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How can we generate appropriate behavior for social artificial agents? A common approach is to (1) establish with controlled experiments which action is most appropriate in which setting, and (2) select actions based on this knowledge and an estimate of the setting. This approach faces challenges, as it can be very hard to acquire and reason with all the required knowledge. Estimating the setting is challenging too, as many relevant aspects of the setting (e.g. personality of the interactee) can be unobservable. We formally describe an alternative approach that can handle these challenges; responsiveness. This is the idea that a social agent can utilize the many feedback cues given in social interactions to continuously adapt its behavior to something more appropriate. We theoretically discuss the relative advantages and disadvantages of these two approaches, which allows for more explicitly considering their application in social agents.

In the present study, we investigated teachers’ attitudes toward teaching with education robots and robot-mediated learning processes. We further explored predictors of attitudes, and investigated teachers’ willingness to use robots in diverse learning settings. To do so, we conducted a survey with 59 German school teachers. Our results suggest that teachers held rather negative attitudes toward education robots. Further, our findings indicate a positive association between technology commitment and teachers’ attitudes. Teachers reported a preferable use of robots in domains related to science, technology, engineering, and mathematics (STEM). Regarding expectations toward the future use of education robots, teachers mentioned their motivational potential, using robots as information source, or easy handling. Teachers’ concerns, however, were associated with the disruption of teaching processes, additional workload, or the fear that robots might replace interpersonal relationships. Implications of our findings for theory and design of education robots are discussed.

Social robots are machines developed to interact with humans. Unlike other technological devices, their presence in society requires accepting and treating them as social agents. This has important implications in terms of social changes for humans’ personal and social identity, and social interactions. We aim to explain the core features that characterize social robots by highlighting what makes them distinct from other types of innovative technology. Equally important, we illustrate how social psychology can provide a useful perspective to understand human-robot interactions. To do so, we focus on studies that have investigated the role of intergroup relations and social identity in the context of human-machine interactions to demonstrate that robots may comprise a new type of social outgroup in future society.

Preventing diseases of affluence is one of the major challenges for our future society. Recently, robots have been introduced as support for people on dieting or rehabilitation tasks. In our current work, we are investigating how the companionship and acknowledgement of a socially assistive robot (SAR) can influence the user to persist longer on a planking task. We conducted a 2 (acknowledgement vs. no-acknowledgment) x 2 (instructing vs. exercising together) x 1 (baseline) study with 96 subjects. We observed a motivational gain if the robot is exercising together with the user or if the robot is giving acknowledging feedback. However, we could not find an increase in motivation if the robot is showing both behaviors. We attribute the later finding to ceiling effects and discuss why we could not find an additional performance gain. Moreover, we highlight implications for SAR researchers developing robots to motivate people to extend exercising duration.

In an online survey, we studied user expectations and preferences for functions and apps in the context of a smart apartment. Furthermore, we explored which type of interface users would choose for an interaction with the smart apartment. Equally important, we investigated users’ acceptance of a service robot in the smart home. Results showed high levels of acceptance for both, the smart apartment and the robot, although the preferred interface for the apartment was context dependent. We discuss implications of the current survey and highlight key aspects to be taken into consideration when developing innovation technology for the home context.