TY - JOUR

T1 - A Hybrid Intelligence Approach to Training Generative Design Assistants

T2 - Partnership Between Human Experts and AI Enhanced Co-Creative Tools

AU - Mao, Yaoli

AU - Rafner, Janet

AU - Wang, Yi

AU - Sherson, Jacob Friis

PY - 2023

Y1 - 2023

N2 - The emergence of generative design (GD) has introduced a new paradigm for co-creation between human experts and AI systems. Empirical findings have shown promising outcomes such as augmented human cognition and highly creative design products. Barriers still remain that prevent individuals from perceiving and adopting AI, entering into collaboration with AI and sustaining it over time. It is even more challenging for creative design industries to adopt and trust AI where these professionals value individual style and expression, and therefore require highly personalized and specialized AI assistance. In this paper, we present a holistic hybrid intelligence (HI) approach for individual experts to train and personalize their GD assistants on the fly. Our contribution to human-AI interaction is three-fold including i) a programmable common language between human and AI to represent the expert’s design goals to the generative algorithm, ii) a human-centered continual training loop to seamlessly integrate AI-training into the expert’s task workflow, iii) a hybrid intelligence narrative to address the psychological willingness to spend time and effort training such a virtual assistant. This integral approach enables individuals to directly communicate design goals to AI and seeks to create a psychologically safe space for adopting, training and improving AI without the fear of job-replacement. We concertize these constructs through a newly developed Hybrid Intelligence Technology Acceptance Model (HI-TAM). We used mixed methods to empirically evaluate this approach through the lens of HI-TAM with 8 architectural professionals working individually with a GD assistant to co-create floor plan layouts of office buildings. We believe that the proposed approach enables individual professionals, even non-technical ones, to adopt and trust AI-enhanced co-creative tools.

AB - The emergence of generative design (GD) has introduced a new paradigm for co-creation between human experts and AI systems. Empirical findings have shown promising outcomes such as augmented human cognition and highly creative design products. Barriers still remain that prevent individuals from perceiving and adopting AI, entering into collaboration with AI and sustaining it over time. It is even more challenging for creative design industries to adopt and trust AI where these professionals value individual style and expression, and therefore require highly personalized and specialized AI assistance. In this paper, we present a holistic hybrid intelligence (HI) approach for individual experts to train and personalize their GD assistants on the fly. Our contribution to human-AI interaction is three-fold including i) a programmable common language between human and AI to represent the expert’s design goals to the generative algorithm, ii) a human-centered continual training loop to seamlessly integrate AI-training into the expert’s task workflow, iii) a hybrid intelligence narrative to address the psychological willingness to spend time and effort training such a virtual assistant. This integral approach enables individuals to directly communicate design goals to AI and seeks to create a psychologically safe space for adopting, training and improving AI without the fear of job-replacement. We concertize these constructs through a newly developed Hybrid Intelligence Technology Acceptance Model (HI-TAM). We used mixed methods to empirically evaluate this approach through the lens of HI-TAM with 8 architectural professionals working individually with a GD assistant to co-create floor plan layouts of office buildings. We believe that the proposed approach enables individual professionals, even non-technical ones, to adopt and trust AI-enhanced co-creative tools.

U2 - 10.3233/FAIA230078

DO - 10.3233/FAIA230078

M3 - Journal article

VL - 368

SP - 108

EP - 123

JO - Frontiers in Artificial Intelligence and Applications

JF - Frontiers in Artificial Intelligence and Applications

SN - 0922-6389

ER -