Artificial Intelligence (AI) is rapidly reshaping team collaboration in workplaces. Meanwhile, women only represent 22% of the global AI workforce, raising questions about whose perspectives drive AI design. This dual reality makes the stakes high: without critical attention, AI may entrench existing gendered dynamics; but with deliberate design, it may open new avenues for equity. Through in-depth interviews with 30 AI professionals (22 women), our work both confirms gendered challenges in male-dominated teams and offers a novel contribution: how those who directly experience these dynamics envision AI’s role in mitigating them. These practitioner-informed design visions reveal AI’s potential of offering multi-level support and empowering women to navigate these teams, and its risks of reinforcing stereotypes and surveillance. We call on the HCI community to explore this emerging design space for equitable human-AI teaming while critically attending to gendered power dynamics.

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.

Trust plays a critical role in both effective teamwork and successful integration of AI into human-AI teams (HATs), which are becoming increasingly interconnected to tackle large-scale problems. As these HATs grow in complexity, so do the challenges of building, maintaining, calibrating, and aligning trust across HATs, as trust is dynamic, socially influenced, and capable of spreading and evolving over time. To understand the underexplored question of how trust and distrust can spread across HATs, we conducted a laboratory experiment with two interconnected HATs (N=40) distributed across the U.S., each comprising two humans and one AI collaborating over a remotely piloted aircraft system (RPAS) conducting simulated reconnaissance tasks. Our findings show that distrust toward the AI teammate spread more easily than trust, both within and across HATs. While intra-HAT (dis)trust in the AI developed through direct interaction, inter-HAT (dis)trust was more vulnerable to word-of-mouth distrust. Additionally, participants’ trust in the (dis)trust spreader, as well as their team-level trust, reflected in-group favoritism and out-group bias. This work presents the first empirical investigation into trust and distrust contagion across HATs, expanding the current understanding of trust dynamics in multi-agent and multi-team systems. It identifies potential mechanisms driving trust and distrust transfer between interconnected HATs and explores the individual, team, and multi-team level impacts. In doing so, we lay the groundwork for developing theories on trust contagion in multi-agent, multi-team systems, providing valuable insights for future research and design strategies to optimize human-AI collaboration in complex environments.

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.

Artificial Intelligence (AI)-mediated collaboration has been a core research interest in CSCW and HCI in recent decades. AI is being embedded in teams as a teammate, known as human-AI teamwork. A critical aspect to this and any type of teamwork is monitoring: the ability to track and be aware of both team- and individual-level actions. As AI continuously evolves from a passive tool into a supportive collaborator capable of playing a role in teams, more research is needed to investigate human perceptions of AI teammates' monitoring. More work is needed on when and how AI can function as a supportive collaborator rather than a surveillant (in regard to monitoring) to enhance team coordination and performance. Drawing on 22 interviews, we examined how humans perceive AI monitoring when AI behaves as a collaborative teammate for team support, focusing on perceptions of AI monitoring at both team and individual levels. Four tensions have been identified, namely: performance versus privacy, support versus control, fairness versus empathy, and team-level and individual-level trade-offs. We also propose feasible design principles for human-AI teaming designers to optimize AI monitoring for effective team support, maximizing its unique advantages while mitigating potential risks, further cultivating more effective and trustworthy human-AI collaboration.

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.

With rapid advancements in large language models (LLMs), generative AI (GenAI) is transforming people's life and work across various domains. Unlike previous AI technologies that are often feminized, most of these GenAI tools are non-gendered, potentially preventing users from applying gender stereotypes. However, GenAI's use of natural language can evoke social perceptions including gender attribution, making it susceptible to gender associations. Using two online experiments, we explored how GenAI's removal of gender could mitigate individuals' gender stereotypes toward it, and how certain linguistic cues could trigger gender stereotypes even if it is non-gendered. We found that the removal of AI's gender did mitigate gender stereotypes toward it, but only to an extent. Additionally, gendered linguistic cues such as politeness, apologies, and tentative language (or lack thereof) could trigger gender stereotypes toward non-gendered AI. We contribute to HCI and CSCW research by providing a timely investigation into individuals' gender stereotypes toward GenAI agents, and one of the first to examine the profound impact of language style on users' perceptions and attributions of social characteristics to GenAI. Our findings shed light on the purposeful and responsible design of GenAI that prioritizes promoting gender equality, thereby ensuring that technological advancements align with evolving social values.

