Leverage Maps #1: Disrupted Education

Disrupted Education: Exploratory Learning Using An LLM

There’s plenty of justified skepticism about AI in education. Critics worry it encourages shortcuts, surface-level engagement, or passive consumption. But while studying for the FE mechanical exam, particularly the fluid mechanics section, I had an experience that showed how AI can enhance learning by helping rebuild understanding from first principles.

Let’s take buoyancy as a case study. The typical approach is to memorize this formula:

F_b = pgV_displaced

Through active inquiry I went through the following chain of explanation:

  1. Fluids have weight, so the deeper you go, the more fluid is above you and the higher the pressure. This relationship is expressed by:

P = pgh

  1. That pressure pushes in all directions, but we care about the difference in pressure from the top of a submerged object to the bottom.
  2. The bottom of the object is deeper, so it feels more pressure than the top. That pressure imbalance creates a net upward force.
  3. When you add up (integrate) all those pressure forces, they combine into a single upward force known as the buoyant force.
  4. The size of that force exactly equals the weight of the fluid that the object displaces. It’s a result of how pressure changes with depth in a gravitational field.

Here’s what brought it all together: buoyancy isn’t a mysterious “extra” force. It’s the natural outcome of how fluids behave under gravity. More surprisingly, it’s similar in spirit to the normal force you learn about in statics. Instead of a rigid surface pushing up, it’s a fluid applying pressure from all sides, with the imbalance resulting in lift. Also, thinking of buoyancy as unevenly applied forces or normal stress differentials allows you to connect the concept to concepts from material science and solid mechanics.



The Educational Shift: From Memorization to Mechanism

This wasn’t a shortcut. It wasn’t a Google search. It was slow, methodical questioning. The AI helped connect the dots in real time. I didn’t stop at “what”. I was able to quickly chase the “why” until the pieces clicked together. The tool responded to every follow-up question, helped clarify fuzzy concepts, and offered mathematical and visual perspectives I hadn’t considered.

This is what AI can do when used deliberately. It can help turn passive study into active exploration. It doesn’t replace teachers. It helps learners think like teachers by asking questions, testing ideas, and making connections.



Here’s the shift worth watching: it’s not about automating knowledge. When used well, AI can empower learners to move past rote memorization and engage deeply with the logic behind the formulas. In my case, it turned the study process from box-checking into curiosity-driven learning.

That’s the kind of disruption education could actually use.

Have you tried learning through prompting? What was it like?