The Cognitive Bias in Cross-Cultural Design

Rau, Pei-Luen Patrick; Guo, Zhao‐Xia; Qie, Nan; Xin, Lei; Zhang, Andong

doi:10.1007/978-3-030-35574-6_24

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…In order to enhance robots’ interaction with people of different cultural backgrounds, Trovato et al (2012 , 2013) recommended alignment between the nationality of the subjects and the cultural characterization of the robot’s facial expression, gestures, and ways of speaking such as greeting. Rau et al (2020) called attention to cultural tendencies resulting in cognitive biases which designers must take into account in cross-cultural design to increase user comfort and accessibility. Nomura et al (2016) suggested that designers thoroughly survey people’s expectations towards robots in the country where the robots are to be deployed.…”

Section: Cultural Explorations Of Social Robotsmentioning

confidence: 99%

Designing for culturally responsive social robots: An application of a participatory framework

Louie

Björling²,

Kuo³

et al. 2022

Front. Robot. AI

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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.

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