Monte Carlo Simulations have been a fantastic way for m3 to evaluate students' assignments in my AI course. Since some of the traditional AI algorithms rely on

Monte Carlo Simulations have been a fantastic way for m3 to evaluate students' assignments in my AI course. Since some of the traditional AI algorithms rely on some degree of random, I run the simulation, check if it was able to achieve some threshold, and increment whether their agents were able to successfully complete the assignment. If they were able to complete the task 70% of the time, they receive credit for that test case. At the start of the semester, its just this weird method for their test cases, but once we hit our Uncertainty section, I bring up Monte Carlo, show them the Monty Hall scene from Brooklyn 99 [1], and explain how it can provide students with approximations. If possible, I recommend including a GUI to visualize a single trial of the simulation. My students tell me it helps them "see" how their agents are behaving. Plus, Computer Science does not have many tangible examples you can show the general public (aka, something they can show parents or potential employers. The GUI helps bridge that gap of "here's something anyone can see and understand". [1] https://www.youtube.com/watch?v=AD6eJlbFa2I https://www.youtube.com/watch?v=AD6eJlbFa2I