Classroom assignments are an effective way for students to investigate many important (and fun) aspects of artificial intelligence. However, for any project of reasonable breadth and depth, especially involving multiple agents and graphics, most of the programming goes into tedious, error-prone administrative tasks. Moreover, time constraints usually preclude an extensible and reusable design, so this overhead repeats itself throughout the semester. This pedagogy-oriented modeling-and-simulation framework provides all the necessary support capabilities to get students up to speed quickly on playing with a variety of AI content. It contains extensive, highly configurable, yet user-friendly, engineering, physics, and communication models for arbitrary components within a definable task environment. These components are managed automatically in a stochastic simulation that allows students to define, test, and evaluate their performance over a wide range of controlled experiments.