Design creativity

Engineering design relies on creative thought to produce new and exciting products, systems, and services. The study of creativity provides many opportunities for interdisciplinary research between engineering, cognitive science, and computer science. This special issue aims to capture a snapshot of some of the best work at this intersection of areas. The scope of this special issue was broadened in relation to the traditional AIEDAM scope to include papers that explicitly discuss creative thinking, types of reasoning, and explicit use of knowledge; such topics often influence the foundation of creative AI design systems. Papers were reviewed by experts in the fields of engineering design creativity, with at least three reviewers per paper. Papers were solicited in two main areas: foundational theory, such as understanding how, why, and what makes designs or designers creative, so as to provide useful performance bounds on computational creativity; and empirical outcomes, such as creative results, processes, or systems. The special issue has distinct themes that emerged naturally among the collection of papers. Within the foundational theory area, there is a strong focus on measuring creativity and design outcomes, with three of the seven papers (Kwon and Kudrowitz, 2018; Sääksjärvi and Gonçalves, 2018; and Ranjan et al., 2018). The measurement of creativity is an important part of design research, particularly when considering the intersection with artificial intelligence. As we strive for larger, more realistic data sets and more rigorous methods for formalizing design science, we may turn to AI to help process qualitative data more efficiently and objectively. These human subject-based studies will be the basis for the potential development of future automation in these areas. It is important to recognize, separate from any future automation, that the measurement of creativity and design outcomes is intrinsic to the validation of the computational design support systems, such as those described next in the empirical outcomes area. One additional paper within the foundational theory area is that of Studer et al. (2018), focusing on studying designer behavior in exploring problems during design, tying neatly into the empirical outcomes of supporting design space exploration with computational tools. Within the empirical outcomes, three papers (Siddharth and Chakrabarti, 2018; Luo et al., 2018; Han et al., 2018) share a common goal of support using design-by-analogy with datamining techniques, each addressing the problem in unique ways with case study validations of their systems.