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Integrating cultural responsiveness into the educational setting is essential to the success of multilingual students. As social robots present the potential to support multilingual children, it is imperative that the design of social robot embodiments and interactions are culturally responsive. This paper summarizes the current literature on educational robots in culturally diverse settings. We argue the use of the Culturally Localized User Experience (CLUE) Framework is essential to ensure cultural responsiveness in HRI design. We present three case studies illustrating the CLUE framework as a social robot design approach. The results of these studies suggest co-design provides multicultural learners an accessible, nonverbal context through which to provide design requirements and preferences. Furthermore, we demonstrate the importance of key stakeholders (students, parents, and teachers) as essential to ensure a culturally responsive robot. Finally, we reflect on our own work with culturally and linguistically diverse learners and propose three guiding principles for successfully engaging diverse learners as valuable cultural informants to ensure the future success of educational robots. 

Background: There are 4.9 million English Language Learners (ELLs) in the United States. Only 2% of educators are trained to support these vulnerable students. Social robots show promise for language acquisition and may provide valuable support for students, especially as we return to needing smaller classes due to COVID-19. While cultural responsiveness increases gains for ELLs, little is known about the design of culturally responsive child–robot interactions. Method: Therefore, using a participatory design approach, we conducted an exploratory study with 24 Spanish-speaking ELLs at a Pacific Northwest elementary school. As cultural informants, students participated in a 15-min, robot-led, small group story discussion followed by a post-interaction feedback session. We then conducted reflexive critiques with six ELL teachers who reviewed the group interactions to provide further interpretation on design feature possibilities and potential interactions with the robot. Results: Students found the social robot engaging, but many were hesitant to converse with the robot. During post-interaction dialogue students articulated the specific ways in which the social robot appearance and behavior could be modified to help them feel more comfortable. Teachers postulated that the social robot could be designed to engage students in peer-to-peer conversations. Teachers also recognized the ELLs verbosity when discussing their experiences with the robot and suggested such interactions could stimulate responsiveness from students. Conclusion: Cultural responsiveness is a key component to successful education in ELLs. However, integrating appropriate, cultural responsiveness into robot interactions may require participants as cultural informants to ensure the robot behaviors and interactions are situated in that educational community. Utilizing a participatory approach to engage ELLs in design decisions for social robots is a promising way to gather culturally responsive requirements to inform successful child–robot interactions.