This study explores the potential of social robots to contribute to reading motivation among children by examining usability issues and situational motivation. A prototype, Robot-Nils, was developed using the Furhat robot and a GPT-4o-based system to manage the dialogue. Fifty-four schoolchildren aged 11 to 12 interacted with the prototype, which was costumed and prompted to act as the main character from a book they had read. Participants interacted with the robot either individually or in groups. This was followed by interviews and a situational motivation questionnaire. Thematic analysis of video recordings and interviews identified four themes of usability issues: response time, politeness, information barriers, and insecurity. There were no statistically significant differences in motivation between individual and group conditions. Motivation was overall rated as moderate. The main design recommendation based on this study is to provide real-time feedback on speech recognition and response generation.

This paper, part of a co-design project, investigates qualitatively how primary students engage with fiction and a social robot, focusing on contributing to design principles for a conversational agent intended to facilitate book discussions. Conducted with 81 participants aged 10–11 from two primary schools, the study is based on field notes, video observations, and discussions. Findings reveal that students engage in meaningful conversations with the robot about the story and its characters, demonstrating critical and creative engagement while showing varying levels of task commitment at the intersection of fantasy and reality. These results suggest that designing the conversational robot as a character from the book, contributes to bridging fiction and technology into a richer, more consistent book conversation and to children’s overall reading experience. This understanding is an important contribution to social robot design aimed at improving educational reading practices.

Significant amounts of HRI research effort are spent on the design/evaluation of robot bodies. Critical discussion and debates concerning the way bodies are treated and considered within HRI have tended to focus on the implications of designing/interacting with highly anthropomorphic robots, yet drawing attention away from the human body in HRI, particularly in the context of everyday interactions with “unexpectedly” social robots, i.e. those non-humanoid robots that human users interact with in a somewhat “social” way. These kind of robots crystallise particular aspects of (and tensions between) the different ways in which bodies are understood across disciplines. At the same time, a fundamental, and largely neglected tool for HRI research is the researchers' own embodied experience of the intersubjective role. This workshop has invited speakers and audience to engage in cross-disciplinary conversations on embodiment in HRI, highlighting the different ways in which “the body” is or can be variously conceptualised and researched.

The motivation to read among children in Nordic countries has seen a noticeable decline in recent years. This study explores the design of social robots for stimulating interest in reading among fourth-grade students (age 10–11). We used a combination of conceptual design methods to engage teachers and students from four classes in two Swedish schools in co-creation workshops. Ideas on functions, qualities, and robot designs were generated, and based on this a set of ten distinct design concepts were created: the Facilitator, the Librarian, the Coach, the Buddy, the Assistant, the Narrator, the Creator, the Apprentice, the Portable, and the Gamer. The strengths and weaknesses of different designs were evaluated, resulting in a final design named ‘The BookBot,’ aiming to inspire and engage students in book reading through discussions of book content, character portrayal, and personalised book recommendations.

This paper presents a novel framework for modelling attention and cognitive control in human-swarm interaction (HSI) and Human-Autonomy Teaming (HAT) more broadly. Using a maritime search and rescue (SAR) scenario as a case study, we integrate the Joint Control Framework (JCF) with the phenomenological Sphere of Attention (SOA) model to analyse how swarm operators' attention might have to shift across multiple levels of abstraction and system components in a future-oriented design scenario. Our analysis highlights the operators' trajectories of attention as they move between high-level mission goals and specific low-level system control actions. The resulting model suggests that the required attention shifts would be primarily serial, allowing for smooth transitions between tasks, while radical shifts may indicate breakdowns in information presentation or system support. We discuss the implications of our framework for designing future HSI systems and user interfaces, emphasising the need for systems that align with operators' attentional demands, thus supporting more effective and intuitive interactions in complex, dynamic environments. Future work will need to include further evaluation of the framework's broader applicability and relationship to cognitive constructs in Ergonomics.

Service robots, like delivery or telepresence robots, often act as proxies for people, though this representation is not always obvious to observers. For instance, when a delivery robot cuts in line at a grocery store, the identity of the person it represents matters. The lower social status often assigned to service robots (“it’s just a robot, it can wait”) can become problematic in human-robot conflicts, if the represented individual (e.g., senior citizen) is treated unfairly. Previous research indicates that people tend to overlook the human behind the robot, potentially disadvantaging the service recipient. This study aimed to determine whether people are more likely to grant priority to a delivery robot when informed about the person it represents. In a between-subjects virtual reality (VR) experiment the recipient was either made apparent with an avatar or not and participants had to choose if they would give priority to a delivery robot in a grocery store queue. Results showed that while the presence of the avatar increased participants’ self-reported consideration of the robot’s human recipient, it did not lead to higher compliance, as the willingness to grant priority was unexpectedly high in both conditions. The results suggest that social desirability and the novelty of human-robot interaction (HRI) may explain these outcomes and future research is discussed.

Prototyping methods are commonly employed iteratively throughout the design and product development, typically ranging from early low-fidelity to later high-fidelity prototypes. We present a case study focusing on prototyping a receptionist agent migrating between three platforms (a monitor on the wall, a mobile phone, and a physical robot). More specifically, we compare virtual reality (VR) and physical (real world) prototyping methods. The two methods are compared in terms of fidelity and usability. The breadth of features, the degree of functionality, and the interactivity were similar. However, the aesthetic refinement differed. The VR prototyping method also had much higher prerequisites in terms of equipment and skills, and the learning curve for the designer was steep. Both methods were equally efficient in user testing, but the VR method revealed more usability issues in the efficiency category, while the physical space method revealed more issues in the effectiveness category.

In this commentary we would like to question (a) Clark and Fischer's characterization of the “social artifact puzzle” – which we consider less puzzling than the authors, and (b) their account of social robots as depictions involving three physical scenes – which to us seems unnecessarily complex. We contrast the authors' model with a more parsimonious account based on attributions.

Safe interaction with automated vehicles requires that human road users understand the differences between the capabilities and limitations of human drivers and their artificial counterparts. Here we explore how people judge what self-driving cars versus human drivers can perceive by engaging online study participants in visual perspective taking toward a car pictured in various traffic scenes. The results indicate that people do not expect self-driving cars to differ significantly from human drivers in their capability to perceive objects in the environment. This finding is important because unmet expectations can result in detrimental interaction outcomes, such as traffic accidents. The extent to which people are able to calibrate their expectations remains an open question for future research.