教育中社交机器人身份多样性的案例研究

属于弱势社会身份的学生，其教育成果不可避免地受到沿性别、族裔和年龄等线路产生的不公平重叠系统的影响。Furhat等机器人平台要求设计者选择被用户解释为这些相同社会身份的特征。之前的工作假设社交机器人可以被故意设计为以"打破常规"的方式利用这些社会身份，旨在打破STEM教育中的社会刻板印象。然而，人机交互研究 largely limited to the examination of gender only. We present a 2x2, between-subjects study in which 161 participants aged 9-12 are shown a robot-delivered lecture presented by a group of three separate robot personas with varying gender and ethnicity performances. We find that participants place greater trust in the persona groups with high gender diversity. Incorporating ethnic diversity seems to have little impact on our quantitative interaction metrics, however we do find evidence to suggest diversity in robots' language capabilities may be important for trustworthiness. In all, the study contributes nuance to the discussions on the implications of (norm-breaking) social identity performance when using robots to pursue more equitable STEM education.