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.

With the emergence of new AI technologies, research on the potential for AI to function as teammates alongside humans has expanded. The recent introduction of highly capable large language models (LLMs) is particularly noteworthy, showing strong potential in human–AI teaming where communication is crucial. However, this novel technology has yet to be validated in human–AI teaming or as a teammate, hindering its application in research and practice. This article presents an empirical online experiment (N = 778) where participants engaged in a real-time and interdependent interaction with a commercially available LLM, with the presentation of the LLM manipulated to be either a tool or a teammate. Results show that when compared to presenting an LLM as a teammate rather than a tool significantly increases trust and significantly impacts the sentiment humans have when talking with their AI, with LLM teammates seeing more positive sentiment. Perceptions of trust, acceptance, and performance were generally high for LLMs presented as teammates. Despite these impacts, participants’ prior experiences with AI technology were still shown to predict the perceptions they formed with their AI teammate. Based on these findings, this article presents an important empirical result, which is that presenting highly capable AI, such as LLMs, as teammates can improve perception and interaction compared to presenting an AI as a tool. In turn, a discussion is had on how future research can continue to identify when and how to introduce LLMs and other AI technologies as teammates.

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.

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.

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.

The success of human-AI teams (HATs) requires humans to work with AI teammates in trustful ways over a certain time period. However, how trust evolves and changes dynamically in response to human-AI team interactions is generally understudied. This work explores the evolvement of trust in HATs over time by analyzing 45 participants' experiences of trust or distrust in an AI teammate prior to, during, and after collaborating with AI in a three-member HAT. Our findings highlight that humans' expectations of AI's ability, integrity, benevolence, and adaptability influence their initial trust in AI before collaboration. However, this initial trust can be maintained or revised through the development of situational trust during collaboration in response to the AI teammate's communication behaviors. Further, the trust developed through collaboration can impact individuals' subsequent expectations of AI's ability and their collaborations with AI. Our findings also reveal the similarities and differences in the temporal dimensions of trust for AI and human teammates. We contribute to CSCW community by offering one of the first empirical investigations into the dynamic and temporal dimension of trust evolvement in HATs. Our work yields insights into the pathways to expanding the methodological toolkit for investigating the development of trust in HATs, formulating theories of trust for the HAT context. These insights further inform the effective design of AI teammates and provide guidance on the timing, content, and methods for calibrating trust in future human-AI collaboration contexts.

People often apply gender stereotypes toward computerized agents. Rather than challenging these stereotypes, modern AI technologies controversially use them in creating AI agents that underline stereotypical gendered roles. This approach thus further reinforces the often male-dominated societal gender norms and disenfranchises women and gender non-binary individuals. While this issue has raised concerns in the HCI and CSCW communities, still little is known regarding how to mitigate the negative impacts of embedding gender stereotypes in AI agents. In this paper, we propose an eXplainable AI (XAI) approach to mitigating individuals' gender stereotypes toward AI agents. We conducted an online video vignette experiment with 350 participants randomly assigned to one of the eighteen conditions of a 3 (gender of the agent: woman, man, gender-neutral) x 3 (task gender: feminine, masculine, neutral) x 2 (presence or absence of AI explanation) between-subjects design. Our findings indeed suggest that XAI helped participants avoid applying gender stereotypes toward gendered AI agents, by increasing their understanding of how the agent came to its decision and decreasing their rating of the agent's humanlikeness. We contribute to CSCW research by providing a timely investigation into individuals' gender stereotypes toward the state-of-the-art AI agents and by advancing the empirical understanding of the cognitive processes and mechanisms underlying these gender stereotypes. We also demonstrate how eXplainable AI can effectively suppress the application of social characteristics (i.e., gender stereotypes) toward AI agents by disrupting the said cognitive processes. Insights from this study can inform how future AI technologies should be designed to create a progressive gender reality that will gradually reshape humans' experience and ingrained gender ideologies.

