What happens when you drop a ball while running? Will it fall in front of you, at your feet or behind you? Most people are convinced it will fall behind them. Makes perfect sense, right?
Where will the ball fall?
Then there’s the famous textbook problem of a monkey and a bullet. Imagine a scenario where a hunter aims at a monkey in a tree, and simultaneously, the monkey lets go of its branch. Most people would assume the bullet will miss. But physics tells a different story—the bullet will always hit the monkey. (Poor monkey.)
Or consider another classic misconception: a heavier ball must surely land before a lighter one when dropped from the same height. Even though we have all heard the story of Galileo supposedly dropping objects from the Leaning Tower of Pisa to disprove this idea, our intuition fights against what science and evidence tell us.
The questions of how we address these misconceptions or alternative conceptions have received a great deal of attention. In my work with teachers over the years, I have done countless activities and demonstrations, seeking to use simple technologies like cell phone cameras to help people see past these incorrect ideas.
This morning, while walking my dog, I wondered if there was some way GenAI could help. I have written earlier about how GenAI can help teachers engage with students who have misconceptions about a phenomenon by having the AI take on the role of the student and allowing the teacher to engage in repeated simulated classroom conversations. These AI-powered interactions provide a safe space to practice, fail, and reflect on our approaches to addressing alternative conceptions.
But this time I was thinking of simulations in a different way.
Over the past few weeks, I have been using AI to code and create simulations—essentially “vibe coding” where I translate my ideas and intuitions directly into working applications with AI assistance, rather than through traditional programming methods. II have written about my unit-circle simulation that weaves together Pythagorean theorem, trigonometric functions, sound, and color. More recently, I have created simulations of bacterial interactions with antibiotics and two different visualizations of gas laws that bring abstract thermodynamic principles to life (Version I & Version II). These simulations take knowledge we have of science and scientific models and bring them to life. They seek to accurately capture some aspect of a scientific view about some phenomenon in the world.
Screenshots of some of the STEM based simulations I have created.
(Just to be clear, it has not all been science and math—there is also the Teen Taal simulator that breaks down the intricate rhythms of Indian classical music.)
But the idea I had this morning was different. Instead of creating accurate simulations, what if I created deliberately incorrect ones. Simulations that intentionally violated the laws of physics. But not randomly—but in ways that aligned with what research showed were certain kinds of standard misconceptions or alternative conceptions people have.
Why? Because true understanding doesn’t come from perfect representations. It comes from critical engagement.
And true understanding happens not when using the simulation (because simulations can lie) but in the moment of verification. Of taking what you see in this little virtual microworld and trying to connect it to the real world. Understanding happens when you pull out a phone camera and collaborate with others to capture actual phenomena. When the virtual world’s claims, which seem to align with our views, crash against real-world evidence.
Given this, here are three simulations that are scientifically incorrect but align with research on the kinds of misconceptions students have. The idea is simple. Create a simulation that is built on an inaccurate understanding and let learners interact with it, and then make them go out in the world and actually run the experiment, drop the ball, and have a friend take a video of them doing it.
Here are the three simulations based on common physics misconceptions (in fact the very ones I mentioned at the beginning of the post).
- Simulation 1: Drop objects of different weights and see which lands first.
- Simulation 2: The Push vs. the Drop – Test your physics intuition with this classic problem. Which ball hits the ground first?
- Simulation 3: The Running Drop – Explore what happens when you drop a ball while standing still, walking or running.
Screenshots of the three simulations above.
There are two fundamental learnings here. First, that science is empirical. Understanding comes from observation and forcing our instincts to collide with real data. Second, that virtual worlds are constructs that can lie, distort, and mislead.
In this sense, these simulations are provocations. Provocations that can lead to opportunities to powerfully engage not just with ideas but their relationship to the real world.
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