Advancing How AI Can Augment Designer Performance
MIE Assistant Professor Mohsen Moghaddam is the PI of a $614K NSF grant with co-PIs Tucker Marion (D’Amore-McKim) and Paolo Ciuccarelli (CAMD), in collaboration with Lu Wang from the University of Michigan – Ann Arbor, for the project “From User Reviews to User-Centered Generative Design: Automated Methods for Augmented Designer Performance.” The work was initially funded by an FY20 TIER 1 Interdisciplinary Research Seed Grant.
Abstract Source: NSF
This project investigates design processes where the unmet needs of users are elicited from social media, online forums, and e-commerce platforms, and translated into new concept recommendations for designers using artificial intelligence (AI). The motivation stems from the growing abundance of user-generated feedback and a lack of advanced computational methods for drawing useful design knowledge and insights from that data. The research will establish a rigorous computational foundation that (1) enables large-scale elicitation of user needs from online reviews using advanced natural language processing (NLP) algorithms, and (2) translates the elicited needs into the visual and functional aspects of new concepts using novel generative adversarial networks (GAN) algorithms. The theoretical innovations will advance the fundamental understanding of how AI can augment the performance and creativity of designers in early-stage product development processes. This project will boost national competitiveness in innovation by creating tacit opportunities for designing innovative, inclusive, and competitive products. The convergent research team will create outreach initiatives for STEM students, teachers, and underrepresented minorities, and engage with industry and research stakeholders to ensure technology-market fit and successful dissemination.
The overarching goal of this project is to establish a transformative, data-driven paradigm for empathetic design that augments the ability of designers to uncover and address the critical yet latent needs of users at scale. The project will create scalable and computationally efficient NLP algorithms that capture the needs of ordinary users from reviews, identify the underlying usage contexts, and infer extreme use-cases to facilitate latent need elicitation. Focus groups and interviews involving ninety design experts and crowdsourced evaluators will be conducted to test the first research hypothesis: The NLP algorithms elicit needs that are nonobvious, difficult to identify, and provide significant value and originality. The project will build novel GAN architectures and algorithms for generative design of form and function conditioned on the elicited latent user needs. New multimodal deep regression models will be developed to evaluate the quality of the generated samples based on user feedback on existing products. Laboratory studies involving fifty subjects and fifty evaluators will be performed to test the second research hypothesis: The GAN-generated design recommendations significantly improve the quality and variety of the design concepts generated by human designers. The project will lead to broad societal outcomes by fostering designer-AI co-creation and innovation centered on empathy with users to bridge the gap between user need discovery and design outcomes.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.