TY - JOUR
T1 - Decoding the agility of artificial intelligence-assisted human design teams
AU - Song, Binyang
AU - Soria Zurita, Nicolas F.
AU - Gyory, Joshua T.
AU - Zhang, Guanglu
AU - McComb, Christopher
AU - Cagan, Jonathan
AU - Stump, Gary
AU - Martin, Jay
AU - Miller, Simon
AU - Balon, Corey
AU - Yukish, Michael
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/3
Y1 - 2022/3
N2 - Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy improved coordination and communications, leading to better performance and adaptations to team disruptions, while devoting more effort to information handling and exploring the solution space more broadly. In sum, working with AI enables human team members to think more and act less.
AB - Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy improved coordination and communications, leading to better performance and adaptations to team disruptions, while devoting more effort to information handling and exploring the solution space more broadly. In sum, working with AI enables human team members to think more and act less.
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U2 - 10.1016/j.destud.2022.101094
DO - 10.1016/j.destud.2022.101094
M3 - Article
AN - SCOPUS:85124483462
SN - 0142-694X
VL - 79
JO - Design Studies
JF - Design Studies
M1 - 101094
ER -