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.

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.

Increased use of collaborative technologies and agile teamwork models has led to a greater need for temporary teams. Unfortunately, they lack the normal team formation processes that traditional teams use. Information sharing recommender systems can be used to share information about team members amongst the team; however, these systems rely on the team members themselves to disclose valuable information. While prior research has shown that an effective way to encourage user disclosure is through explanations to the user about what benefits they will gain from disclosure, the timing of such explanations has yet to be consideblack. In a between-subjects study with 150 participants, we assessed the content and timing of explanations on levels of disclosure in temporary teams. Our results indicate that providing benefit-related explanations during the time of disclosure can increase user disclosure, and providing benefit-related explanations during the recommendation process can increase user trust in the system. These results provide important design implications for teams and the HCI community.

In the coming years, Artificial Intelligence (AI) will be applied as a teammate that works alongside and collaborates with humans. Prior research in teaming and CSCW has shown that teammates have the ability to change the thoughts and behaviors of each other through simple interactions in a process known as social influence. However, to date, research has yet to identify the social influence that AI teammates could have in these human-AI teams, which has led to a limited understanding of how AI teammates will change the behaviors of their human teammates. To remedy this gap, we conduct a mixed-methods study (N=33) with young individuals to explore how humans could behaviorally adapt and perceive their behavioral adaptation due to interaction with an AI teammate. Qualitative results report that perceived three unique stages they had to experience for the social influence of their AI teammate to lead to adaptation (i.e., perceiving a sense of control, identifying a technological or performative justification, and gaining first-hand experience). Quantitative results validate and illustrate the results of this perceived process, as results show that participants adapted their behaviors to complement the behaviors of different types of AI teammates. This study contributes to the CSCW/HCI field by developing an initial understanding of AI teammates' social influence in human-AI teams, which will be a pivotal design and research consideration in future efforts.