If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

In an effort to improve customization for today's highly competitive global marketplace, many companies are utilizing product families and platform-based product development to increase variety, shorten lead times, and reduce costs. The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches. This nascent field of engineering design has matured rapidly in the past decade, and this paper provides a comprehensive review of the flurry of research activity that has occurred during that time to facilitate product family design and platform-based product development for mass customization. Techniques for identifying platform leveraging strategies within a product family are reviewed along with metrics for assessing the effectiveness of product platforms and product families. Special emphasis is placed on optimization approaches and artificial intelligence techniques to assist in the process of product family design and platform-based product development. Web-based systems for product platform customization are also discussed. Examples from both industry and academia are presented throughout the paper to highlight the benefits of product families and product platforms. The paper concludes with a discussion of potential areas of research to help bridge the gap between planning and managing families of products and designing and manufacturing them.

Recent advances in machine learning have enabled computers to converse with humans meaningfully. In this study, we propose using this technology to facilitate design conversations in large-scale urban development projects by creating chatbot systems that can automate and streamline information exchange between stakeholders and designers. To this end, we developed and evaluated a proof-of-concept chatbot system that can perform design conversations on a specific construction project and convert those conversations into a list of requirements. Next, in an experiment with 56 participants, we compared the chatbot system to a regular online survey, focusing on user satisfaction and the quality and quantity of collected information. The results revealed that, with regard to user satisfaction, the participants preferred the chatbot experience to a regular survey. However, we found that chatbot conversations produced more data than the survey, with a similar rate of novel ideas but fewer themes. Our findings provide robust evidence that chatbots can be effectively used for design discussions in large-scale design projects and offer a user-friendly experience that can help to engage people in the design process. Based on this evidence, by providing a space for meaningful conversations between stakeholders and expanding the reach of design projects, the use of chatbot systems in interactive design systems can potentially improve design processes and their outcomes.

Artificial intelligence and cognitive science are two core research areas in design. Artificial intelligence shows the capability of analysing massive amounts of data which supports making predictions, uncovering patterns and generating insights in varying design activities, while cognitive science provides the advantage of revealing the inherent mental processes and mechanisms of humans in design. Both artificial intelligence and cognitive science in design research are focused on delivering more innovative and efficient design outcomes and processes. Therefore, this thematic collection on “Applications of Artificial Intelligence and Cognitive Science in Design” brings together state-of-the-art research in artificial intelligence and cognitive science to showcase the emerging trend of applying artificial intelligence techniques and neurophysiological and biometric measures in design research. Three promising future research directions: 1) human-in-the-loop AI for design, 2) multimodal measures for design, and 3) AI for design cognitive data analysis and interpretation, are suggested by analysing the research papers collected. A framework for integration of artificial intelligence and cognitive science in design, incorporating the three research directions, is proposed to inspire and guide design researchers in exploring human-centred design methods, strategies, solutions, tools and systems.

Computer-aided design (CAD) plays an essential role in creative idea generation on 2D screens during the design process. In most CAD scenarios, virtual object translation is an essential operation, and it is commonly used when designers simulate their innovative solutions. The degrees of freedom (DoF) of virtual object translation modes have been found to directly impact users’ task performance and psychological aspects in simulated environments. Little is known in the existing literature about the sense of agency (SoA), which is a critical psychological aspect emphasizing the feeling of control, in translation modes on 2D screens during the design process. Hence, this study aims to assess users’ SoA in virtual object translation modes on mouse-based, touch-based, and handheld augmented reality (AR) interfaces through subjective and objective measures, such as self-report, task performance, and electroencephalogram (EEG) data. Based on our findings in this study, users perceived a greater feeling of control in 1DoF translation mode, which may help them come up with more creative ideas, than in 3DoF translation mode in the design process; additionally, the handheld AR interface offers less control feel, which may have a negative impact on design quality and creativity, as compared with mouse- and touch-based interfaces. This research contributes to the current literature by analyzing the association between virtual object translation modes and SoA, as well as the relationship between different 2D interfaces and SoA in CAD. As a result of these findings, we propose several design considerations for virtual object translation on 2D screens, which may enable designers to perceive a desirable feeling of control during the design process.

Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed 108 AI-based engineering design papers from peer-reviewed journals and conference proceedings and mapped their contribution to five stages of the engineering design process. We categorized seven AI-based methods in our dataset. Our literature study indicated that most AI-based design research works are targeted at the conceptual and preliminary design stages. Given the open-ended, ambiguous nature of these early stages, these results are unexpected. We conjecture that this is likely a result of several factors, including the iterative nature of design tasks in these stages, the availability of open design data repositories, and the inclination to use AI for processing computationally intensive tasks, like those in these stages. Our study also indicated that these methods support designers by synthesizing and/or analyzing design data, concepts, and models in the design stages. This literature review aims to provide readers with an informative mapping of different AI tools to engineering design stages and to potentially motivate engineers, design researchers, and students to understand the current state-of-the-art and identify opportunities for applying AI applications in engineering design.

The goal of this paper is to develop and test a gamified design thinking framework, including its pedagogical elements, for supporting various learning objectives for school students. By synthesizing the elements and principles of design, learning and games, the authors propose a framework for a learning tool for school students to fulfil a number of learning objectives; the framework includes a design thinking process called “IISC Design Thinking” and its gamified version called “IISC DBox”. The effectiveness of the framework as a learning tool has been evaluated by conducting workshops that involved 77 school students. The results suggest that the gamification used had a positive effect on the design outcomes, fulfilment of learning objectives, and learners' achievements, indicating the potential of the framework for offering an effective, gamified tool for promoting design thinking in school education. In addition to presenting results from empirical studies for fulfilment of the objectives, this paper also proposes an approach that can be used for identifying appropriate learning objectives, selecting appropriate game elements to fulfil these objectives, and integrating appropriate game elements with design and learning elements. The paper also proposes a general approach for assessing the effectiveness of a gamified version for attaining a given set of learning objectives. The methodology used in this paper thus can be used as a reference for developing and evaluating a gamified version of design thinking course suitable not only for school education but also for other domains (e.g., engineering, management) with minimal changes.

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

The term “creative space” describes a relatively recent phenomenon of innovative workplace design. Such creative workspaces are becoming popular in industry and academia. However, the impact of specific spatial design decisions on creativity and innovation is not yet fully understood. This paper provides an overview of state-of-the-art research on creative work and learning environments. We conducted a systematic literature search within the Scopus database and identified a total of 73 relevant sources discussing creative spaces within academic, practice, and other innovation environments. Among the included sources are 51 academic publications and 22 sources from company research and illustrative coffee-table books. We analyzed the sources using three lenses of interest: (1) the types of theoretical and practical contributions that are provided, (2) the spatial characteristics that are suggested to be beneficial for creativity and innovation, and (3) the discussed potential of new technologies for designing or researching creative spaces. The results provide in-depth insight into the current state of research on the topic of creative spaces. Practitioners, educators, and researchers can use the presented overview to investigate the possible impact of creative workspace design and identify research gaps that can be filled by conducting further research in the field.