TY - GEN
T1 - ADAPTATION AND CHALLENGES IN HUMAN-AI PARTNERSHIP FOR THE DESIGN OF COMPLEX ENGINEERING SYSTEMS
AU - Xu, Zeda
AU - Hong, Chloe
AU - Soria Zurita, Nicolás F.
AU - Gyory, Joshua T.
AU - Stump, Gary
AU - Nolte, Hannah
AU - Cagan, Jonathan
AU - McComb, Christopher
N1 - Publisher Copyright:
© 2023 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2023
Y1 - 2023
N2 - Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.
AB - Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.
KW - Engineering Design
KW - Human-AI Partnership
KW - Hybrid Teams
UR - http://www.scopus.com/inward/record.url?scp=85165439606&partnerID=8YFLogxK
U2 - 10.1115/DETC2023-115176
DO - 10.1115/DETC2023-115176
M3 - Contribución a la conferencia
AN - SCOPUS:85165439606
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 49th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Y2 - 20 August 2023 through 23 August 2023
ER -