Collaborative problem-solving under time pressure is common but difficult, as teams must generate ideas quickly, coordinate actions, and track progress. Generative AI offers new opportunities to assist, but we know little about how proactive agents affect the dynamics of real-time, co-located teamwork. We studied two forms of proactive support in digital escape rooms: a facilitator agent that offered summaries and group structures, and a peer agent that proposed ideas and answered queries. In a within-subjects study with 24 participants, we compared group performance and processes across three conditions: no AI, peer, and facilitator. Results show that the peer agent occasionally enhanced problem-solving by offering timely hints and memory support; however, it also disrupted flow, increased workload, and created over-reliance. In comparison, the facilitator agent provided light scaffolding but had a limited impact on outcomes. We provide design considerations for proactive generative AI agents based on our findings.

As robots and autonomous agents integrate into society, understanding their influence on human social dynamics is crucial. We investigate human–robot interactions, focusing on the impact of prosocial behavior by robots on subsequent human interactions and humans’ willingness to exhibit prosocial behavior toward robots. Our study involved a token-collection game in a grid-world environment. Players, human or robot, could become trapped; a prosocial action involved another player freeing the trapped individual. Findings indicate that robots demonstrating prosocial behavior toward humans can inspire prosocial behavior toward others. Humans also show a notable propensity to assist robots. Witnessing robots engage in prosocial behavior may activate social norms related to cooperation, prompting humans to emulate these behaviors. Robots’ actions could improve the saliency of these acts, focusing people’s attention on prosocial behaviors they might not notice otherwise. Overall, the findings suggest that robots can promote prosocial behavior among humans, contributing to a more cooperative social environment. This research has implications for design and implementation of future autonomous systems, emphasizing the importance of social considerations in human-AI interaction studies.

For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the form of a Dynamic Bayesian Network (DBN) that infers the cognitive states of both AV users and other road users, integrating this information into the AV's decision-making process. Specifically, our model captures the ``well-being'' of both an AV user and an interacting road user as cognitive states alongside trust. Our DBN models infer beliefs over the AV user’s evolving well-being, trust, and intention states, as well as the possible well-being of other road users, based on observed interaction experiences. Using data collected from an interaction study, we refine the model parameters and empirically assess its performance. Finally, we extend our model into a causal inference model (CIM) framework for AV decision-making, enabling the AV to enhance user well-being and trust while balancing these factors with its own operational costs and the well-being of interacting road users. Our evaluation demonstrates the model’s effectiveness in accurately predicting user's states and guiding informed, human-centered AV decisions.

In hybrid mobility societies, where automated vehicles (AVs) and humans interact in public spaces, the significance of prosocial behaviors intensifies. These behaviors are crucial for the smooth functioning of an interdependent transportation environment, mitigating challenges from the integration of AVs and human-operated systems, and enhancing user well-being by fostering more efficient, less stressful, and inclusive environments. This study explores the impact of receiving prosocial behaviors on cognition, riding behavior, and well-being of micromobility users through interdependent traffic situations within a simulated urban environment. Our mixed design study involved two types of social interactions as between-subject conditions of prosocial and asocial interaction, and three categories of time constraint as within-subject conditions: relaxed, neutral, and pressed. The findings reveal that receiving prosocial and asocial behaviors can affect the state of well-being and trial performance in a mobility environment.

Micromobility devices, such as e-scooters and delivery robots, hold promise for eco-friendly and cost-effective alternatives for future urban transportation. However, their lack of societal acceptance remains a challenge. Therefore, we must consider ways to promote prosocial behavior in micromobility interactions. We investigate how post-ride feedback can encourage the prosocial behavior of e-scooter riders while interacting with sidewalk users, including pedestrians and delivery robots. Using a web-based platform, we measure the prosocial behavior of e-scooter riders. Results found that post-ride feedback can successfully promote prosocial behavior, and objective measures indicated better gap behavior, lower speeds at interaction, and longer stopping time around other sidewalk actors. The findings of this study demonstrate the efficacy of post-ride feedback and provide a step toward designing methodologies to improve the prosocial behavior of mobility users.

Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popular in development, research, and operation. When compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique challenges as they combine many maneuvers from both types of aircraft. Pilot workload is a critical factor for safe and efficient operation of VTOLs. In this work, we conduct a user study to collect multimodal data from 28 pilots while they perform a variety of VTOL flight tasks. We analyze and interpolate behavioral patterns related to their performance and perceived workload. Finally, we build machine learning models to estimate their workload from the collected data. Our results are promising, suggesting that quantitative and accurate VTOL pilot workload monitoring is viable. Such assistive tools would help the research field understand VTOL operations and serve as a stepping stone for the industry to ensure VTOL safe operations and further remote operations.