How Language Models Expose the Flaws in Multiple Choice Testing

Imagine an AI passing the IIT-JEE, India's top exam for entry into premier engineering institutions. Sounds absurd? It shouldn't. Large Language Models are getting good at multiple-choice tests. But instead of marveling at AI, we should be questioning our tests.

India's top exams - IIT-JEE, AIIMS, JIPMER - are just elaborate multiple-choice questionnaires. Pick A, B, C, or D. Sometimes more than one. When the papers are graded, no one knows if the student understood or guessed. It's a black box.

Now, picture an AI taking these tests. Same problem. Did it "know" the answer or just crunch probabilities really well? We can't tell. And that's the real issue.

Multiple choice tests are a hack. They're easy to grade and hard to cheat on. But they're a poor measure of understanding. They reward test-taking skills over deep knowledge. AI is just making this glaringly obvious.

The rise of language models is accidentally exposing the flaws in our education system. These AIs are like mirrors, reflecting our own limitations back at us. We built them to process information, and now they're showing us how badly we measure that ability in humans.

This is a classic case of technology changing the rules. When the SAT was invented, multiple choice made sense. It was efficient. But now we have AI that can ace multiple choice while utterly failing at explaining its reasoning. This should set off alarm bells.

The solution isn't to make tests AI-proof. It's to make them better at measuring real understanding. Tests where you have to show your work. Where you have to explain your reasoning. Where guessing doesn't cut it.

This is hard. It's much easier to grade a multiple choice test than an essay. But that's the point. Learning is hard. Understanding is hard. Our tests should reflect that.

The irony is that by trying to create AI that can think like us, we've created a tool that shows us how poorly we measure thinking. It's a wake-up call. Our education system needs to evolve.

In startups, we talk about product-market fit. Maybe it's time to talk about test-knowledge fit. Our current tests don't fit what we actually value - deep understanding and the ability to apply knowledge.

So next time you hear about an AI acing a test, don't worry about the AI. Worry about the test. It's not just measuring students anymore. It's measuring us and our ability to measure understanding. So far, we're failing.

But like any good test, this one comes with a lesson. We now have a chance to rethink how we evaluate knowledge. To create tests that even the smartest AI couldn't pass without true understanding. That's the test we should be aiming for. Not just for our students, but for our entire education system.