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ABSTRACTThe analysis of conversations during design activity can facilitate deeper insights into design thinking and its relation to creativity. A semantic analysis approach was employed to explore the semantic content of communication and information exchange between students and instructors. The goal was to examine design conversations in terms of abstraction, Polysemy, Information Content, and Semantic Similarity measures, and analyze their relation to the creativity of final solutions. These design outcomes were assessed according to their Originality, Usability, Feasibility, Overall Value, and Overall Creativity. Consequently, 35 design conversations from the 10th Design Thinking Research Symposium (DTRS10) dataset were analyzed. The main results showed that Information Content and Semantic Similarity predicted Originality, and Information Content alone predicted Overall Creativity. Likewise, Abstraction predicted Feasibility, while Semantic Similarity, Information Content, and Polysemy predicted Overall Value. In context of instructors, Semantic Similarity predicted Usability, and Polysemy predicted Feasibility. For students, Semantic Similarity predicted Overall Value. On the whole, Semantic Similarity and Information Content were the most prolific measures, and therefore could be considered for promoting creativity in the design studio. The implications of using support tools such as automated systems are also discussed.

AbstractIn order to support the design of products with innovative desired functions (creating the desired functions), this study aims to develop a methodology to create new scenes in which a new product may be used by synthesizing the existing scenes. The development of this methodology focused on thematic relations, which are the relationships between two objects through a thematic scene, such as “an apple is cut by knife.” Thematic relations enable us to represent and manipulate the scenes in a computational manner. In particular, we highlighted the synthesis of the scenes which belong to the different contexts (periods in time, culture, and technology). We propose a specific methodology in which thematic relations are used to construct a database of existing scenes and then create a new scene using this database. This methodology was implemented in a computer system. Case studies were provided to demonstrate the use of this system.