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.

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other's working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality- derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.

As AI becomes more advanced in natural language processing, the research on AI's communication in teaming environments is getting more attention in CSCW/HCI. Even though AI's communication serves as an essential part of facilitating team coordination and shaping team outcomes, the impact of AI's communication modality on human-AI teamwork is still understudied. Using a mixed-design experiment and follow-up group interviews with 100 participants, we investigate the impact of AI's communication modality, one of AI's most essential communication characteristics, on team coordination and team outcomes in two different human-AI team compositions, human-human-AI teams, and human-AI-AI teams. Our findings highlight the trade-offs between AI's verbal communication and visual communication, which inspire two design recommendations on how to apply AI's verbal and visual communication to support human-AI coordination effectively. Our study generates an initial understanding of the role that AI's communication will have in ensuring team effectiveness in human-AI teams in the CSCW/HCI field.

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.

Non-profit driven indie game development represents a growing open and participatory game production model as an alternative to the traditional mainstream gaming industry. However, this community is also facing and coping with tensions and dilemmas brought by its focus on artistic and cultural values over economic benefits. Using 28 interviews with indie game developers with a non-profit agenda across various cultures, we investigate the challenges non-profit driven indie game developers face, which mainly emerge in their personal or collaborative labor and their endeavors to secure sustainable resources and produce quality products. Our investigation extends the current HCI knowledge of the democratization of technology and its impact on the trajectory of innovating, designing, and producing future (gaming) technologies. These insights may help increase the opportunities for and retention of previously underrepresented groups in technology production and inform effective decision/policy making to better support the creativity industry in the future.

Extensive HCI research has investigated how to prevent and mitigate harassment in virtual spaces, particularly by leveraging human-based and Artificial Intelligence (AI)-based moderation. However, social Virtual Reality (VR) constitutes a novel social space that faces both intensified harassment challenges and a lack of consensus on how moderation should be approached to address such harassment. Drawing on 39 interviews with social VR users with diverse backgrounds, we investigate the perceived opportunities and limitations for leveraging AI-based moderation to address emergent harassment in social VR, and how future AI moderators can be designed to enhance such opportunities and address limitations. We provide the first empirical investigation into re-envisioning AI’s new roles in innovating content moderation approaches to better combat harassment in social VR. We also highlight important principles for designing future AI-based moderation incorporating user-human-AI collaboration to achieve safer and more nuanced online spaces.

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.

As universities transitioned in-person classrooms to virtual classrooms, instructors faced challenges and changes in how they conduct their classes and teaching style trying to keep virtual classrooms as similar to in-person as much as possible through the use of group work in breakout sessions. Within these breakout sessions, students are expected to work together to complete an assignment. Through 669 surveys and 19 interviews, our paper outlines the successes and challenges of breakout sessions and teamwork in virtual learning environments faced by professors, graduate teaching assistants, and students. Our findings show the importance of pedagogical research for online environments, the student need for persistent instructions and check-ins to facilitate teamwork in breakout sessions, and strong justification for the use of breakout sessions in online courses. Based on our findings, we propose design recommendations to address the challenges highlighted within the classroom and software used by instructors and students. Our work contributes and extends previous CSCW and HCI research in online distributed teamwork and education.

