✍ Scribed by K. Nakakoji
No coin nor oath required. For personal study only.
Although design as a discipline has been an enduring interest of the both AI and HCI communities, the impact of such interest has yet to be felt in the available design support tools. The practicing design community has been frustrated by the limited range of interaction techniques especially for the early stages of design. As they stand, existing support tools are more suited to later-stage designing.
The decisions we take in early design are often the most influential, imposing key constraints on our view of the problem space and thereby shaping later downstream decisions. In contrast to later stages of decision making, the tools we have for doing early stage design-the methods of analysis, the representations, and the computational instruments-are relatively unstructured, and ad hoc. At present, it seems that pencil and paper sketching, a discussion over a coffee, or even playing with blocks and physical stuff is more useful than currently available computational tools. Existing design-support tools are more suitable for later stages, when our design direction is more focused and more formalized, and our strategy is clearer.
The early stages of design are cognitively intensive, and not clearly structured in the same sense as problem-solving activities. The tasks involved in early stages of design are characterized by tentative, trial-and-error, and explorative processes with ambiguous and imprecise representations. Designers employ metaphors, allow ideas to remain open, identify loosely defined requirements and hold a number of possible solutions concurrently within a broad problem framework.
Such characteristics make the type of task informal, indecisive, and very humane. Put another way, by taking the issues of early design seriously we are confronting some of the hard questions of both computer science and design. It may not be easy, but it has the potential of being very rewarding.
Our challenge has been to support these early stages of design with methods and tools that result in better design processes and consequently better end-products than conventional approaches. The challenge is especially acute as more and more designers employ computational design environments as their primary creative medium.
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