From Struggling at School to Building the Technology I Wish I’d Had

I Was Made to Feel Stupid at School. Now I’m Building the Technology I Wish I’d Had.

There is something I wish somebody had explained to me when I was at school: struggling with the way you are being taught is not necessarily the same as struggling to understand.

I didn’t know that then. There were times at school when I could reach an answer but struggled to explain how I had got there in the expected way. My mind didn’t always seem to travel neatly from A to B to C. Sometimes it went from A to somewhere completely different, connected several things together, and arrived at C from another direction.

Trying to explain that process could be harder than finding the answer itself. When the way you think does not fit neatly into the way understanding is being measured, it is very easy for people to underestimate you. Eventually, you can start underestimating yourself. For a long time, I did.

Today, I am the founder of Kyzel Kreates™ and creator of the 4P3X Verse Ecosystem™. That sentence still feels slightly strange when I compare it with the child I was at school, but the distance between those two points has taught me something important about computing, education and artificial intelligence.

Some of the minds that struggle most with existing systems may also be capable of imagining completely different ones.

I wasn’t trying to become a technologist

My journey into technology did not begin with a grand business plan. I didn’t sit down one morning and decide I was going to create an ecosystem of technology. It began much more simply: I kept noticing problems, and then I kept asking questions.

Why does it have to work like that? Why can’t somebody use it another way? What happens to the person who doesn’t fit the process? And perhaps the most important question: could we build something different?

That way of thinking gradually became the foundation of how I approach technology. I don’t normally begin with the technology itself. I begin with the person experiencing the problem.

Computing gave me a language for how I think

One of the things I find fascinating about computing is that there is rarely only one possible route to a solution. There are constraints, rules and things that simply will not work, but within those boundaries there can be enormous room for creativity.

You can break a large problem into smaller ones, test an idea, discover that it doesn’t work, change it and try again. You can connect systems that were previously separate. You can build a rough version, learn from it and improve it. Failure becomes information rather than proof that you are incapable.

For somebody whose mind naturally asks, “But why does it have to work that way?”, computing can be incredibly powerful. It gives that question somewhere to go.

Then AI changed what was possible

Artificial intelligence accelerated that process for me. Not because AI suddenly knew everything, because it doesn’t. Not because it always gets things right, because it certainly doesn’t. And not because I can type a sentence into a machine and magically receive a finished product.

What AI changed was the distance between an idea and the ability to explore it. I could describe a problem, challenge an assumption, explore possible structures, test approaches, find weaknesses, refine an idea, learn about areas where my knowledge was limited and then repeat the process.

AI became most useful when I stopped treating it as something that should simply give me answers and started treating it as something I could think with.

I still had to decide what problem mattered. I still had to question the output, recognise when something wasn’t right and decide what should happen next. But suddenly, the unusual connections my mind was making could be explored much faster.

One problem kept becoming another

Something else started happening. I would work on one problem and realise that the underlying idea could help somewhere completely different.

A feature designed to make learning clearer might also improve accessibility. An offline approach designed for education might have value wherever connectivity is unreliable. A method of simplifying information could potentially help somebody trying to navigate a complicated support service. A system designed around different ways of processing information could have applications far beyond a classroom.

One idea became another, and then another. Eventually, those ideas started becoming connected families of systems rather than isolated projects. That is how the 4P3X Verse Ecosystem™ developed.

It did not grow because I started with a plan to build something enormous. It grew because I kept asking the same basic question: if this helps here, where else could the same principle help?

The child asking awkward questions might be onto something

Schools naturally need structure. Teachers have limited time, large numbers of pupils and considerable responsibilities. This is not an argument that every rule or educational process should disappear because one child wants to do things differently.

But I do think we should be careful about what we interpret as ability.

The pupil who struggles to explain their reasoning may still understand the problem. The pupil asking endless questions may not be trying to disrupt the lesson. The child taking an unusual route towards an answer may not be taking the wrong route. The pupil who appears disengaged may simply not have found the thing that makes the subject meaningful yet.

Computing can be particularly powerful for those children because it gives curiosity somewhere constructive to go. Instead of only asking, “Can you remember how we told you to do this?”, we can sometimes ask, “What would you try?”

That is a very different invitation.

Technology is something young people can shape

I think this matters even more as AI becomes part of everyday life. Young people should not grow up believing technology is something produced by mysterious people somewhere else that they are simply expected to consume.

They should understand that systems are designed by people. Interfaces are designed by people. Rules are written by people. AI systems are developed, trained, configured and deployed by people. Therefore, those systems can be questioned.

A young person should be able to look at a piece of technology and ask: why does it work this way? Who might this exclude? What could make it better? Could I build another version?

That final question is particularly important because the answer increasingly is yes.

The skills around AI matter as much as the AI

As AI tools become more capable, there is an understandable fear that they will reduce the need to learn technical skills. I think the opposite challenge is emerging.

We need young people who can think critically enough to use powerful tools responsibly. That means understanding problems before trying to automate them. It means checking information, testing, debugging, recognising limitations, understanding privacy and safety, thinking about accessibility, considering the person who will actually use what has been created, and being willing to tell an AI system that its confident-looking answer is wrong.

Those are computing skills, but they are also human skills.

I sometimes think about the pupil I used to be

If you had shown that child what I am building today, I’m not sure he would have believed you. The important part isn’t the number of projects or the technology itself. It is the change in what I believed I was capable of doing.

For years, thinking differently could feel like a disadvantage. Now it is often the reason I see a problem differently in the first place. That does not mean every difficulty magically becomes a strength. Life is considerably less tidy than motivational posters would have us believe.

It means that a characteristic that creates difficulties in one environment can become valuable in another. Education has an extraordinary opportunity to help young people discover that earlier.

Somewhere in a classroom is another child who thinks differently

They might be struggling, bored or asking awkward questions. They might know the answer but be unable to explain how they got there. They might already have decided they are not clever because the evidence they receive every day appears to tell them so.

Computing cannot solve every problem that child faces. Neither can AI. But computing can give them something enormously valuable: the ability to turn “Why does it work like this?” into “What if I built it differently?”

I know how powerful that transition can be, because eventually, that is exactly what happened to me.