POMDPs.jl is a leading research tool for partially observable Markov decision processes that also enables new teaching opportunities. This talk will describe POMDPs.jl and the Decision Making under Uncertainty class at CU Boulder. Each assignment in this class includes an open-ended challenge problem where students implement algorithms in Julia that are auto-graded. The system enables challenging assignments such as programming MCTS with a 100ms time limit and DQN for reinforcement learning.