How I Went From Below Average to Scoring Good Marks

I scored below 40% in most subjects through my early school years. Not because I wasn't capable — but because I was studying the wrong way, for the wrong reasons. The shift that changed everything was surprisingly simple: I stopped studying out of fear and started studying out of curiosity.

The Problem Was Never Intelligence

In school, I was what teachers politely call a "below average" student. Exams were something to survive, not something to learn from. I'd cram formulas the night before, pass the test with whatever I could retain, and forget everything two days later. The cycle repeated every semester.

I wasn't alone. Most of my classmates were doing the same thing. We were all optimizing for marks, not understanding. And the system rewarded that — you pass if you can reproduce the right answer, regardless of whether you actually understand why it's right.

The irony? I wasn't bad at thinking. I just wasn't applying any of it to what I was "supposed" to be learning. I could spend hours understanding how a game worked, how a machine worked, how things broke down and got fixed. But open a textbook? Blank.

The Moment the Shift Happened

It wasn't dramatic. There was no single epiphany moment where everything clicked. It was more like a gradual realization that crept up during my first semester of engineering — when I encountered programming for the first time.

Java was my first programming language. And for the first time in my academic life, I actually wanted to understand something. Not because it would be on the exam. Because I wanted to know: how does this actually work?

That one question — "why?" — was the shift. Instead of memorizing syntax, I wanted to understand what a class was. Why objects existed. What the point of inheritance was. I started connecting concepts to the real world, and suddenly information started sticking.

"The moment I stopped asking 'what do I need to memorize?' and started asking 'why does this work this way?' — everything changed."

Math Started Making Sense

Here's a specific example. I struggled with matrices in school. They felt abstract, mechanical, pointless. Nobody explained why matrix multiplication mattered. The textbook just showed the procedure.

Then I started building AI projects and encountered matrix operations everywhere. Neural networks are fundamentally matrix operations. Image processing is matrix math. When I finally saw matrices as a way to transform data — rotate, scale, project vectors into new spaces — the math went from abstract symbols to a powerful tool. I went back and re-read that chapter. It took me 30 minutes to understand what two months of school hadn't taught me.

The content hadn't changed. My relationship to it had.

The System I Built for Myself

Once I understood that curiosity was the engine, I rebuilt how I approached every subject:

• Start with the "why" before the "what." Before reading a chapter, I'd ask: what problem does this concept solve? Why did someone invent it?
• Connect to something real. For every abstract concept, I'd find a real-world application — even a rough one. This anchored the information.
• Teach it back. If I couldn't explain a concept to myself in plain language, I didn't actually understand it. I'd explain things out loud just to verify my own understanding.
• Embrace confusion. Instead of giving up when something didn't make sense, I treated confusion as a signal that something interesting was happening — not a sign of failure, but a sign that learning was occurring.

How This Applies to AI Engineering

I applied this exact system to every new technology I've learned — AWS, Docker, LLMs, RAG pipelines, OpenSearch. Every time I approach a new tool, I start with: what problem was this built to solve? What was broken before it existed?

This approach also made me a better teacher. When I became DSA Lead at Hackslash and mentored 300+ students, I could recognize fear-based learners immediately. They memorized patterns but got lost when the problem changed slightly. The fix was always the same: slow down, ask why, connect to something concrete.

If You're the "Below Average" Student Right Now

Here's what I'd tell my younger self: you're not bad at learning. You might just be learning the wrong thing the wrong way, for the wrong reasons.

Intelligence is not a fixed thing you either have or don't have. It's a direction you point your curiosity. Point it at something that genuinely matters to you — not just what's on the syllabus — and watch what happens.

I went from below 40% in school to building AI systems that cut token usage by ~99% in production at Idyllic Services. The gap wasn't talent. It was finding the right reason to care.