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.

This paper describes S.P.O.T., a game-based application for promoting children's practical understanding of ML concepts and applications. Current tools for teaching ML in K-12 engage students in playful exploration of ML mechanisms and teach ML from a cognitive perspective. However, in S.P.O.T, learners interact with ML within real-life sociopolitical contexts and examine how ML predictions impact their daily lives and communities. Through the immersion of stories that mirror children's lived experiences, S.P.O.T. provides elementary school aged children with opportunities to learn how machine learning applications function and develop children's abilities to critically examine, question, and reimagine the consequences of ML decisions in the real world.

As social Virtual Reality (VR) grows in prevalence, new possibilities for embodied and immersive social interaction emerge, including varied forms of interpersonal harm. Yet, challenges remain regarding defining, identifying, and mitigating said harm in social VR. In this paper, we take an alternative approach to understanding and designing solutions for interpersonal harm in social VR through the lens of consent, which circumvents the lack of consensus and social norms on what should be defined as harm in social VR and reflects the embodied, immersive, and offline-world-like nature of harm in social VR. Through interviews with 39 social VR users, we offer one of the first empirical explorations on how social VR users understand consent as "boundaries," (re)purpose existing social VR features for practicing consent as "boundary setting," and envision the design of future consent mechanics in social VR to balance protection and interaction expectations to mitigate interpersonal harm as "boundary violations" in social VR. This work makes significant contributions to CSCW and HCI research by (1) uncovering how social VR users craft novel conceptualizations of consent as boundaries and harm as unwanted boundary violations, and (2) providing three foundational principles for designing future consent mechanics in social VR informed by actual social VR users.