A growing organizational trend is to utilize ad-hoc team formation which allows for teams to intentionally form based on the member skills required to accomplish a specific task. Due to the unfamiliar nature of these teams, teammates are often limited by their understanding of one another (e.g., teammate preferences, tendencies, attitudes) which limits the team's functioning and efficiency. This study conceptualizes and investigates the use of a teammate information-sharing recommender system which selectively shares interpersonal recommendations between unfamiliar teammates (e.g., "Your voice may be overshadowed by this teammate when making decisions...") to promote teammate understanding. Through a mixed-methods approach involving 105 participants working on actual unfamiliar teams, this study explores how presentation elements such as anonymity and explanations influence system perceptions and how anonymity influences team outcomes. Results indicate that anonymizing recommendations was associated with worse team measures, particularly team satisfaction and team cohesion. Qualitative results shed light on why team members perceived privacy concerns and team benefits associated with using the system. We contribute to CSCW through a better understanding of how to support unfamiliar teams, the conceptualization and empirical investigation of a novel teammate information-sharing recommender system, and foundational design recommendations associated with such a system.

Recently, AI is integrating into teams to collaborate with humans as a teammate with the goal of achieving unprecedented team outcomes. Much of the coordination between humans and AI teammates relies on human-AI communication, which is challenging due to AI's limitations on natural language communication. Thus, it is essential to identify and develop effective communication strategies for AI teammates in human-AI teams to facilitate the coordination process. Through interviews with 60 participants who collaborated with an AI teammate in a multiplayer online game, in this paper, we explore communication strategies that humans expect AI teammates to apply to support human-AI coordination and collaboration in dyadic teaming environments, and how the AI teammate's communication can impact teaming processes. Our findings highlight four communication strategies AI teammates should apply to support their coordination with humans in dyadic teaming environments. We also find that AI teammates' proactive communication with humans could facilitate the development of human trust and situation awareness, whereas AI lacking such proactive communication is often not perceived as a teammate. Our study extends the current CSCW/HCI research on human-AI communication in teaming environments by shedding light on how communication should be structured in dyadic human-AI teams for effective and smooth collaboration.

Computer-mediated collaboration has long been a core research interest in CSCW and HCI. As online social spaces continue to evolve towards more immersive and higher fidelity experiences, more research is still needed to investigate how emerging novel technology may foster and support new and more nuanced forms and experiences of collaboration in virtual environments. Using 30 interviews, this paper focuses on what people may collaborate on and how they collaborate in social Virtual Reality (VR). We broaden current studies on computer-mediated collaboration by highlighting the importance of embodiment for co-presence and communication, replicating offline collaborative activities, and supporting the seamless interplay of work, play, and mundane experiences in everyday lives for experiencing and conceptualizing collaboration in emerging virtual environments. We also propose potential design implications that could further support everyday collaborative activities in social VR

This paper explores the role of live streaming in distributed collaborative software development using indie game development, an end-user driven creative community, as an example. We conducted 27 in-depth interviews with indie game developers from various cultures and countries, who had engaged in live streaming for collaborative software development either as a streamer or a viewer. Our findings show how live streaming can be used by indie game developers to support their endeavors to innovate the traditional game development model, which goes beyond just learning and teaching technical skills. We also highlight the potential challenges indie developers face in this process. We thus make unique contributions to CSCW by bridging the previously often disconnected research agendas on collaborative software development and live streaming. We also provide potential directions for designing future live streaming platforms to better support distributed collaboration in emerging end-user driven creative activities.

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.

Game jams are intense and time-sensitive online or face-to-face game creation events where a digital game is developed in a relatively short time frame (typically 48 to 72 hours) exploring given design constraints and end results are shared publicly. They have increasingly become emerging sites where non-professional game developers, amateurs, and hobbyists engage in bottom-up technological innovation by collaboratively designing and developing more creative and novel digital products. Drawing on 28 interviews, in this paper we focus on how game developers collaborate as small teams to innovate game design and development from the bottom up in virtual game jams (i.e., exclusively online) and the unique role of virtual game jams in their technological innovation. We contribute to CSCW by providing new empirical evidence of how team practices for innovation may emerge in a novel technology community that is not widely studied before. We also expand a growing research agenda in CSCW on explicating nuanced social behaviors, processes, and consequences of bottom-up technological innovation.

