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Leveraging Artificial Intelligence for Team Cognition in Human-AI Teams.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Leveraging Artificial Intelligence for Team Cognition in Human-AI Teams./
Author:	Schelble, Beau.
Description:	1 online resource (336 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Design. -
Online resource:	click for full text (PQDT)
ISBN:	9798382654669

Leveraging Artificial Intelligence for Team Cognition in Human-AI Teams.
Schelble, Beau.

Leveraging Artificial Intelligence for Team Cognition in Human-AI Teams. - 1 online resource (336 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Clemson University, 2023.

Includes bibliographical references

Advances in artificial intelligence (AI) technologies have enabled AI to be applied across a wide variety of new fields like cryptography, art, and data analysis. Several of these fields are social in nature, including decision-making and teaming, which introduces a new set of challenges for AI research. While each of these fields has its unique challenges, the area of human-AI teaming is beset with many that center around the expectations and abilities of AI teammates. One such challenge is understanding team cognition in these human-AI teams and AI teammates' ability to contribute towards, support, and encourage it. Team cognition is defined as any cognitive activity among the team members regarding their shared knowledge of the team and task, including concepts such as shared task or team mental models, team situation awareness, or schema similarity. Team cognition is fundamental to effective teams, as it is directly linked to the successful and efficient execution of team processes and subsequent team outcomes from decades of research in human-only teaming. However, the construct is challenging in human-AI teams given the significant differences in how we interact with humans versus AI (communication is a notable example). Despite the importance and knowledge of team cognition in human teams, there is little to no empirical research on the construct within human-AI teams. Without this research on human-AI teams, it is difficult to understand how the findings for human teams compare and apply to human-AI teams, leaving practitioners of human-AI teams in the dark.Very few studies directly examine what components of team cognition are most important to the human teammates working with AI or how including one or more AI teammates affects team cognition development. Without this knowledge, the ability to design better AI teammates that support and encourage the development of team cognition in human-AI teams will be a difficult task, and the need for extensive research in this area is apparent. As such, the current dissertation presents three studies that iterate upon one another to determine how AI teammates influence team cognition, what aspects of team cognition are most important to human-AI teams, and how AI teammate design may support contributions to team cognition.The initial study of this dissertation utilized a mixed-methods approach to investigate how including AI teammates affects team cognition development in content, structure, and perception. This study also examines how human teammates' attitudes towards AI teammates change alongside those manipulations in team composition. Study 1 found that human-AI teams are similar to human-only teams in that team cognition develops over time; however, human-AI teams are different in that communication containing specific information related to the task and explicitly shared goals are more beneficial to developing team cognition. Additionally, human-AI teams trusted AI teammates less when working with only AI and no other humans, making AI contributions to team cognition difficult in teams with a majority AI composition. Perceived team cognition was also lower for AI teammates than human ones and had significantly inconsistent levels of team mental model similarity compared to human-only teams. These findings highlight the importance of information-sharing attributes of AI teammates to contribute to team cognition and drive the focus of the subsequent study.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382654669Subjects--Topical Terms:

595500
Design.
Index Terms--Genre/Form:

554714
Electronic books.

Leveraging Artificial Intelligence for Team Cognition in Human-AI Teams.
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