Artificially Intelligent (AI) agent systems such as robots are increasingly integrated into the workplace and gaining more power in collaborating with humans. Yet studies on robot power and compliance report mixed findings. To address these inconsistencies, we introduced legitimacy as people’s psychological acceptance of power. Three preregistered experiments were conducted (N = 431). In Experiment 1 and 2, we manipulated power assignment (robot power vs. human power), and legitimacy of power (legitimate, illegitimate, no explanation) through competence and procedural fairness. The results showed that participants complied more to the legitimate robot power than illegitimate one. In Experiment 3, we examined whether perceptions of legitimacy would emerge naturally in more ecologically valid collaboration. Results of multigroup mediation model showed that the robot leader was perceived as less legitimate than the human leader, which accounted for the reduced compliance to the robot’s decisions. In all three experiments, people’s perceived social attributes of robots with power and their affective responses after the interaction were negatively affected. This study underscores the importance of legitimacy in understanding power and compliance in human-robot collaboration.

While resuscitation training is critical, healthcare workers (HCWs) with high workload have limited chance to get trained and re-trained due to time and resource constraints. To address this gap, we engaged in a co-design process of robots that facilitate and prepare HCWs for resuscitation procedures (i.e., codes). First, we investigated what resuscitation training consists of, including challenges faced by trainees and trainers. Second, we collaboratively explored how a crash cart robot, that guides users to medical supplies and equipment, could assist trainers and trainees synchronously–during team-based clinical simulations and asynchronously–during one-on-one training. We found that robots could 1) serve as a learning assistant by providing real-time feedback and supporting personalized training needs; and 2) an evaluating assistant by monitoring multiple trainees and tracking critical timing of interventions in the training. Through this new training paradigm, we hope to demonstrate opportunities for crash cart robots to aid HCWs for their sustainable training and reskilling. We discuss the role of robots in training beyond cognitive knowledge, situating them within two underexplored contexts: practical skill training and team-based training.

As families increasingly live apart due to reasons such as education, employment, or personal independence, they rely on computer-mediated communication (CMC) tools to stay connected. While tools like audio and video calls help bridge the distance, they often fall short in fostering social presence. This study explores how telepresence robots, with their physicality and mobility, may enhance social presence in remote family communication. We hypothesize that telepresence robots will increase perceived social presence compared to current CMC tools. To test this, we conducted a within-subjects study with eight families, deploying telepresence robots in their homes for two weeks. The results show that among the five factors encompassed by social presence—perceived presence, psychological closeness, seamless communication, and conversational involvement—four were significantly increased after using telepresence robots, while communication privacy showed no effect.

Healthcare workers (HCWs) encounter challenges in hospitals, such as retrieving medical supplies quickly from crash carts, which could potentially result in medical errors and delays in patient care. Robotic crash carts (RCCs) have shown promise in assisting healthcare teams during medical tasks through guided object searches and task reminders. Limited exploration has been done to determine what communication modalities are most effective and least disruptive to patient care in real-world settings. To address this gap, we conducted a between-subjects experiment comparing the RCC’s verbal and non-verbal communication of object search with a standard crash cart in resuscitation scenarios to understand the impact of robot communication on workload and attitudes toward using robots in the workplace. Our findings indicate that verbal communication significantly reduced mental demand and effort compared to visual cues and with a traditional crash cart. Although, frustration levels were slightly higher during collaborations with the robot compared to a traditional cart. These research insights provide valuable implications for human-robot teamwork in high-stakes environments.

The rising demand for AI-powered smart home solutions has produced a recent surge in the adoption of smart home devices (SHDs). SHDs are uniquely situated within private, personal environments, and understanding the impact of device design on users’ parasocial relationship, privacy perceptions, and overall adoption is crucial; however, the literature lacks a satisfactory exploration of this essential facet of integrating intelligent devices into everyday living. This study addresses this deficit by analysing the nuanced interplay between device design features and user adoption intentions. An online experiment was conducted using a 2 (communication style: task-oriented vs. social-oriented) × 3 (embodiment type: application voice vs. virtual animation vs. physical robot) between-subjects design (N = 297). The findings indicate that SHDs employing a social-oriented communication style, while promoting stronger parasocial interactions, are simultaneously correlated with increased perceptions of privacy risk when compared to those utilising a task-oriented communication style. The more embodied the SHDs, the stronger the perceived parasocial interaction. Furthermore, higher perceived privacy risks negatively affect purchase intention. The findings provide novel insights into the design of SHDs that not only address privacy concerns but also create positive user experiences in IoT-based smart homes, thereby fostering long-term adoption.

Healthcare workers (HCWs) face many challenges during bedside care that impede team collaboration and often lead to poor patient outcomes. Robots have the potential to support medical decision-making, help identify medical errors, and deliver supplies to clinical teams in a timely manner. However, there is a lack of knowledge about using robots to support clinical team dynamics despite being used in surgery, healthcare operations, and other applications. To address this gap, we engaged in a co-design process of robots that support clinical teamwork. We collaboratively explore how robots can support clinical teamwork with HCWs. This collaborative process includes understanding the challenges they face during bedside care and envisioning robots that can help mitigate these issues. Our study shows that robots can act as a shared mental model for clinical teams, help close communication gaps, and provide procedural steps to assist HCWs with limited in-hospital experience. This research highlights new ways HRI researchers can deploy robots in acute care settings, as well as define appropriate levels of autonomy to maintain human control in safety-critical settings.