With the age of diversification, it’s important to nurture the qualities of children to enable them to create their future society. To achieve this, it’s crucial for children to broaden and deepen their views through dialogue with others. In the field of human-agent interaction, introducing robots into educational settings has shown positive educational effects. In this study, we developed an autonomous dialogue robot that delved into the children’s ideas and prompted them to think about aspects they had not yet considered, to broaden and deepen their viewpoints. We introduced this developed robot into a 6th-grade science class and conducted an experiment. The results based on subjective evaluations by students through questionnaires and objective evaluations of the students’ reaction papers by third parties, suggest that discussions with the robot can broaden and deepen the viewpoints of students. This study highlights the potential of robots to support the important educational goal.

Conversational recommendation systems are crucial for making recommendations agreeable to the user. To reach an agreeable recommendation, this study proposes a dialog strategy that represents a reasonable order of items and elicits the current estimation by the wording of utterances based on the subjective preference estimation. We developed two dialog functions for the topic and word choice based on the history of preference estimations. The human impression of a robot’s diligence, understanding capability, and satisfaction were evaluated through a conversation with a virtual robot using a crowdsourcing platform. We compared six conditions that differed based on two topics and three wording patterns. The experimental results indicated that the main effect of the wording patterns, whereas one of the topic choices was not found to be significant. Further analysis showed that accurate estimation improves the robot’s impression when demonstrating its diligence.

Mobile robots are increasingly being utilized as guides in various settings. Ensuring efficient and hospitable interactions between robots and individuals is crucial for their successful implementation. However, there is a lack of understanding regarding the specific robot behaviors that can effectively guide individuals to their destinations while providing a sense of hospitality. In this study, we investigate two robot behaviors: “vertical oscillation motion” and “looking back motion,” with the objective of identifying behaviors that can optimize efficiency and hospitality in a guide robot. The vertical oscillation motion replicates the natural up-and-down movement of the human body during walking, while the looking back motion establishes eye contact and conveys a sense of hospitality. To evaluate the effectiveness of these behaviors, we conducted experiments with actual visitors at a shopping mall. Participants were exposed to the robot's behaviors and their responses were analyzed through questionnaires and interviews. The results revealed that the looking back motion successfully conveyed the robot's hospitality, while the vertical oscillation motion instilled a sense of urgency in individuals. However, it was found that only the looking back motion effectively motivated individuals to promptly commence walking towards the robot. These findings highlight the significance of incorporating specific robot behaviors to enhance both efficiency and hospitality in guiding individuals to their destinations. By understanding the impact of these behaviors, we can develop guide robots that accommodate a larger number of individuals while increasing their satisfaction. Future research endeavors should focus on evaluating the effectiveness of these behaviors in real-world environments, ensuring their practical applicability.

With recent technological developments, such as machine learning, data collection has become increasingly important. However, due to privacy issues, there is an ethical problem in collecting data on everyday activities. In this study, we aimed to develop a mechanism for acquiring consent interactively by using a robot and, as a first step, focus on a dialogue strategy in which users feel safe to provide their data. Specifically, we investigated the effects of reminder and rationale provided by the robot in situations where consent is obtained. We conducted two experiments in which users conversed with a virtual robot agent in a crowd setting. We found that the robot reminder improved the degree of understanding regarding the use of data. In addition, the permission rate was improved by robot notifications. However, no effect of rationale was observed. These results contribute to the discussion on the ethical aspects of data collection by robots.

To provide a better discussion experience in current argumentative dialogue systems, it is necessary for the user to feel motivated to participate, even if the system already responds appropriately.In this paper, we propose a method that can smoothly introduce argumentative dialogue by inserting an initial discourse, consisting of question-answer pairs concerning personality.The system can induce interest of the users prior to agreement or disagreement during the main discourse.By disclosing their interests, the users will feel familiarity and motivation to further engage in the argumentative dialogue and understand the system's intent.To verify the effectiveness of a questionanswer dialogue inserted before the argument, a subjective experiment was conducted using a text chat interface.The results suggest that inserting the questionanswer dialogue enhances familiarity and naturalness.Notably, the results suggest that women more than men regard the dialogue as more natural and the argument as deepened, following an exchange concerning personality.