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.

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.

Safe and efficient interaction with autonomous road vehicles requires that human road users, including drivers, cyclists, and pedestrians, understand differences between the capabilities and limitations of self-driving vehicles and those of human drivers. In this study, we explore how people judge the ability of self-driving cars versus human drivers to keep track of out-of-sight objects by engaging online study participants in cognitive perspective taking toward a car in an animated traffic scene. The results indicate that people may expect self-driving cars to have similar object permanence capability as human drivers. This finding is important because unmet expectations on autonomous road vehicles can result in undesirable interaction outcomes, such as traffic accidents.

Much research in robotic artificial intelligence (AI) and Artificial Life has focused on autonomous agents as an embodied and situated approach to AI. Such systems are commonly viewed as overcoming many of the philosophical problems associated with traditional computationalist AI and cognitive science, such as the grounding problem (Harnad) or the lack of intentionality (Searle), because they have the physical and sensorimotor grounding that traditional AI was argued to lack. Robot lawn mowers and self-driving cars, for example, more or less reliably avoid obstacles, approach charging stations, and so on—and therefore might be considered to have some form of artificial intentionality or intentional directedness. It should be noted, though, that the fact that robots share physical environments with people does not necessarily mean that they are situated in the same perceptual and social world as humans. For people encountering socially interactive systems, such as social robots or automated vehicles, this poses the nontrivial challenge to interpret them as intentional agents to understand and anticipate their behavior but also to keep in mind that the intentionality of artificial bodies is fundamentally different from their natural counterparts. This requires, on one hand, a “suspension of disbelief ” but, on the other hand, also a capacity for the “suspension of belief.” This dual nature of (attributed) artificial intentionality has been addressed only rather superficially in embodied AI and social robotics research. It is therefore argued that Bourgine and Varela’s notion of Artificial Life as the practice of autonomous systems needs to be complemented with a practice of socially interactive autonomous systems, guided by a better understanding of the differences between artificial and biological bodies and their implications in the context of social interactions between people and technology.

Autonomous vehicles that operate on public roads need to be predictable to others, including vulnerable road users. In this study, we asked participants to take the perspective of videotaped pedestrians and cyclists crossing paths with an automated shuttle bus, and to (1) judge whether the bus would stop safely in front of them and (2) report whether the bus's actual stopping behavior accorded with their expectations. The results show that participants expected the bus to brake safely in approximately two thirds of the human-vehicle interactions, more so to pedestrians than cyclists, and that they tended to underestimate rather than overestimate the bus's capability to yield in ways that they considered as safe. These findings have implications for the design and implementation of automated shuttle bus services.

The overall aim of the school subject technology is to develop pupils’ understanding of technological solutions in everyday life. A starting point for this study is that it is important for teachers in technology to have knowledge of pupils’ prior conceptions of the subject content since these can both support and hinder their learning. In a previous study we found that when pupils (age 7) talk about digital technology and programming, they often refer to out-of-school experiences such as films, television programmes and books. Typically, their descriptions include robots with some form of intelligence. Hence, it seems like children’s culture may have an impact on the conceptions they bring to the technology classroom. In light of this, it is vital that technology teachers have knowledge about how robots and artificial intelligence (AI) are portrayed in children’s culture, and how pupils perceive these portrayals. However, knowledge about these aspects of technology in children’s culture is limited.The purpose of this study is to investigate how artifacts with artificial intelligence are portrayed in television programmes and literature aimed at children. This study is the first step in a larger study aiming to examine younger pupils’ conceptions and ideas about artificial intelligence. A novice conception of artificial intelligence can be described as an understanding of what a programmed device may, or may not, “understand” in relation to a human, which includes discerning th edifferences between the artificial and the human mind. Consequently, as a theoretical framework for investigating how artificial intelligence is portrayed in children’s culture, the concepts of Theoryof Mind (ToM) and Theory of Artificial Mind (ToAM), are used. The empirical material presented in this paper, i.e. four children’s books and a popular children’s television programme, was analysed using a qualitative thematic analysis. The results show that the portrayal of AI is ambiguous. The structure and function of the robot has elements of both human and machine, and the view of the human fictional characters of the robot is sometimes that of a machine, sometimes of a human. In addition, the whole empirical material includes portrayals of AI as a threat as well as a saviour. As regards implications, there is a risk that without real-life experiences of robots, the representations children’s books and other media convey can lead to ambivalent feelings towards real robots.