Research in learning technologies is often focused on optimizing some aspects of human learning. However, the usefulness of practical learning environments is heavily influenced by their weakest aspects, and, unfortunately, there are many things that can go wrong in the learning process. In this article, we argue that in many circumstances, it is more useful to focus on avoiding stupidity rather than seeking optimality. To make this perspective specific and actionable, we propose a definition of stupidity, a taxonomy of undesirable behaviors of learning environments, and an overview of data-driven techniques for finding defects. The provided overview is directly applicable in the development of learning environments and also provides inspiration for novel research directions and novel applications of existing techniques.