The integration of social robots into educational contexts is steadily increasing, as these agents are used to foster engagement, support personalized learning, and create interactive experiences. In these contexts, empathy towards a social robot is a crucial mechanism to build trust, motivation, and social connections. Narrative framing has been shown to support the elicitation of empathy. However, little is known about how children’s empathy towards robots is affected by different narrative framing and how individual factors shape this relationship. Across two preregistered experiments, we investigated 7–15-year-olds’ empathy towards a social robot in focus groups (Experiment 1; n = 19) and the effect of narrative framing (sad vs. neutral), baseline empathy, age, and gender thereon in an experimental study (Experiment 2; n = 73). Experiment 1 showed that robot perception, personal experiences, and social norms affected children’s empathy towards a robot. Experiment 2 showed no significant effects of narrative framing, age, or baseline empathy on children’s empathy towards the robot. However, varying the narrative framing of the robot resulted in gender differences in elicited empathy, with girls showing higher empathy than boys in the neutral narrative condition. Our findings indicate that contextual and relational cues might exert a stronger influence on children’s empathic responses towards robots than developmental factors or dispositional traits specific to individual children. The two experiments that compose this study offer valuable insights into how empathy might be elicited through social robots. These insights hold promise for informing how best to design robotic agents that children can connect with in meaningful and effective ways in education and learning contexts.
We present a detailed and unusual case study of ensuring trustworthiness in social robots in practice—the artwork Cat Royale in which a robot played with a family of three cats. We reveal how delivering Cat Royale involved tackling diverse aspects of trustworthiness beyond the immediate interaction between cats and robot, from the control room, to animal welfare expertise, extensive ethical review, and carefully planned public engagement. We contribute a framework for describing ecosystems of trust comprising five layers: personal workspace, orchestration, wider workspace, organisational culture, and the public sphere.
Robotic systems are increasingly integrated into healthcare settings, providing physical assistance, social interaction, remote diagnostics, and data-intensive services. Although these systems collect sensitive multimodal data, such as audio, video, physiological signals, and contextual metadata, existing security frameworks often fail to adequately address the combined challenges of privacy, cybersecurity, and human-robot interaction (HRI). To understand further, we conducted a systematic review of 62 peer-reviewed studies from an initial pool of 393 articles. Using our proposed PRoSec-HRI (Privacy and Robotic Security in Healthcare Robotics Interaction) framework, we identified leading technical strategies, including differential privacy for sensor data anonymization, federated learning for decentralized model training, blockchain-based authentication for auditable processes, and formal verification for privacy-compliant behavior. Despite majority of studies discuss privacy-preserving ( 68% ) and cybersecurity ( 76% ) techniques, we found only 10% conducted in-situ evaluations, and just 13% considered firmware or lifecycle security. Moreover, only 14% demonstrated regulatory compliance (e.g., GDPR, HIPAA), and a mere 9% addressed real-time threat mitigation. While 61% papers featured trust-building mechanisms, such as symbolic gestures or consent dashboards, only 11% of those accounted for cultural sensitivity or user education. Our findings expose significant gaps and provide a foundation for developing privacy-aware, secure, and user-centered healthcare robots.
This paper investigates how terrain complexity and user interaction modes (teleoperation versus passive observation) influence human cognitive load and trust during interactions with a reinforcement learning−trained legged robot navigating complex terrains. We quantitatively assess cognitive load and trust dynamics using multimodal physiological measurements, including GSR, facial skin temperature, and blink rate combined with self-reported NASA-TLX scores. Participants either actively teleoperated or passively observed the robot traversing flat surfaces, irregular terrains, and stairs within a high-fidelity IsaacGym simulation. Our findings highlight distinct physiological and subjective responses linked to both terrain difficulty and interaction role. Specifically, teleoperators experienced higher cognitive load but stable trust levels, whereas observers showed heightened stress responses and reduced trust during challenging terrain navigation. By combining reinforcement learning robot locomotion with multimodal physiological sensing, this research advances methods for real-time assessment of trust and workload in human-robot interaction, offering insights for designing more adaptive and user-centered robotic systems.
The Syrian refugee crisis is among the largest globally. We are developing a social robot tailored to the needs of displaced Syrians hosted in Scotland. As part of a mixed-methods study to understand the needs of this population and the possible use cases of the robot, we conducted two focus groups with Syrian refugees and asylum seekers residing in Glasgow. Using thematic analysis, we identified these participants’ unmet needs and existing gaps in access to services. Participants observed an Arabic-speaking social robot, and together we explored its potential as a solution to help navigate bureaucratic processes and access services. The participants expressed curiosity and enthusiasm about the robot. As they shared experiences of homelessness and displacement, they also highlighted bureaucracy and the English language as key barriers to accessing services. This study identifies key design requirements for developing a multilingual support robot for refugees and asylum seekers.
