Laboratory activity is an indispensable part of science and engineering education. To develop children’s interest in science and engineering, we want to create hands-on activities using artificial intelligence. In this paper, we first describe the use of case-based reasoning (CBR) and an existing knowledge base to yield a combinatorial design space for experiments. We then apply automated planning techniques to generate experiment procedures. We further use functional modeling to represent the experiment devices and demonstrate how that representation enables the planner to generate a valid Rube Goldberg Machine. Finally, a semantic similarity metric is proposed to evaluate the quality of a generated chain of experiments.