Computers are not smart enough yet to design and thus alleviate the need for a designer, although they can offer various possibilities of assistant to the designer. Various learning techniques provided in machine learning and artificial intelligence, such as dynamic programming and reinforcement learning, are making computers smarter at so many of the tasks humans wish to pursue. But even at this level of intelligence, computers make design decisions by processing information about the environment or with which they have been fed. In Architectural, Engineering, and Construction (AEC) domain, the overall building or final product configuration is defined in preliminary stages based on design specifications and principles. The design process continues until it satisfies or exceeds clients or end users’ (e.g. building occupants) expectations and needs. Thus, understanding client design feedback and evaluation is vital input for smart computer designers. Measuring of satisfaction is a relatively new concept in AEC industry that has been relied exclusively on questionnaire and interview data. The objective of this study is to provide a quantitative way to understand how people evaluate a design and rate the level of satisfaction through an innovative use of psychological measurements. The eye-tracking technology is used to collect gaze data of the client and an automated facial expression tool is used to capture the emotions derived from the facial expressions of the client. These data are synchronized and then combined with the results of a questionnaire to address how to better understand client’s evaluation and satisfaction without direct feedback from him or her during a design process.
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