someone or something else to do it for you!
• If you think the point of education is to get a job, you will probably be more open to using technology in education.

Education for personal development

However good AI gets at writing, there is always going to be a personal development use case for writing which is analogous to running.

That is, even if the direct economic value of being a good writer gets completely obliterated by technology, the general life value of being able to write will remain. 

Being able to run a mile or two without getting out of breath is good for your general health and fitness, and that’s why lots of people choose to go out running even though there is no direct economic benefit in it for them. Likewise, I suspect that people will value being able to read and write complex texts even if they can’t make money out of it.

One reason I think this is because writing has so many underappreciated benefits. Often, we assume the main purpose of writing is communication, as though it works like this: I have a series of well-worked out and complete thoughts in my head; I write them down; other people can read them.

That is certainly one way we use writing, and the one that generative AI is perhaps best placed to help with. If you have a series of thoughts in your head, you can speak them into a voice memo and get AI to polish them up into a perfect paragraph or series of paragraphs. A good example is where you have a relatively simple decision to make – perhaps whether to say yes to an invitation or not – you make the decision, and then you struggle to communicate it. Generative AI can be helpful in these cases.

But there is another important function of writing which generative AI is less well placed to help with: writing helps extend working memory and is a tool for thinking. A lot of the time we don’t have complete and well-worked out thoughts in our head. It is only by writing our thoughts down that we discover the gaps and the flaws in them. “If you’re thinking without writing, you only think you’re thinking.”

This process is obviously important if you are doing some kind of deep academic work, but it’s still important in everyday life – if you are writing an email to friends with the logistics for a day out, or a memo to colleagues on the decisions that have to be taken at your next meeting. Generative AI might already be a better writer than you in terms of being able to quickly draft readable and error-free texts. But is it better than you at working out what you actually think?

Cars are faster than humans, but we still teach PE at school. Similarly, even if generative AI totally outstrips humans at writing, I think we will still teach it because it is a valuable tool for life.

One of the reasons I love the work of The Writing Revolution is that they explicitly make this link between thinking and writing, and design lessons and curriculum sequences that teach writing as a tool for thinking right from the start. The resources we’ve developed at No More Marking follow this approach too. 

But what about education for the job market?

Of course, personal development is not the only purpose of education. Another important purpose is to get a job, and here it would seem the advocates of generative AI are on stronger ground. If AI is going to be used extensively in the workplace, surely it should be used extensively in the classroom too?

I am not so sure. Even if you are solely concerned with the economic function of education, and even if you don’t care very much about the personal development aspect, there are still strong reasons to avoid the excessive use of generative AI in education. 

This post was originally published on the No More Marking Substack here. You can subscribe for free to read future posts in this series.