This study investigated how the information-sharing traits of an AI teammate, such as augmenting team memory (ATM) and changes in intra- and extra-team information (IET), can enhance situation awareness (SA) and responses to unexpected events in human-AI teams. Thirty-one teams of two participants and one AI flew a simulated UAV task with the AI’s information-sharing attribute set to ATM, IET, or control. Results showed that IET teams, whose AI provided direct solutions to task disruptions, were the most likely to overcome them. The ATM teams, whose AI helped teammates find the solution themselves, also outperformed the control group, but also exhibited more action communication, a better perceived shared mental model with the AI, and a more positive perception of SA compared to the IET condition. These results highlight that while providing direct, immediate AI solutions can improve responses to task disruptions, there can be a cost to team development.

Recent innovations in AI have allowed AI agents to work as collaborative teammates; however, these human-AI teams still face significant challenges in achieving high levels of effective coordination and collaboration. Focusing on the temporal nature of teaming, collaboration is often evaluated and improved through transition-phase discussions, held among teammates before or after achieving team goals. Using a mixed-methods approach, this study examines how AI teammates participate in transition-phase discussions and share various types of information, with a focus on situation awareness, impact on team cognition, trust, and performance in human-AI teams. Data from 31 teams completing a three-hour simulated uncrewed aerial system task, comprising four distinct rounds and two transition phases, were analyzed. Quantitative results indicated that AI involvement later in the team's life cycle fostered more trust in the AI teammate, as compared across the four rounds, and was associated with higher performance. Perceived team effectiveness also improved following transition phases the AI teammate participated in, irrespective of whether it occurred early or late in the team's life cycle. Qualitative findings revealed that AI involvement benefits transition phases, particularly when it prompts teammates to recall task details and develop shared knowledge. Based on these results, we demonstrate the value of AI teammates engaging in transition-phase discussions for human-AI teams and provide design recommendations for researchers and practitioners to improve the efficacy of HATs by implementing transition phases.

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.

As the world evolves, human-AI teams (HAT) have become increasingly more capable in their ability to complete task objectives. Due to this rising importance, it has become essential to understand the interpersonal dynamism between humans and AI to further optimise their performance potential. Given the demonstrated utility of emotional communication within human-human team structures, this research investigates the nature of AI-sourced positive emotions on human teammates. Through 47 interviews, our findings show that for these AI teammates to be accepted, human teammates have preferences on understanding the emotional utility prior to its presentation, as well as which emotions are situationally acceptable. Also, findings show that integrating emotions within AI teammates has a positive influence on human perceptions and behaviour in a task. In further detail, emotions act as status updates that allow human teammates to not only better understand their teammates' mental states but also understand how their AI teammates perceive the situation around them. Together, this gives insight into how AI emotional expressions influence the perception of social support on the wider Human-AI team. Mainly how emotions can be used to increase acceptance of AI teammates and improve the overall experience human teammates have within the task.

An emerging research agenda in Computer-Supported Cooperative Work focuses on human-agent teaming and AI agent's roles and effects in modern teamwork. In particular, one understudied key question centers around the construct of team cognition within human-agent teams. This study explores the unique nature of team dynamics in human-agent teams compared to human-human teams and the impact of team composition on perceived team cognition, team performance, and trust. In doing so, a mixed-method approach, including three team composition conditions (all human, human-human-agent, human-agent-agent), completed the team simulation NeoCITIES and completed shared mental model, trust, and perception measures. Results found that human-agent teams are similar to human-only teams in the iterative development of team cognition and the importance of communication to accelerating its development; however, human-agent teams are different in that action-related communication and explicitly shared goals are beneficial to developing team cognition. Additionally, human-agent teams trusted agent teammates less when working with only agents and no other humans, perceived less team cognition with agent teammates than human ones, and had significantly inconsistent levels of team mental model similarity when compared to human-only teams. This study contributes to Computer-Supported Cooperative Work in three significant ways: 1) advancing the existing research on human-agent teaming by shedding light on the relationship between humans and agents operating in collaborative environments, 2) characterizing team cognition development in human-agent teams; and 3) advancing real-world design recommendations that promote human-centered teaming agents and better integrate the two.