The role of digital gaming on parenthood and parent-child relationships is a common research interest in HCI and CHI PLAY. Yet, how technology co-use, such as co-playing digital games, affords and impacts parent-child relationships is still understudied. Using 20 in-depth interviews of adults who had co-played modern digital games with their parents and/or children, in this paper we investigate parent-child relationships mediated by co-playing modern digital games. We update prior HCI and CHI PLAY research on game-mediated parent-child relationships by suggesting a "democratized" family life and a fading digital divide for families with favorable digital game co-play experiences. We also contribute to HCI and CHI PLAY by providing new perspectives of technology co-use in the context of gaming, such as an important relational tool that parents can use to promote conversations with their child(ren). These insights can further inform the design of future play to better support parent-child interactions during digital game co-play.

A large body of research has underscored the importance of the cognitive process of team cognition and its relation to team performance. However, little research has focused on applying such an important teamwork process to computer-mediated collaboration within a fast-paced virtual environment. In this paper, we use esports as a research platform to address this limitation due to its fast-paced nature and its heavy reliance on teamwork. We report the experience and perceptions of 20 players with regard to their descriptions of team cognition within esports. We found that esports players relied on their game experience and understanding of role interdependencies in order to develop team cognition with strangers. We also found that experienced teams utilized a mutual understanding of teammate skills and personalities in order to predict responses and limit the verbal communication required to make quick team decisions. We contribute to CSCW by extending the cognitive understanding of computer-mediated collaboration and by advancing research on team cognition and how it can occur within a fast-paced virtual environment.

This article considers human-autonomy teams (HATs) in which two human team members interact and collaborate with an autonomous teammate to achieve a common task while dealing with unexpected technological failures that were imposed either in automation or autonomy. A Wizard of Oz methodology is used to simulate the autonomous teammate. One of the critical aspects of HAT performance is the trust that develops over time as team members interact with each other in a dynamic task environment. For this reason, it is important to examine the dynamic nature of teammate trust through real-time measures of team interactions. This article examines team interaction and trust to understand better how they change under automation and autonomy failures. Thus, we address two research questions: 1) How does trust in HATs evolve over time?; and 2) How is the relationship between team interaction and trust impacted by the failures? We hypothesize that trust in HATs will decrease as autonomy failures increase. We also hypothesize that team interaction would be related to the development of trust and recovery from the failures. The results implicate three general trends: 1) team interaction dynamics are linked to the development of trust in HATs; 2) trust in the autonomous teammate is only associated with recovery from autonomy failures; 3) team interaction dynamics are related to both automation and autonomy failure recovery.

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.

Much HCI research seeks to contribute to technological agendas that lead to more just and participative labor relations and practices, yet that research also raises concerns about forms of exploitation associated with them. In this paper, we explore how U.S. independent [indie] game developers' socio-technological practices inject forms of labor, capital, and production into the game development industry. Our findings highlight that indie game development 1) seeks to promote an alternative to business models of game development that depend on free and immaterial labor; 2) builds offline networks at different scales to develop collectives that can sustain their production; and 3) emphasizes how distributed collaboration, co-creation, and the use of free tools and middleware make game production more widely accessible. The research contributes to HCI research that seeks to explicate and mitigate emerging forms of exploitation enabled by new technologies and processes. Our critical review of indie developers' practices and strategies also extends the current conceptualization of labor and technology in CSCW.

The phenomenon of end-user driven technological practices such as DIY making, hacking, crafting, and open design/manufacturing is shaping debates in HCI and CSCW about participatory innovation dynamics. However, prior research also reveals two limitations, namely, unequal participation in decision-making and the neglect of middle-tier "pro-amateur" end users. In this paper, we use independent [indie] game development as a case to explore the above-mentioned two key issues. Specifically, we highlight the importance of small teams, "crafting," and "democracy" in supporting and facilitating middle-tier end-users' engagement with technology. Our focus on indie game developers, an understudied group of middle-tier end users in HCI and CSCW, offers new empirical evidence of the dynamic process through which pro-amateurs can participate in technological innovation. Understanding their practices and the socio-technological challenges that they face, therefore, informs the design of more participatory technologies that both allow hobbyists' and experts' innovation and support the technological practices performed by users who are at the middle-tier. This not only promotes the democratization of technology and bottom-up innovation but also adds nuance to existing literature on end-user driven technological practices.

