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.

As AI technologies advance, teams are beginning to see AI transition from a tool to a full-fledged teammate. Introducing an AI teammate brings several challenges, ranging from how human teammates perceive their new AI teammates from an affective standpoint to how AI should engage in the various teaming behaviors that make up effective teamwork. The current study used a mixed factorial survey and structural equation modeling to assess how participants in hypothetical human-AI teams respond to various forms of AI information-sharing, including information related to explainability, back-up behavior, situational awareness, and augmenting team memory. The study's results found that AI design features related to situational awareness and augmenting the teams' memory had the strongest effect on participants' attitudes and perceived team cognition with their teammates. However, much of this effect was mediated by participants' affective attitudes towards the AI as a teammate, with higher ratings leading directly to higher levels of perceived team cognition constructs. These results highlight the importance of fostering positive attitudes towards AI teammates, such as trust and cohesion in human-AI teams, to support the development of effective team cognition and the ability of AI information-sharing to bring about such positive impacts.

Technical and practical advancements in Artificial Intelligence (AI) have led to AI teammates working alongside humans in an area known as human-agent teaming. While critical past research has shown the benefit to trust driven by the incorporation of interaction rules and structures (i.e. etiquette) in both AI tools and robotic teammates, research has yet to explicitly examine etiquette for digital AI teammates. Given the historic importance of trust within human-agent teams, the identification of etiquette’s impact within said teams should be paramount. Thus, this study empirically evaluates the impact of AI teammate etiquette through a mixed-methods study that compares AI teammates that either adhere to or ignore traditional etiquette standards for machine systems. The quantitative results show that traditional etiquette adherence leads to greater trust, perceived performance of the AI, and perceived performance of the team as a whole. However, qualitative results reveal that not all traditional etiquette behaviors have universal appeal due to the presence of individual differences. This research provides the first empirical and explicit exploration of etiquette within human-agent teams, and the results of this study should be used further design specific etiquette behaviors for AI teammates.

The paper reports on two empirical studies that provide the first examination into how the presentation of an AI teammate’s identity, responsibility, and capability impacts humans’ perception surrounding AI teammate adoption before interacting as teammates. Study 1’s results indicated that AI teammates are accepted when they share equal responsibility on a task with humans, but other perceptions such as job security generally decline the more responsibility AI teammates have. Study 1 also revealed that identifying an AI as a tool instead of a teammate can have small benefits to human perceptions of job security and adoption. Study 2 revealed that the negative impacts of increasing responsibility can be mitigated by presenting AI teammates’ capabilities as being endorsed by coworkers and one’s own past experience. This paper discusses how to use these results to best balance the presentation of AI teammates’ capabilities and responsibilities, as well as identifying AI as teammates.

Explanation of artificial intelligence (AI) decision-making has become an important research area in human–computer interaction (HCI) and computer-supported teamwork research. While plenty of research has investigated AI explanations with an intent to improve AI transparency and human trust in AI, how AI explanations function in teaming environments remains unclear. Given that a major benefit of AI giving explanations is to increase human trust understanding how AI explanations impact human trust is crucial to effective human-AI teamwork. An online experiment was conducted with 156 participants to explore this question by examining how a teammate’s explanations impact the perceived trust of the teammate and the effectiveness of the team and how these impacts vary based on whether the teammate is a human or an AI. This study shows that explanations facilitate trust in AI teammates when explaining why AI disobeyed humans’ orders but hindered trust when explaining why an AI lied to humans. In addition, participants’ personal characteristics (e.g., their gender and the individual’s ethical framework) impacted their perceptions of AI teammates both directly and indirectly in different scenarios. Our study contributes to interactive intelligent systems and HCI by shedding light on how an AI teammate’s actions and corresponding explanations are perceived by humans while identifying factors that impact trust and perceived effectiveness. This work provides an initial understanding of AI explanations in human-AI teams, which can be used for future research to build upon in exploring AI explanation implementation in collaborative environments.

Teammates powered by artificial intelligence (AI) are becoming more prevalent and capable in their abilities as a teammate. While these teammates have great potential in improving team performance, empirical work that explores the impacts of these teammates on the humans they work with is still in its infancy. Thus, this study explores how the inclusion of AI teammates impacts both the performative abilities of human-AI teams in addition to the perceptions those humans form. The current study found that participants perceiving their third teammate as artificial performed worse than those perceiving them as human. Furthermore, these performance differences were significantly moderated by the task’s difficulty, with participants in the AI teammate condition significantly outperforming participants perceiving a human teammate in the highest difficulty task, which diverges from previous human-AI teaming literature. Alternatively, no significant effect of perceived teammate artificiality was found on shared mental model similarity. However, it did significantly affect participants’ levels of perceived team cognition. Individual performance on medium difficulty maps also mediated the effect of perceived teammate artificiality on perceived team cognition. These results further build on the current understanding of how AI teammates impact perceptions of individual human teammates and how those perceptions subsequently impact their objective performance, which is critical in building more effective AI teammates to incorporate alongside humans.

