We can’t afford to turn away from computing degrees | Wonkhe
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by<br>Steve Pettifer
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13/08/26
Image: Shutterstock
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Wonkhe-Steve-Pettifer
by<br>David Kernohan
staff
13/08/26
Steve Pettifer is Vice-President (Academy) for BCS, The Chartered Institute for IT, and Professor of Computer Science at the University of Manchester
Tags
admissions
AI
Steve Pettifer<br>Steve Pettifer is Vice-President (Academy) for BCS, The Chartered Institute for IT, and Professor of Computer Science at the University of Manchester
Tags
admissions
AI
A dangerous myth is gaining traction with teenagers picking their university subjects, which I’m sure has also spread to many parents: there’s no point studying computing any more, because Generative AI can do it all now.
On results day, figures from UCAS showed that the numbers of students accepting places on Computing degrees at UK universities had fallen for the second year running; (by seven per cent for UK 18 year olds).
While we also know that people taking dedicated degrees in AI have been rising, these represent a very small minority of all computer science graduates.
My professional body, BCS The Chartered Institute for IT, is asking government, employers and my sector – education – to take this trend very seriously.
What a computing qualification really means
Why? Not just because of the risk of expecting whole areas of critical infrastructure to run safely on vibe-coded software, without expert technologists who understand what it takes to maintain it
The reason is that it’s built on a misunderstanding of what a computing qualification has ever been for.
A degree in computer science was never about learning a particular programming language, framework, or paradigm, any more than chemistry has ever been about mixing things in test tubes. The subject is and always has been about understanding the world around us well enough to make new things that work, and improve the things that don’t. In computing, that understanding has a name: computational thinking, the ability to take a messy real-world problem and work out how to make a machine solve it. Languages and frameworks come and go. That skill does not.
I suspect the myth comes from a reasonable-looking but false analogy: you can type a sentence into an image model and get a passable picture for a funny birthday card, so it feels as though you ought to be able to type a sentence or two into some other AI chat thing and get a working system of any complexity out of the other end. You cannot. Building, debugging, securing and maintaining real software is nothing like ordering a cartoon.
Evolving at pace
Generative AI is a genuinely powerful addition to the toolbox; it can help you think, save you time, point you at problems you would have missed, but it is a tool alongside the others, not a substitute for knowing what you are doing.
There is something particular about this technology that makes the panic understandable though. The past fifty years or so have not been short of disruptive technologies, and every one of them left computing a solid career. But the earlier ones were at least explainable. The computer: a machine that follows simple instructions mindlessly, but very fast. The internet: a way for computers to talk to one another. The web: a way to publish and link content. Each explanation fits neatly into a short sentence. We also saw these technologies evolve from their most basic form to what we know now. Generative AI, on the other hand, seemed to arrive out of nowhere in a startlingly mature form.
It keeps evolving at pace, and it is annoyingly and stubbornly resistant to explanation. I have spent more than a year trying, with colleagues who know far more than I do, to find a way to describe how it does what it does that is both true enough and clear enough to be useful to a general audience. I have failed. When even the practitioners struggle to explain the magic, it is no wonder the creative world is anxious about what it means for them, and no wonder computing is asking the same question of itself.
Lessons from a history of disruption
We have been here before though, even if in a smaller way. What we ask computers...