My sixteen-year-old son spent a weekend fine-tuning an mBERT language model with labeled hate speech data, then benchmarked it against MuRIL, a publicly available model for Indian languages. No assignment. No tutorial. Just Google AI Studio, Google Colab, and curiosity.
He'd essentially skipped to the end of a university summer school curriculum. Using mBERT and MuRIL is advanced deep learning. Most students start with if/else logic and work their way up to Transformers over years. He started with Transformers.
When he got interested in AI/ML summer programs like NUS, we looked at the syllabi. He was already beyond where the program would end. That's when it crystallized for me: we're teaching kids to write code in an era when AI writes code. We're drilling them in syntax when they need judgment. We're preparing them for an education system that's already obsolete.
The Assessment Crisis
The real issue isn't learning. It's testing.
We test memorisation because it's easy to grade. We test syntax because it's objective. We test formula recall because answers are unambiguous. But when AI can instantly recall any formula, translate any language, and generate working code from plain English, what exactly are we testing?
How would we grade my son's project? There's no rubric for "saw a problem, taught himself the tools, built a solution." Traditional education has no framework for assessing judgment, taste, and the ability to translate ideas into reality.
Here's the paradox: memorisation was never really about recall. It was about building mental models, creating neural connections that enable insight. Can you outsource that connection-building to AI? I genuinely don't know. This might be education's most important unanswered question.
From "What Did You Learn?" to "What Did You Build?"
The old model: absorb information, regurgitate on tests, forget, repeat.
The new model: identify a problem, learn what you need, build a solution, iterate.
My son doesn't "study programming" anymore in the traditional sense. He builds things. When they don't work, he debugs with AI assistance. When they do work, he shows me with genuine pride. Think about it: when was the last time a student felt that kind of pride from passing a test versus building something that actually works? That said, he may still need to learn the internals of programming.
What Changes, What Doesn't
Programming: Concepts Without Syntax
I'm teaching my son computational thinking: data structures, algorithms, system design. He'll learn what hash maps do and when to use them, understand the difference between O(n) and O(n²) complexity and why it matters. He won't write sorting algorithms from scratch, but he'll know how to tell AI which approach fits his data characteristics. That's the skill that matters now.
Mathematics: The Unsolved Question
Why learn trigonometry when AI can solve any triangle problem instantly? The traditional answer is "to build mathematical intuition." But is that actually true?
I use trigonometry in the real world by describing problems to AI. The AI calculates, I interpret the result. Am I losing something essential by not working through the math manually? I'm honestly not sure. This uncertainty matters because if we keep obsolete practices or abandon essential ones, we damage kids either way. But waiting for certainty means falling behind.
Language Learning: Conversation Over Drills
Instead of drilling verb conjugations, imagine an AI language tutor conducting natural conversations, introducing new words in context, adapting to your pace. Sesame Street on steroids. The technology exists. We're just not deploying it systematically.
Writing: Evaluation Over Production
If AI can write fluently, what's left for humans? Judgment and insight. Students should learn to evaluate arguments, detect persuasive techniques, distinguish profound from merely plausible. The skill becomes: commission good writing from AI, evaluate the output, refine the prompt, iterate until excellent.
Science: Experiential Learning Becomes Critical
AI can help design experiments and analyze results, but hands-on experimentation creates knowledge that simulation can't replicate. Actually running the experiment, seeing unexpected behaviors, forming hypotheses about what went wrong or right. That becomes more important, not less.
The "Standing on Shoulders" Gap
Here's something we fundamentally don't teach in schools, especially in India: research before you build. Check what already exists. Understand how it works. Use what's available.
In the developer world, this is standard practice. Open source libraries, GitHub repositories, standing on the shoulders of giants. But in school, we're taught to solve problems from scratch, in isolation, as if nobody has ever tackled similar challenges before.
My son's hate speech project shows exactly this. He didn't just build a model. He researched existing solutions like MuRIL, understood their approaches, then benchmarked his work against them. That's how real work happens. That's how progress compounds.
In the AI age, this becomes even more critical. Before starting any project: what models exist? What approaches have been tried? What worked, what didn't, and why?
My son went down the rabbit hole of linear algebra because he wanted to understand how transformers actually work. Not because it was assigned. Because he asked "how does this work?" and followed the thread. This is the mindset we need to cultivate: intellectual curiosity combined with practical reuse. Train kids to ask "how does this work?" and then give them the tools to find out. Teach them that using existing work isn't cheating, it's how the entire field advances.
