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1. Collaborative Virtual Environment to Encourage Teamwork in Autistic Adults in Workplace Settings

   https://doi.org/10.1007/978-3-030-78092-0_22  

   Amat, A; Breen, M; Hunt, S; Wilson, D; Khaliq, Y; Byrnes, N; Cox, D; Czarnecki, S; Justice, C; Kennedy, D; et al (July 2021, International Conference on Human-Computer Interaction (HCII))

   Antona, M; null (Ed.)

   The employment settings for autistic individuals in the USA is grim. As more children are diagnosed with ASD, the number of adolescent and young adult with ASD will increase as well over the next decade. Based on reports, one of the main challenges in securing and retaining employment for individual with ASD is difficulty in communicating and working with others in workplace settings. Most vocational trainings focused on technical skills development and very few addresses teamwork skills development. In this study, we present the design of a collaborative virtual environment (CVE) that support autistic individual to develop their teamwork skills by working together with a partner in a shared virtual space. This paper described the CVE architecture, teamwork-based tasks design and quantitative measures to evaluate teamwork skills. A system validation was also carried out to validate the system design. The results showed that our CVE was able to support multiple users in the same shared environment, the tasks were tolerable by users, and all the quantitative measures are recorded accordingly. 

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2. Career Interview Readiness in Virtual Reality (CIRVR): A Platform for Simulated Interview Training for Autistic Individuals and Their Employers

   https://doi.org/10.1145/3505560  

   Adiani, Deeksha; Itzkovitz, Aaron; Bian, Dayi; Katz, Harrison; Breen, Michael; Hunt, Spencer; Swanson, Amy; Vogus, Timothy J.; Wade, Joshua; Sarkar, Nilanjan (March 2022, ACM Transactions on Accessible Computing)

   Employment outcomes for autistic 1 individuals are often poorer relative to their neurotypical (NT) peers, resulting in a greater need for other forms of financial and social support. While a great deal of work has focused on developing interventions for autistic children, relatively less attention has been paid to directly addressing the employment challenges faced by autistic adults. One key impediment to autistic individuals securing employment is the job interview. Autistic individuals often experience anxiety in interview situations, particularly with open-ended questions and unexpected interruptions. They also exhibit atypical gaze patterns that may be perceived as, but not necessarily indicative of, disinterest or inattention. In response, we developed a closed-loop adaptive virtual reality (VR)–based job interview training platform, which we have named Career Interview Readiness in VR (CIRVR). CIRVR is designed to provide an engaging, adaptive, and individualized experience to practice and refine interviewing skills in a less anxiety-inducing virtual context. CIRVR contains a real-time physiology-based stress detection module, as well as a real-time gaze detection module, to permit individualized adaptation. We also present the first prototype of the CIRVR Dashboard, which provides visualizations of data to help autistic individuals as well as potential employers and job coaches make sense of the data gathered from interview sessions. We conducted a feasibility study with 9 autistic and 8 NT individuals to assess the preliminary usability and feasibility of CIRVR. Results showed differences in perceived usability of the system between autistic and NT participants, and higher levels of stress in autistic individuals during interviews. Participants across both groups reported satisfaction with CIRVR and the structure of the interview. These findings and feedback will support future work in improving CIRVR’s features in hopes for it to be a valuable tool to support autistic job candidates as well as their potential employers. 

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3. A study on an applied behavior analysis-based robot-mediated listening comprehension intervention for ASD

   https://doi.org/10.1515/pjbr-2021-0005  

   Louie, Wing-Yue Geoffrey; Korneder, Jessica; Abbas, Ibrahim; Pawluk, Cristyn (November 2020, Paladyn, Journal of Behavioral Robotics)

   null (Ed.)

   Abstract Autism spectrum disorder (ASD) is a lifelong developmental condition that affects an individual’s ability to communicate and relate to others. Despite such challenges, early intervention during childhood development has shown to have positive long-term benefits for individuals with ASD. Namely, early childhood development of communicative speech skills has shown to improve future literacy and academic achievement. However, the delivery of such interventions is often time-consuming. Socially assistive robots (SARs) are a potential strategic technology that could help support intervention delivery for children with ASD and increase the number of individuals that healthcare professionals can positively affect. For SARs to be effectively integrated in real-world treatment for individuals with ASD, they should follow current evidence-based practices used by therapists such as Applied Behavior Analysis (ABA). In this work, we present a study that investigates the efficacy of applying well-known ABA techniques to a robot-mediated listening comprehension intervention delivered to children with ASD at a university-based ABA clinic. The interventions were delivered in place of human therapists to teach study participants a new skill as a part of their overall treatment plan. All the children participating in the intervention improved in the skill being taught by the robot and enjoyed interacting with the robot, as evident by high occurrences of positive affect as well as engagement during the sessions. One of the three participants has also reached mastery of the skill via the robot-mediated interventions. 

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4. Transparent Learning from Demonstration for Robot-Mediated Therapy

   https://doi.org/10.1109/RO-MAN53752.2022.9900854  

   Tyshka, Alexander; Louie, Wing-Yue Geoffrey (August 2022, 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   Robot-mediated therapy is an emerging field of research seeking to improve therapy for children with Autism Spectrum Disorder (ASD). Current approaches to autonomous robot-mediated therapy often focus on having a robot teach a single skill to children with ASD and lack a personalized approach to each individual. More recently, Learning from Demonstration (LfD) approaches are being explored to teach socially assistive robots to deliver personalized interventions after they have been deployed but these approaches require large amounts of demonstrations and utilize learning models that cannot be easily interpreted. In this work, we present a LfD system capable of learning the delivery of autism therapies in a data-efficient manner utilizing learning models that are inherently interpretable. The LfD system learns a behavioral model of the task with minimal supervision via hierarchical clustering and then learns an interpretable policy to determine when to execute the learned behaviors. The system is able to learn from less than an hour of demonstrations and for each of its predictions can identify demonstrated instances that contributed to its decision. The system performs well under unsupervised conditions and achieves even better performance with a low-effort human correction process that is enabled by the interpretable model. 

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5. Supporting Students with an Autism Spectrum Disorder in Engineering: K-12 and Beyond

   https://doi.org/10.14448/jsesd.13.0011  

   Kouo, Jennifer L.; Hahn, Alexis E.; Morton, Sarah; Gregorio, Jay (October 2021, Journal of Science Education for Students with Disabilities)

   Individuals with disabilities, including individuals with an autism spectrum disorder (ASD), are underrepresented in science, technology, engineering, and mathematics (STEM) fields. With the importance of STEM skills in future employment and other disciplines, effective instructional strategies must be identified to enhance early and sustained access to STEM for students with ASD. However, the literature identifying effective STEM-specific supports and practices for this population of students is sparse and regarding engineering, there are no empirical studies that focus on teaching engineering skills to students with ASD. Therefore, the article aims to provide an overview of the available literature on the perspectives of engineering educators and suggested strategies aimed at supporting students with ASD in K-12 instruction and higher education. Additionally, recommendations regarding employment preparation and shifting the workplace environment to support individuals with ASD are presented. The available literature reveals limitations and implications for future research including the presentation of the voices of individuals with ASD across the spectrum. Furthermore, there continues to be work that must be done to prepare educators, employers, peers, and colleagues to better understand the disability and support individuals with ASD in all contexts. 

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