Human-Centered Team Training for Human-AI Teams: From Training with AI Tools to Training for AI Teammates

作者：Caitlin Lancaster, Wen Duan, Rohit Mallick, Nathan J. McNeese · 发表于：Proceedings of the ACM on Human-Computer Interaction · 年份：2025 · DOI：10.1145/3710998 · 被引用次数：9 · 研究领域：Big Data and Business Intelligence、Human-Automation Interaction and Safety、Technology Assessment and Management

AI increasingly assumes complex roles in Human-AI Teaming (HAT). However, communication and trust issues between humans and AI often hinder effective collaboration within HATs, highlighting a need for effective human-centered team training, an area significantly understudied. To address this gap, we interviewed eSports athletes and team-based, competitive gamers (N=22), a group experienced in HATs and team training, about their HAT team training needs and desires. Through the lens of Quantitative Ethnography (QE), we analyzed their insights to understand preferred team training strategies and the desired roles of AI within these strategies, considering the varying levels of human expertise. Our findings reveal a strong preference across all expertise levels for cross-training, which is training in other teammate roles, to improve perspective taking and coordination in HATs. Less experienced participants prefer structured procedural training, while experts favor self-correction methods for growth. Additionally, participants desired that AI act as a companion, with beginners and intermediates valuing AI's functional roles, and experts seeking AI in a coaching role. Among the first to emphasize human-centered team training in HATs, this study contributes to CSCW/HCI research by revealing varied preferences for training and AI roles, emphasizing the need to tailor these aspects to team dynamics and individual skills for better outcomes in HATs.