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COMMUNICATION IN AI-ASSISTED TEAMS DURING AN INTERDISCIPLINARY DRONE DESIGN PROBLEM

Published online by Cambridge University Press:  27 July 2021

Joshua T. Gyory ,
Binyang Song ,
Jonathan Cagan  and
Christopher McComb
Joshua T. Gyory
Affiliation:
Carnegie Mellon University;
Binyang Song
Affiliation:
Pennsylvania State University
Jonathan Cagan*
Affiliation:
Carnegie Mellon University;
Christopher McComb
Affiliation:
Pennsylvania State University
*
Cagan, Jonathan, Carnegie Mellon University, Mechanical Engineering, United States of America, [email protected]

Abstract

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Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.

Keywords

Artificial intelligenceCommunicationHuman behaviour in designHybrid teaming
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 651 - 660
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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