Group-Based Online Job Interview Training Program Using Virtual Robot for Individuals With Autism Spectrum Disorders

A new group-based online job interview training program using computer graphics robots for individuals with autism spectrum disorders

Introduction

Job interviews are a major barrier to employment for individuals with autism spectrum disorders (ASD). During the coronavirus pandemic, establishing online job interview training at home was indispensable. However, many hurdles prevent individuals with ASD from concentrating on online job interview training. To facilitate the acquisition of interview skills from home for individuals with ASD, we developed a group interview training program with a virtual conferencing system (GIT-VICS Program) that uses computer graphics (CG) robots.

Methods

This study investigated the feasibility of the GIT-VICS Program in facilitating skill acquisition for face-to-face job interviews in pre-post measures. In the GIT-VICS Program, five participants were grouped and played the roles of interviewees (1), interviewers (2), and human resources (2). They alternately practiced each role in GIT-VICS Program sessions conducted over 8 or 9 days over three consecutive weeks. Before and after the GIT-VICS Program, the participants underwent a mock face-to-face job interview with two experienced human interviewers (MFH) to evaluate its effect.

Results

Fourteen participants completed the trial procedures without experiencing any technological challenges or distress that would have led to the termination of the session. The GIT-VICS Program improved their job interview skills (verbal competence, nonverbal competence, and interview performance).

Discussion

Given the promising results of this study and to draw clear conclusions about the efficacy of CG robots for mock online job interview training, future studies adding appropriate guidance for manner of job interview by experts are needed.

Differences in the Optimal Motion of Android Robots for the Ease of Communications Among Individuals With Autism Spectrum Disorders

Android robots are employed in various fields. Many individuals with autism spectrum disorders (ASD) have the motivation and aptitude for using such robots. Interactions with these robots are structured to resemble social situations in which certain social behaviors can occur and to simulate daily life. Considering that individuals with ASD have strong likes and dislikes, ensuring not only the optimal appearance but also the optimal motion of robots is important to achieve smooth interaction and to draw out the potential of robotic interventions. We investigated whether individuals with ASD found it easier to talk to an android robot with little motion (i.e., only opening and closing its mouth during speech) or an android robot with much motion (i.e., in addition to opening and closing its mouth during speech, moving its eyes from side to side and up and down, blinking, deeply breathing, and turning or moving its head or body at random). This was a crossover study in which a total of 25 participants with ASD experienced mock interviews conducted by an android robot with much spontaneous facial and bodily motion and an android robot with little motion. We compared demographic data between participants who answered that the android robot with much motion was easier to talk to than android robot with little motion and those who answered the opposite. In addition, we investigated how each type of demographic data was related to participants' feeling of comfort in an interview setting with an android robot. Fourteen participants indicated that the android robot with little motion was easier to talk to than the robot with much motion, whereas 11 participants answered the opposite. There were significant differences between these two groups in the sensory sensitivity score, which reflects the tendency to show a low neurological threshold. In addition, we found correlations between the sensation seeking score, which reflects the tendency to show a high neurological threshold, and self-report ratings of comfort in each condition. These results provide preliminary support for the importance of setting the motion of an android robot considering the sensory traits of ASD.