任务(项目管理)
知识管理
团队效能
团队构成
过程(计算)
探索性研究
信息共享
心理安全
心理学
动力学(音乐)
计算机科学
信息系统
团队学习
应用心理学
任务分析
信息技术
团队合作
信息搜寻
作者
Sophie Kerstan,Gudela Grote,Jan B. Schmutz
出处
期刊:Human Factors
[SAGE Publishing]
日期:2026-09-08
卷期号:: 187208261477411-187208261477411
标识
DOI:10.1177/00187208261477411
摘要
Objective We investigate whether decision-making processes in human-AI teams mirror those in human-only teams in a task where team members hold common and unique information. Specifically, we examine how information inquiry and performance expectations influence these processes. Background Human-AI decision-making has traditionally been studied as a linear, segmented process where AI provides recommendations and humans make final decisions. However, recent advancements in large language models (LLMs) enable increasingly dynamic interactions similar to the collaborative processes in human-only teams, where members have to share and integrate unique information to reach decisions. Despite the potential for similar dynamics in human-AI teams, research on such configurations—particularly on when unique information is shared—remains limited. Method We conducted an experiment using a hidden profile task, a well-established paradigm in team research. We manipulated team type ( N human-AI = 98; N human-only = 65, three-member teams) and information inquiry, and created conditions with differing performance expectations. Results Information inquiry enhanced information sharing and, consequently, improved decision-making performance, irrespective of performance expectations and team type. Exploratory analyses suggested that while this effect was consistent across team types, information inquiry patterns may differ. Conclusion Our findings prompt avenues for future research on how humans’ communication norms vary when interacting with AI versus human team members. Methodologically, this study demonstrates how established decision-making paradigms can be adapted to incorporate AI, offering insights into both human-only and human-AI team processes. Application The results have practical implications for designing AI systems that promote effective team decision-making by engaging in information inquiry.
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