Introduction

Computer system developers often speak of the ‘coupling’ of human intelligence with machine power in a single, interactive system that substantially enhances performance. But achieving this objective is not primarily a matter of deciding how to allocate functions between the machine components and the human elements, as much of the literature on human factors in expert and automated systems would have us believe. Without denying that this allocation problem has some heuristic relevance, the most important and vexing issue facing developers is how to build effective tools that take the fundamental differences between human action and machine operation into account.

Although several studies of human–machine interaction have demonstrated the significance of these differences for effective expert system design and deployment (e.g. Suchman, 1987; Hartland, 1993; Whalen, 1995a), and both cognitive science and the artificial intelligence (AI) research underpinning expert system design have been subjected to farranging criticism for their views on human action (Coulter, 1983, 1989; Winograd and Flores, 1986; H. Collins, 1990; Dreyfus, 1992; Button et al., 1995; Hutchins, 1995; Clancey, 1997), most artificial intelligence practitioners have continued to assume that machines can do, or can in principle be designed to do, what humans do. Accordingly, they remain focused on the allocation problem, and have been intrigued about the possibilities for designing expert applications that contain most, if not all, of the knowledge required to perform a task or solve a problem, with the ‘knowledgeability’ of the user confined largely to data entry and information retrieval procedures.