Generative design uses artificial intelligence-driven algorithms to create and optimize concept variants that meet or exceed performance requirements beyond what is currently possible using the traditional design process. However, current generative design tools lack the integration of human factors, which diminishes the efforts to understand and inject a broad set of human capabilities, limitations, and potential emotional responses for future human-centered product and service innovation. This paper demonstrates collaborative research in formulating a human-centered generative design framework that injects human factors early in the design for quick-and-dirty concept creation and evaluation. Three case studies overviewing our ongoing multidisciplinary research efforts in synthesizing human and mechanical attributes are presented. The results show that the framework has the potential to enhance human factors representation within generative design workflow. Strategies from a computational design perspective, such as data-driven generative design, digital human modeling, and mixed-reality validation, are discussed as alternative approaches that could be implemented to augment designers.

The effective use of computational modeling and simulation tools early in the design process is arguably becoming a gold standard for modern product development. Compared to many mechanistic computational design approaches, modeling and simulating humans, due to their inherent complexities of physiological and cognitive attributes, provides one of the most challenging undertakings. With the rapidly expanding use of computer, sensor, and visualization technologies, digital human modeling (DHM) emerged as a computerized design support methodology that enables modeling and simulation of humans within a computer-aided design (CAD) or virtual environment (VE). Implementing DHM with physical or digital mockups brings the advantages of running various “what-if” design scenarios early in design; thus, enhancing concept generation efforts by filtering out infeasible ideas and exploring better design alternatives. A modern product development process with DHM can also help to reduce the overall cost and time required in the long run. Although several DHM software packages are available and many companies have been designing with DHM, the domain has not reached maturity in resolving theoretical research questions and fostering simulation-based ergonomics practice. Besides, the growing body of literature, software platforms, and technology integration makes it challenging for newcomers and specialists from disciplines other than human factors engineering (HFE) to recognize the power of DHM tools. This paper aims to provide a comprehensive review of the DHM domain and summarize the evolution, current status, and future trends of the DHM design support tools. We hope this review will provide a guideline for designers and serve as a roadmap for current and future researchers interested in DHM-related research to find out new venues and opportunities for further international collaborations.

Collective robotic systems are biologically inspired and advantageous due to their apparent global intelligence and emergent behaviors. Many applications can benefit from the incorporation of collectives, including environmental monitoring, disaster response missions, and infrastructure support. Transparency research has primarily focused on how the design of the models, visualizations, and control mechanisms influence human-collective interactions. Traditionally most transparency research has evaluated one system design element. This article analyzed two models and visualizations to understand how the system design elements impacted human-collective interactions, to quantify which model and visualization combination provided the best transparency, and provide design guidance, based on remote supervision of collectives. The consensus decision-making and baseline models, as well as an individual collective entity and abstract visualizations, were analyzed for sequential best-of- n decision-making tasks involving four collectives, composed of 200 entities each. Both models and visualizations provided transparency and influenced human-collective interactions differently. No single combination provided the best transparency.

This work-in-progress, set in the context of an introductory design and graphics course, explores student reasoning when tasked with choosing a “best” option among solutions developed from a CAD-based generative design solution space. Students tend to cite rationalistic reasoning in design decision-making, rather than relying on intuition or on empathy for users. Future work will explore the relationship between traditional undergraduate engineering design task decision-making and generative design decision-making.