Robot motion planning is a fairly intuitive and engaging topic, yet it is difficult to teach. The material is taught in undergraduate and graduate robotics classes in computer science, electrical engineering, mechanical engineering and aeronautical engineering, but at an abstract level. Deep learning could be achieved by having students implement and test different motion planning strategies. However, it is practically impossible in the context of a single class to have undergraduates implement motion planning algorithms that are powerful and fun to use, even when the students have proficient programming skills. Due to lack of supporting educational material, students are often asked to implement simple (and uninteresting) motion planning algorithms from scratch, or access thousands of lines of code and just figure out how things work. We present an ongoing project to develop microworld software and a modeling curriculum that supports undergraduate acquisition of motion planning knowledge and tool use by computer science and engineering students. The goal is to open the field of motion planning and robotics to young and enthusiastic talent.