AI Did Not Break Education, It Revealed the Bug

When Gi from Python Singapore invited me to speak at PyCon Singapore, I decided it was time to explain why I had left.

The talk was called So Kiasu, Still Kena Replaced by AI. It is a very Singaporean title. You need at least a little local context to fully appreciate kiasu and kena.

Not many people in the Python community knew that I was born in Singapore. Fewer knew the circumstances in which I had left. Even fewer knew that I had once trained to become a teacher there, or why I walked away after one year.

I did not leave because I disliked teaching. I loved working with children and helping them understand something that had previously felt impossible.

I left because the education system I entered seemed more concerned with completing the syllabus than making sure students were learning.

That disappointment stayed with me for more than twenty years.

So when I began preparing a talk about AI, I found myself returning to the same questions.

And what happens when a system built around producing correct answers meets technology that can generate those answers in seconds?

For most of my adult life, I had told the story in fragments. I had left Singapore. I had moved to France. Later, I moved to the Netherlands. Somewhere in between, I had learned Python, joined open-source communities, organised conferences, and eventually became a Board Director of the Python Software Foundation.

That version had a clean line through it.

Kiasu comes from Hokkien dialect. Literally, it means being afraid to lose.

In Singapore, it is more than a word. It is a reflex, a joke, a parenting strategy and sometimes an entire philosophy of education compressed into two syllables.

Study hard. Get good grades. Enter a good school. Find a stable job. Then you will be safe.

I heard some version of this throughout my childhood. I suspect many people from my generation did.

I studied hard. I went to public school. I do not remember feeling much academic pressure until Primary 3, when I was around nine years old.

I was one of the top three students in my class, so the system moved me into 4A, where many of the strongest students in the year were placed.

That was when I realised my ranking depended on which class I was placed in.

Many of my new classmates came from families where English was spoken at home. Their parents were executives or professionals. Mine were not.

My old friends were in other classes. My new classmates did not spend much time with me, and I did not spend much time with them.

In the new class, the competition was harder. I went from being near the top to being one of the lowest-ranked students, so the following year I was moved back into a normal class.

There, I became one of the top students again.

Once I was in the higher-performing class, I fell towards the bottom again and was moved down once more.

For a while, my education ran like a for-loop:

The result changed according to the class, but the loop remained the same.

In one room, I was bright. In another, I was not bright enough. The system kept returning different judgments.

When I signed up to become a teacher, I signed a bond.

This was normal. The government would pay for my training, and in exchange I would owe several years of service. Leave early and I would owe money instead.

Most arrangements seem fair enough on paper.

Before we received formal teacher training, we were assigned to schools for six months.

I was the main class teacher for a group of eight-year-olds and a subject teacher for children between ten and twelve. There were forty students in a class.

Alongside teaching, teachers completed weekly reports for heads of department and school leadership. The syllabus moved at a fixed pace. The reports documented that movement.

One week, I was teaching a particularly difficult mathematics topic.

By the end of the week, only four children understood it. Four out of forty. Ninety percent of the class did not understand.

The following week, we were scheduled to begin another topic. I asked whether I could spend more time on the one we had just covered.

The syllabus said move on, so we moved on.

We left thirty-six children standing on a foundation that was not there, and we built the next floor on top of them anyway.

It would be easy to tell this story as though someone in the staff room had made a cold, deliberate decision to abandon those children.

The teachers were not uncaring. The head of department was not trying to harm anyone. Nobody arrived at school intending to leave children behind.

Every adult was doing what the system required.

The syllabus had to be completed. The next topic had to begin. The weekly report had to show that the class was moving according to schedule.

The system asked for completion, not comprehension. It asked the children to keep up or fall behind quietly. It asked the teachers to record that the chapter was done.

And we did. The system was not malfunctioning. It was functioning exactly as designed, optimised for the wrong thing.

Six months later, I began studying at NTU (Nanyang Technological University) to become a certified teacher.

There, I found that teachers were prepared through the same system used to educate students.

Then you are ready to teach. For me, that defeated the purpose of teaching. Teaching, to me, is listening.

It is watching a specific child fail to understand a specific thing, then changing your approach until something works.

It is noticing that a student’s silence does not mean “I understand,” but “I am confused.”

It is recognising that two children can give the same wrong answer for entirely different reasons.

The system I had entered had a slot for delivery. It had a slot for assessment. It had a marking pile tall enough to bury the listening completely.

