Decoding the agility of artificial intelligence-assisted human design teams. (March 2022)

• Record Type: Journal Article
• Authors: Song, Binyang; Gyory, Joshua T.; Zhang, Guanglu; Soria Zurita, Nicolas F.; Stump, Gary; Martin, Jay; Miller, Simon; Balon, Corey; Yukish, Michael; McComb, Christopher; Cagan, Jonathan
• Is Part Of: Design studies. Volume 79(2022)
• Publication Date: 2022-03
• Subjects: artificial intelligence -- collaborative design -- engineering design -- human–computer interaction -- complex problem solving; Design -- Periodicals; 745.405
• DOI: 10.1016/j.destud.2022.101094
• Languages: English
• ISSNs: 0142-694X

Abstract

Abstract : 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. Highlights: Assistive AI boosts team agility with respect to both evolving and abrupt changes. The AI-assisted teams outperform the human-only teams. The AI assistance improves team action effectiveness and behavioral flexibility. The AI assistance results in more information-intensive team coordination. The AI-assisted teams explore the drone design and operations spaces more broadly.