This paper reports the effects of praise from two robots on children’s learning time, their performance, and their acceptance of the robots. Since praise from a single robot provides positive effects for children as educational support, we hypothesized that praise from two robots will increase such effects because two robots will enhance the social influences. To verify this hypothesis, we developed an English learning system that consisted of an e-learning application, a depth sensor for human-tracking, a tabletop humanoid robot, a doll-type robot, and an operator. We designed the robots to interactively praise children during their learning situations and experimented with a with-in participant design to compare the effects of praise from two robots and just one. 22 children participated in an experiment whose results showed that children learned more when two robots praised them than just one, even with an identical amount of praise. These results suggest the effectiveness and empirical evidence of using two robots for educational support. GRAPHICAL ABSTRACT

This study investigated how exposure to changing or reaffirmation opinions in robot conversations influences the impressions of observers and their trust in media. Even though the provided conversational contents include the same amount of information, their order, positive/negative attitudes, and discussion styles change their perceived impressions. We conducted a web survey using video stimuli, where two robots discussed Japan’s first state of emergency response to the COVID-19 pandemic. We prepared two patterns of opinion changes to a different side (positive–negative and negative–positive) and two patterns of opinion reaffirmation (positive–positive and negative–negative) with identical information contents; we only modified their order. The experimental results showed that exposure to opinion changes from the positive side (i.e., negative–positive) or positive opinion reaffirmation (positive–positive) effectively provides positive and fair impressions. Exposure to an opinion that became negative (i.e., positive–negative) effectively provided negative and fair impressions, although negative opinion reaffirmation (negative–negative) led to significantly less trust in media.

We have been examining how an intelligent lighting system affects energy consumption in the office. In this paper, we evaluate the traditional intelligent lighting system at the office and how to improve the best use of the intelligent lighting system by each user. The user in the large office is not able to change the level of luminance. Moreover, the traditional lighting system is not able to specify which desk is occupied or not occupied by each worker. The proper use of lighting control affects the power consumption, and the worker sometimes forgets to change the occupancy status after leaving the office, which affects the energy. This paper argues to find the best model of the intelligent lighting system to save energy by using the technology of sensing devices for detecting the occupancy of the desk. As a result, we found that each worker has the individual lighting system, and the energy of the office is reduced by improving the intelligent lighting system.

With the expansion of social robots’ working environments, developing strategies to mitigate their mistakes has become crucial, especially given the difficulty of entirely avoiding errors. Previous studies have explored effective strategies for robot apologies, including conversational content, yet have paid less attention to nonverbal behaviors like bowing. In this study, we focused on the modeling of bowing behaviors. A distinctive aspect of our study is its consideration of failure severity, since we hypothesized that apology behaviors vary with the situation. For instance, a minor delay might prompt a rather mechanical apology, whereas a serious failure would undoubtedly elicit a more sincere response. We elucidated the relationship in apologies between failure severity and bowing behaviors, based on data collected from participants. Then we implemented the modeled bowing behavior in a robot and conducted a web-based survey to assess its effectiveness. Our findings highlighted the significance of taking into account the severity of bowing behaviors.