Driven by state-of-the-art AI technologies, human-AI collaboration has become an important area in computer-supported teamwork research. While human-AI collaboration has been investigated in various domains, more research is needed to explore human perceptions and expectations of AI teammates in human-AI teaming. To achieve an in-depth understanding of how people perceive AI teammates and what they expect from AI teammates in human-AI teaming, we conducted a survey with 213 participants and a follow-up interview with 20 participants. Considering the context-dependency of teamwork, we chose to study human-AI teaming in the context of multiplayer online games as a case study. This study shows that people have mixed feelings toward AI teammates but hold a positive attitude toward future collaboration with AI teammates in general. Our findings highlight people's expectations for AI teammates in a rapidly changing collaborative environment (e.g., instrumental skills for in-game tasks, shared understanding between humans and AI, communication capabilities, human-like behaviors and performance), as well as factors that impact people's willingness to team up with AI teammates (e.g., pre-existing attitudes toward AI, previous collaboration experience with humans). We contribute to CSCW by shedding light on how AI should be structured in human-AI teaming to support highly complex collaborative activities in CSCW environments.

In the past, team coordination dynamics have been explored using nonlinear dynamical systems (NDS) methods, but the relationship between team coordination dynamics and team performance for all-human teams was assumed to be linear. The current study examines team coordination dynamics with an extended version of the NDS methods and assumes that its relationship with team performance for human-autonomy teams (HAT) is nonlinear. In this study, three team conditions are compared with the goals of better understanding how team coordination dynamics differ between all-human teams and HAT and how these dynamics relate to team performance and team situation awareness. Each condition was determined based on manipulation of the .pilot role: in the first condition (synthetic) the pilot role was played by a synthetic agent, in the second condition (control) it was a randomly assigned participant, and in the third condition (experimenter) it was an expert who used a role specific coordination script. NDS indices revealed that synthetic teams were rigid, followed by experimenter teams, who were metastable, and control teams, who were unstable. Experimenter teams demonstrated better team effectiveness (i.e., better team performance and team situation awareness) than control and synthetic teams. Team coordination stability is related to team performance and team situation awareness in anonlinear manner with optimal performance and situation awareness associated with metastability coupled with flexibility. This result means that future development of synthetic teams should address these coordination dynamics, specifically, rigidity in coordination.

Collaborative information seeking (CIS) is of growing importance in the information sciences and human–computer interaction (HCI) research communities. Current research has primarily focused on examining the social and interactional aspects of CIS in organizational or other settings and developing technical approaches to support CIS activities. As we continue to develop a better understanding of the interactional aspects of CIS, we need also start to examine the cognitive aspects of CIS. In particular, we need to understand CIS from a team cognition perspective. To examine how team cognition develops during CIS, we conducted a study using observations and interviews of student teams engaged in colocated CIS tasks in a laboratory setting. We found that a variety of awareness mechanisms play a key role in the development of team cognition during CIS. Specifically, we identify that search, information, and social methods of awareness are critical to developing team cognition during CIS. We discuss why awareness is important for team cognition, how team cognition comprises both individual and team‐level cognitive activities, and the importance of examining both interaction and cognition to truly understand team cognition.

Electronic medical record (EMR) systems are used by a wide variety of users. However, current research on the design and use of the EMR primarily focuses on clinical users such as physicians and nurses. While it is important to understand EMR use by clinicians, there is also a need to understand how non-clinicians use these systems because of the important role they play in the patient-care process. In this note, we present results of an ethnographic field study on the use and perceptions of EMR systems by non-clinicians in an emergency department. We then discuss design implications that can improve the system usability and strengthen the empowerment of these non-clinicians.