Some researchers have argued that providing operators with externalized, graphic representations can lead to a trade-off whereby deep knowledge is sacrificed for cognitive economy and performance. This article provides an initial empirical investigation of this hypothesis by presenting a longitudinal study of the effect of ecological interface design (EID), a framework for designing interfaces for complex industrial systems, on subjects' deep knowledge. The experiment continuously observed the quasi-daily performance of the subjects' over a period of six months. The research was conducted in the context of DURESS II, a real-time, interactive thermal-hydraulic process control simulation that was designed to be representative of industrial systems. The performance of two interfaces was compared, an EID interface based on physical and functional (P+F) system representations and a more traditional interface based solely on a physical (P) representation. Subjects were required to perform several control tasks, including startup, tuning, shutdown and fault management. Occasionally, a set of knowledge elicitation tests was administered to assess the evolution of subjects' deep knowledge of DURESS II. The results suggest that EID can lead to a functionally organized knowledge base as well as superior performance, but only if subjects actively reflect on the feedback they get from the interface. In contrast, if subjects adopt a surface approach to learning, then EID can lead to a shallow knowledge base and poor performance, although no worse than that observed with a traditional interface.