By Andrew Cheung; Co-Founder RoboLabX
On the last day of term, I walked into a school assembly without Jasmine — our delivery robot, and arguably the star of the after-school club. She had broken down that morning, and I hadn't had time to fix her. I stood in front of a room full of kids who had spent weeks learning robotics, programming, and AI, and I simply told them the truth: Jasmine wasn't here because something had gone wrong.
What happened next stopped me in my tracks. Without asking, the children began to diagnose the problem out loud. "Could it be the hardware?" one said. "Maybe a wiring issue?" offered another. "What about the programming?" a third chimed in. They hadn't seen Jasmine. They had no information beyond the fact that she wasn't working. And yet, systematically and confidently, they were thinking through every possible layer of failure — hardware, software, connectivity — just as any engineer would.
That moment is why Robolab X exists.
I spent fifteen years working in the technology industry, from Yahoo to EE to other startups as a digital product manager. For most of that time, I heard the same phrase: fail fast. It was everywhere. On walls, in company values documents, in town halls. And it was almost entirely meaningless, because in a real workplace, failure stays on your record. People are risk-averse for good reason. Your reputation matters. No matter what a manager says about embracing failure, almost nobody actually does it. I realised that if we genuinely want the next generation to think boldly, experiment freely, and recover from mistakes without freezing up, we cannot wait until they are adults. We have to start now, while the stakes are low, and the curiosity is high.
That is the foundation of Robolab X. We run clubs that teach robotics, programming, drone sequencing, and AI literacy. But underneath it all, what we're really teaching is a different relationship with failure. One that's honest and not particularly dramatic — just: something didn't work, let's figure out why.
We do not hand children ready-made toys. We have them build robots from individual components, so that when something breaks, and it always does, they can trace the problem back to its source. Early on, kids come to me saying "it's not working." By the end of term, they come saying "I think it might be the motor command, because the wheel is responding but not in the right direction." That shift in language represents a shift in thinking. By the end of term, children who arrived unable to
articulate a problem are leaving with the instinct to diagnose one.
The physical robot is what makes it click. When a child writes code to move a robot forward and it turns left instead, something that a screen alone can't produce. They can see exactly what went wrong, right in front of them. They learn quickly that the computer isn't guessing at what they mean. It's doing exactly what they said. If you want something specific, you have to say it specifically. That turns out to be the same skill that makes a good AI prompt. And a good engineering brief. And a good question, that's it.
AI literacy is a big part of our curriculum. Not in a "here's how to use ChatGPT" way, but in a "here's how this actually works, and here's where it goes wrong" way. We talk about how machines learn to recognise images and sounds. We talk about hallucinations, safety, and spotting AI-generated content, and why that matters. We teach them to think of AI the way their parents think of a search engine: enormously useful, not inherently trustworthy, and only as good as the question you ask it.
Discussing AI often provokes curiosity across subjects in a single conversation. Philosophy, maths, history, science, languages. That kind of cross-disciplinary exploration used to require a library visit and a lot of luck. Now it's a prompt away, and teaching children to do it well, critically, with good questions, is something schools should be doing now. We want children to understand how to use that power wisely, guided by good questions and critical thinking, rather than discovering it unsupervised through a random video online.
This generation will be the first to grow up with AI as a fundamental layer of society, much like my generation grew up with personal computers and the internet. Here's what worries me: the regulations that will govern AI are being written right now, by people who won't be living under them. The children in our clubs will. They'll have eighty, maybe a hundred years ahead of them in a world where AI and robots are as fundamental as the internet and devices are today. They should be the ones helping to shape how it works — not because I'm being idealistic, but because they're the only ones who'll actually have to live with the consequences. They deserve to understand it, to question it, and ultimately to help shape it.
Robolab X was built on that belief. My co-founder and I both came from technology business backgrounds, and we both noticed the same thing: technology moves much faster than it filters down into education. Our experiences at the frontier of AI and robotics have shown us that world-leading work should not stay locked inside boardrooms and startups. It should filter down. All the way down, to a classroom on a Tuesday afternoon, where a child is figuring out why their robot won't turn left.
We are not here to produce engineers or programmers, though some of them may become exactly that. We are here to make sure that when these children grow up and sit at the table where decisions about AI and robots are made — as employees, as citizens, as voters, as leaders — they will not be intimidated by what they do not understand. They will have been there before. They will have broken things, fixed things, and learned that failure, handled well, is just the beginning of the answer.
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