I had signed a government bond for my teacher training, so walking away meant repaying the cost of that education. The amount continued to grow while I appealed and negotiated, and I spent nearly twenty years paying it back across several international moves.

The debt was difficult, but it was not why I left.

I left because I could not accept an education system that required teachers to move on while most of the class was still lost.

By many measures, Singapore’s education system is world-class. I will not pretend otherwise. But I am not an objective observer of it.

I experienced the system as a student and later as a teacher. I also paid a substantial price to leave it.

That history inevitably shapes how I see it.

I built the talk expecting to share my story and let people decide what they thought of it.

What I did not expect was how many people would recognise parts of their own lives inside it.

One attendee found me afterwards, and we talked for far longer than the usual handshake and long discussion that follows a keynote. We did not talk much about AI. We talked about Singapore’s education system, and about how closely my story resembled something he had been carrying privately for years.

He was not responding to a talk. He was responding to recognition.

I had described a room he had also stood in, and for a while, he wanted to stand there with someone else.

That conversation has stayed with me more than any compliment or photograph. It confirmed something I had suspected but could not prove until I said the story aloud:

This was never only my story. I just had the microphone.

A teacher came to find me too. He told me about his children, and about trying to help them find lives that suited who they were rather than the narrow idea of success expected of them.

I do not think he needed my permission. Perhaps he needed to hear someone say plainly that there is no single correct ladder.

The ladder was always a story we agreed to believe. It was never a law of physics.

Michelle later wrote about the talk, and her reflection helped me see that the question was larger than whether students and teachers were ready for AI.

The real question was whether an education culture built around correct answers could survive a world in which answers had become almost free.

Iqbal wrote a blog with nearly fifteen hundred words the next day. He took the argument somewhere I had not yet finished taking it myself: AI is not the disease. It is the mirror. The system has been standing in front of that mirror for years, blaming the lighting.

He added a fifth imperative to the four I had offered in the talk:

Be the person who still reads the entire hundred-page document.

Not “children, go forth and GitHub yourselves into enlightenment.”

Healthy open-source projects have maintainers, contribution guidelines, documentation, review processes, codes of conduct, mentorship and governance. They are open, but they are not without structure.

What education could borrow from open source is not the software. It is the way learning remains visible.

In open source, the final code is only part of the record. You can often see the problem that started the work, the first attempt, the questions, the review, the changes and the reasons behind them. The polished result does not appear from nowhere. Its history is part of the contribution.

A student should be able to show where an idea began, how it changed and what caused that change. If AI was involved, that should not need to be hidden or smuggled past the teacher. The important question is not whether the machine touched the work. The important question is whether the student exercised judgment.

What was inaccurate, shallow or irrelevant?

What can they now explain without the tool?

In that environment, AI output is not an answer key. It is closer to a pull request.

It may be useful. It may also be incomplete, poorly reasoned or confidently wrong. Students should not accept an AI response just because it sounds polished and convincing. It has to be reviewed. It has to make sense.

Open source also gives learners the chance to leave something behind.

A person may begin in open source by following documentation written by someone else. Later, they may help others by improving an example, rewriting unclear instructions, translating documentation, or answering a beginner’s question.

Students could learn in the same way. They may begin by using notes, examples, or explanations created by others. Once they understand the subject, they could improve those materials or create a clearer explanation for the next group of students.

The learner gradually becomes a contributor.

That is a profound shift from the way school often works. Students normally produce work for one teacher, receive a grade, and then watch the work disappear into a drawer or an online portal. Before long, they may also forget much of what they learned.

But what if part of learning was making the path clearer for the person who came next?

A student who finally understands a difficult concept might write the explanation they wish they had received. Another might improve an example that confused the class. Someone else might translate it for a student who learns in another language.

The work would no longer exist only to prove that learning had happened.

It would become part of the learning environment itself.

In this model, the teacher is not the repository of knowledge.

The teacher is a maintainer of the learning environment.

A good maintainer does not personally create every contribution. They uphold standards. They review work. They protect newcomers. They explain the rules nobody wrote down. They notice where participation breaks. They help people move from using something to taking responsibility for it.

The claim I am making is larger than my own grievance.

Singapore did not invent these problems. It simply runs the system more efficiently than many other countries.

Technology changed. Work changed. The way we communicate changed. The way we find information changed. The world kept moving, but some assumptions buried inside education remained un