Tell Me About It: Adolescent Self-Disclosure with an Online Robot for Mental Health

Self-disclosure to a social robot is a mental health intervention that can decrease stress for adolescents. Online digital robots provide the potential to scale this intervention especially in COVID-19 social distancing situations. However, self-disclosure interactions with digital social robots remain relatively unexplored. We conducted two online self-disclosure studies with adolescents (13-19 years old): our Active Listening Study compared experiences sharing positive, negative, and neutral feelings with a social robot, while our Journaling Study explored differences in sharing stressors by speaking with and without a social robot and by writing. We found that positive prompt tone improved mood while neutral prompt decreased stress, and less negative attitudes toward robots correlate with more qualitatively positive experiences with robot interactions.We also found robot disclosure interactions hold promising potential as a preferred method of self-disclosure over solo speaking, moderated by negative attitudes toward robots. This paper outlines limitations and future work from these studies. © 2023 IEEE Computer Society. All rights reserved.

Robots for Connection: A Co-Design Study with Adolescents

Adolescents isolated at home during the COVID19 pandemic lockdown are more likely to feel lonely and in need of social connection. Social robots may provide a much needed social interaction without the risk of contracting an infection. In this paper, we detail our co-design process used to engage adolescents in the design of a social robot prototype intended to broadly support their mental health. Data gathered from our four week design study of nine remote sessions and interviews with 16 adolescents suggested the following design requirements for a home robot: (1) be able to enact a set of roles including a coach, companion, and confidant;(2) amplify human-to-human connection by supporting peer relationships;(3) account for data privacy and device ownership. Design materials are available in open-access, contributing to best practices for the field of Human-Robot Interaction. © 2022 IEEE.

Engaging English Language Learners as Cultural Informants in the Design of a Social Robot for Education

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.