Decoding the agility of artificial intelligence-assisted human design teams

Binyang Song, Nicolas F. Soria Zurita, Joshua T. Gyory, Guanglu Zhang, Christopher McComb, Jonathan Cagan, Gary Stump, Jay Martin, Simon Miller, Corey Balon, Michael Yukish

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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.

Original languageEnglish (US)
Article number101094
JournalDesign Studies
Volume79
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

  • Architecture
  • Arts and Humanities (miscellaneous)
  • General Engineering
  • General Social Sciences
  • Computer Science Applications
  • Artificial Intelligence

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