
The Acceptability of Virtual Characters as Social Skills Trainers: Usability Study
Author(s) -
Hiroki Tanaka,
Satoshi Nakamura
Publication year - 2022
Publication title -
jmir human factors
Language(s) - English
Resource type - Journals
ISSN - 2292-9495
DOI - 10.2196/35358
Subject(s) - virtual agent , usability , social skills , psychology , virtual training , virtual reality , applied psychology , computer science , multimedia , human–computer interaction , developmental psychology
Background Social skills training by human trainers is a well-established method to provide appropriate social interaction skills and strengthen social self-efficacy. In our previous work, we attempted to automate social skills training by developing a virtual agent that taught social skills through interaction. Previous research has not investigated the visual design of virtual agents for social skills training. Thus, we investigated the effect of virtual agent visual design on automated social skills training. Objective The 3 main purposes of this research were to investigate the effect of virtual agent appearance on automated social skills training, the relationship between acceptability and other measures (eg, likeability, realism, and familiarity), and the relationship between likeability and individual user characteristics (eg, gender, age, and autistic traits). Methods We prepared images and videos of a virtual agent, and 1218 crowdsourced workers rated the virtual agents through a questionnaire. In designing personalized virtual agents, we investigated the acceptability, likeability, and other impressions of the virtual agents and their relationship to individual characteristics. Results We found that there were differences between the virtual agents in all measures (P<.001). A female anime-type virtual agent was rated as the most likeable. We also confirmed that participants’ gender, age, and autistic traits were related to their ratings. Conclusions We confirmed the effect of virtual agent design on automated social skills training. Our findings are important in designing the appearance of an agent for use in personalized automated social skills training.