Recent research has demonstrated that knowledge about specific instances may be of more relevance to reasoning than has previously been assumed. Students can rely on principles they have learned, or they can recall something similar previously experienced, and base the new prediction on it in an instance-based approach. Instance-based (or case-based) problem solving is important in simulation environments. The CAse-BAsed Tutor (CABAT) is a tool that supports case-based learning in a simulation environment, specifically the computer-simulated laboratory environment DIBI. CABAT stores simulated experiments done by the student and uses the episodic knowledge when the student does a new experiment, reminding the student of the former solution. The interface for elaborated examples is described, and some early empirical results with a chess game that used the same approach are presented. Three figures illustrate the simulation approach. (Contains 17 references.) (SLD)