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.

Awareness, and specifically, spatial awareness, has long played a pivotal role in Computer-Supported Cooperative Work research in both theory and design. This significant background gives awareness the ability to answer challenges facing human-agent teams in communication and shared understanding. As such, the current study investigates the effects of spatial information level (low, high) on the development of team cognition and its outcomes in varying compositions of human-agent teams (human-human-agent, human-agent-agent) versus human-only (human-human-human) teams. The mixed-methods study had teams complete several rounds of the NeoCITIES emergency response management simulation and complete various team cognition and perception measures, followed by qualitative free-response questions. The study found that human-only teams did not perform at the same level as human-agent teams, with multi-agent human-agent teams having the best performance. A significant interaction, though with inconclusive simple main effects, displayed the trend that human-agent teams had better team mental model similarity when spatial awareness was high rather than low, while human-only teams experienced the reverse trend. Qualitative findings identified that high spatial awareness jump-started team cognition development, fostered more accurate shared mental models, enhanced the explainability of the agent, and helped the iterative development of team cognition over time.

With artificial intelligence continuing to advance, so too do the ethical concerns that can potentially negatively impact humans and the greater society. When these systems begin to interact with humans, these concerns become much more complex and much more important. The field of human-AI teaming provides a relevant example of how AI ethics can have significant and continued effects on humans. This paper reviews research in ethical artificial intelligence, as well as ethical teamwork through the lens of the rapidly advancing field of human-AI teaming, resulting in a model demonstrating the requirements and outcomes of building ethical human-AI teams. The model is created to guide the prioritization of ethics in human-AI teaming by outlining the ethical teaming process, outcomes of ethical teams, and external requirements necessary to ensure ethical human-AI teams. A final discussion is presented on how the developed model will influence the implementation of AI teammates, as well as the development of policy and regulation surrounding the domain in the coming years.

There are many unknowns regarding the characteristics and dynamics of human-AI teams, including a lack of understanding of how certain human-human teaming concepts may or may not apply to human-AI teams and how this composition affects team performance. This article outlines an experimental research study that investigates essential aspects of human-AI teaming such as team performance, team situation awareness, and perceived team cognition in various mixed composition teams (human-only, human-human-AI, human-AI-AI, and AI-only) through a simulated emergency response management scenario. Results indicate dichotomous outcomes regarding perceived team cognition and performance metrics, as perceived team cognition was not predictive of performance. Performance metrics like team situational awareness and team score showed that teams composed of all human participants performed at a lower level than mixed human-AI teams, with the AI-only teams attaining the highest performance. Perceived team cognition was highest in human-only teams, with mixed composition teams reporting perceived team cognition 58% below the all-human teams. These results inform future mixed teams of the potential performance gains in utilizing mixed teams' over human-only teams in certain applications, while also highlighting mixed teams' adverse effects on perceived team cognition.

Advances in artificial intelligence are constantly increasing its validity as a team member enabling it to effectively work alongside humans and other artificial teammates. Unfortunately, the digital nature of artificial teammates and their restrictive communication and coordination requirements complicate the interaction patterns that exist. In light of this challenge, we create a theoretical framework that details the possible interactions in human-agent teams, emphasizing interactions through groupware, which is based on literature regarding groupware and human-agent teamwork. As artificial intelligence changes and advances, the interaction in human-agent teams will also advance, meaning interaction frameworks and groupware must adapt to these changes. We provide examples and a discussion of the frameworks ability to adapt based on advancements in relevant research areas like natural language processing and artificial general intelligence. The results are a framework that detail human-agent interaction throughout the coming years, which can be used to guide groupware development.

Technological advancement goes hand in hand with economic advancement, meaning applied industries like manufacturing, medicine, and retail are set to leverage new practices like human-autonomy teams. These human-autonomy teams call for deep integration between artificial intelligence and the human workers that make up a majority of the workforce. This paper identifies the core principles of the human-autonomy teaming literature relevant to the integration of human-autonomy teams in applied contexts and research due to this large scale implementation of human-autonomy teams. A framework is built and defined from these fundamental concepts, with specific examples of its use in applied contexts and the interactions between various components of the framework. This framework can be utilized by practitioners of human-autonomy teams, allowing them to make informed decisions regarding the integration and training of human-autonomy teams.