Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

Song, Binyang; Zurita, Nicolás F. Soria; Zhang, G.; Stump, Gary; Balon, Corey; Miller, Sonya K.; Yukish, Michael A.; Cagan, Jonathan; McComb, Christopher

doi:10.1017/dsd.2020.68

Cited by 23 publications

(21 citation statements)

References 39 publications

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“…Behavioural analyses in that previous work by the authors revealed that AI-assisted teams exhibited greater agility in adapting to structural shocks, performed fewer but more meaningful design actions, and explored more of the design space than human-only teams (Song et al, 2020). While only a preliminary analysis on team communication was conducted, this current work aims to expand that analysis to deepen the understanding of this fundamental process of AI-assisted human teams, specifically focusing on the discourse among the human team members.…”

Section: Introductionmentioning

confidence: 85%

“…The business discipline, consisting of a business manager, operations manager and design manager, focuses on market targeting; the operations discipline, consisting of two operations specialists and one operations manager, focuses on drone path planning; while the design discipline, consisting of one design specialist and one design manager focuses on drone design. More detailed descriptions regarding the experimental platform, problem statement, roles, and experimental methodology can be found in related work (Song et al, 2020).…”

Section: Drone Design Studymentioning

confidence: 99%

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Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 85%

Section: Drone Design Studymentioning

confidence: 99%

Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

et al. 2021

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“…Importantly, participants configure, test, and share their designs and communicate with their teammates in these experiments through an online research platform, called HyForm. 1 The architecture of the platform and the detailed information of the design and operation AI agents integrated in the platform are found in prior research [1] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Depending on the role, each participant has access to one of the following three interface modules. More details of each module are found in the prior research [1] . The drone design module enables members of the design team to construct drones.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on the design and operation of drones by both individuals and teams

et al. 2021

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Human subject experiments are performed to assess the impact of artificial intelligence (AI) agents on distributed human design teams and individual human designers. In the team experiment, participants in teams of six develop and operate a drone fleet to deliver parcels routed to multiple locations of a target market. Among the design teams in the experiment, half of the design teams are human-only teams with no available AI agent. The other half of the design teams, designated as hybrid teams, have drone design and operation AI agents to advise them. Halfway through the team experiment, team structure is changed unexpectedly, requiring participants to adapt to the change. In the individual experiment, participants develop drones based on given design specifications, either on their own or with the availability of a drone design AI agent to advise them. During these experiments, participants configure, test, and share their designs and communicate with their teammates through an online research platform. The platform collects a step-by-step log of the actions made by participants. This article contains data sets collected from 44 teams (264 participants) in the team experiment and 73 participants in the individual experiment. These data sets can be used for behavioral analysis, sequence-based analysis, and natural language processing.

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“…AI cannot effectively learn and replicate creativity [37], interpersonal empathy [38], [39], intuition [40], knowledge consolidation or persuasion for problem-solving, or to coordinate and lead teams [41], [42]. AI cannot fully replace human designers because of their lack of creativity and agility, which is needed to solve complex engineering design problems [43]. Computerizing creativity is limited by the programmer's ability to define the creative values clearly to be encoded into an algorithm [44].…”

Section: Limitations Of Ai In Engineering Designmentioning

confidence: 99%

A Pilot Study on the Prevalence of Artificial Intelligence in Canadian Engineering Design Curricula

Khanolkar

Gad

Liao

et al. 2021

PCEEA

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Recent advances in artificial intelligence (AI) have shed light on the potential uses and applications of AI tools in engineering design. However, the aspiration of a fully automated engineering design process still seems out of reach of AI’s current capabilities, and therefore, the need for human expertise and cognitive skills persists. Nonetheless, a collaborative design process that emphasizes and uses the strengths of both AI and human engineers is an appealing direction for AI in design. Touncover the current applications of AI, the authors review literature pertaining to AI applications in design research and engineering practice. This highlights the importance of integrating AI education into engineering design curricula in post-secondary institutions. Next, a pilot studyassessment of undergraduate mechanical engineering course descriptions at the University of Waterloo and University of Toronto reveals that only one out of a total of 153 courses provides both AI and design-related knowledge together in a course. This result identifies possible gaps in Canadian engineering curricula and potential deficiencies in the skills of graduating Canadianengineers.

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Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

Cited by 23 publications

References 39 publications

Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

Data on the design and operation of drones by both individuals and teams

A Pilot Study on the Prevalence of Artificial Intelligence in Canadian Engineering Design Curricula

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