Personally, I feel like I can learn anything now with AI assistance. It's just that there's no time. Now imagine a kid with that power and time at hand. A teenager who can research any topic, understand any system, build on any existing work, and has the time and energy to explore deeply. That's not a future scenario. That's available right now.
The Practical Path for Parents
Start Tomorrow. Give them AI access and real computing power. A capable computer, AI tool subscriptions, and most importantly, permission to experiment freely. Tools like Qwen-Code offer generous daily limits that remove the anxiety of running out of quota.
Reframe the daily questions. Instead of "Did you finish your homework?" ask "What did you build this week?" Instead of "Did you study for the test?" ask "What problem are you trying to solve?" The shift in framing signals that creation matters more than completion.
Encourage cross-domain exploration. When I got interested in trading algorithms, he learned finance, statistics, API integration, and real-time data processing, all in service of building something. Let curiosity drive the learning.
Teach AI collaboration as a skill. Prompt engineering, iterative refinement, critical evaluation of outputs. Kids need to develop what I call "AI taste," the ability to recognize when AI output is excellent versus merely plausible.
Normalise failure and iteration. My son's first projects barely worked. Some crashed immediately. This is how learning happens now, through rapid iteration with AI assistance, not through getting perfect scores on predetermined problems.
Find the self-motivation trigger. When a kid builds something that actually works, even something small, the success creates genuine motivation to try something harder. Start small, build confidence, let it compound.
The Hard Parts
The credentialing problem looms large. Universities still want grades. Employers still want degrees.
I've genuinely considered not enrolling my son in college. Many Indian CS programs are essentially high school extended to the graduation level. Peter Thiel's fellowship literally pays kids $100,000 to skip college and build companies instead. Not a real option for everyone.
But that's a gamble I'm not willing to take because society simply isn't ready yet. My son is taking the SAT later this summer. Not because he'll learn something from it, but because that's what gets him into better colleges. He needs the degree not for education but for the path to a career, a job, a life.
Companies still hire based on leetcode performance and pedigrees. But when AI can solve leetcode problems instantly and the actual work becomes AI collaboration rather than manual coding, how long does that criteria last? The gap between what degrees certify and what jobs actually require is widening fast.
So we're looking for colleges that use AI as a learning accelerator rather than treating it as cheating. We're facing a gap period, maybe five to ten years, where kids will learn skills the system doesn't yet know how to credential. Parents who embrace this approach are betting that by the time their kids enter the workforce, these skills will be valued even without traditional certification.
The Timeline
This isn't about preparing for a future 20 years away. This is happening now. The tools exist. The capabilities exist. Only institutions and culture are lagging.
Companies are already starting to prefer people who can effectively use AI tools over those who resist them. That preference will only accelerate. Universities will adjust their curricula or become increasingly irrelevant. The question for parents is simple: do you wait for institutions to catch up, or do you start preparing your kids now?
What Success Looks Like
Imagine a student at 18 who can identify problems worth solving, break complex challenges into manageable pieces, collaborate effectively with AI to build solutions, and has a portfolio of actual projects rather than just grades on tests. Someone who possesses real taste and judgment about technical quality, and has developed genuinely self-directed learning skills.
This student can move from concept to working prototype in days rather than months. They understand systems deeply without getting lost in implementation details.
Compare this to a traditionally educated student with excellent grades from memorizing material, but who has never built anything real, never collaborated with AI, and has no portfolio of actual work to show. Which student is actually prepared for 2030?
The Broader Pattern
Andrew Ng captures this well: "When we moved from assembly to high-level languages like COBOL, there were people arguing that now we have COBOL, we don't need programmers anymore."
Every major technological shift has faced similar skepticism. Punch cards to keyboards. Assembly to high-level languages. Manual deployment to automated pipelines. Each time, practitioners worried that democratization would devalue their expertise. Each time, the field actually expanded rather than contracted. Each time, the new tools enabled entirely new categories of creation that weren't previously possible.
AI-assisted development is just the latest iteration of this pattern. It's not a threat to human capability but an amplification of it.
The Question That Matters
"What did you build this week?"
This simple question reframes everything. It shifts focus from absorption to creation, from performance to production, from grades to actual results. The answers might start small: "I built a calculator." Then they grow: "I built a game." Then they become genuinely substantial: "I built a trading algorithm" or "I built a hate speech detector."
This is education for the AI age. Not learning to memorize or replicate, but learning to create and evaluate. Not preparing for tests, but preparing for a world where the bottleneck is judgment and taste, not execution speed.
The revolution is already here. The tools already exist. The only question left is whether we're brave enough to actually use them.
PS: Written with Claude
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