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Employment of autistic individuals is strikingly low in relation to the skill level and capabilities of this population. Roughly 65% of autistic adults are either unemployed or underemployed relative to their abilities but there is increasing recognition that this number could be greatly improved through empowering autistic individuals while simultaneously providing a boost to the economy. Much of this disparity can be attributed in part to the lack of awareness and understanding among employers regarding behavior of autistic individuals during the hiring process. Most notably, the job interview—where strong eye contact is traditionally expected but can be extremely uncomfortable for autistic individuals—presents an unreasonable initial barrier to employment for many. The current work presents a data visualization dashboard that is populated with quantitative data (including eye tracking data) captured during simulated job interviews using a novel interview simulator called Career Interview Readiness in Virtual Reality (CIRVR). We conducted a brief series of case studies wherein autistic individuals who took part in a CIRVR interview and other key stakeholders provided lived experiences and qualitative insights into the most effective design and application of such data visualization dashboard. We conclude with a discussion of the role of information related to visual attention in job interviews with an emphasis on the importance of descriptive rather than prescriptive interpretation. 

Studies show that young autistic adults are under- or unemployed, with almost half never holding a paying job in their 20’s. Unemployment within this population leads to decreased personal growth and increased dependence on caregivers. Research suggests that the interview process is one of the largest barriers to employment for this population. Autistic individuals often struggle with emotion regulation, which can be exacerbated by the interview process. To address this, we propose the use of a stress detection model in conjunction with a virtual reality interview simulator. This combination will allow for the interview to adapt to the state of the participant to improve the skills and engagement of the user and positively influence their comfort level. Data regarding negative affective responses to categories of questions can also be used to inform employers on better interviewing techniques. A model was designed using data obtained from neurotypical participants completing a modified Computerized Paced Serial Addition Task (PASAT-C) and evaluated on a dataset obtained from Autistic participants who took part in a simulated interview. Agreement between the model and ground truth was compared based on Pearson correlation coefficients. It was found that was r(289) = 0.28, which was statistically significant (p < .001; CI: 0.17 to 0.38). Our preliminary results provide evidence for the validity of observer-based labeling of data captured using a wrist-worn physiological sensor. 

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. 

The objective of this study is to develop and use a virtual reality game as a tool to assess the effects of realistic stress on the behavioral and physiological responses of participants. The game is based on a popular Steam game called Keep Talking Nobody Explodes, where the players collaborate to defuse a bomb. Varying levels of difficulties in solving a puzzle and time pressures will result in different stress levels that can be measured in terms of errors, response times, and other physiological measurements. The game was developed using 3D programming tools including Blender and a virtual reality development kit (VRTK). To measure response times accurately, we added LSL (Lab Stream Layer) Markers to collect and synchronize physiological signals, behavioral data, and the timing of game events. We recorded Electrocardiogram (ECG) data during gameplay to assess heart rate and heart-rate variability (HRV) that have been shown as reliable indicators of stress. Our empirical results showed that heart rate increased significantly while HRV reduced significantly when the participants under high stress, which are consistent with the prior mainstream stress research. This VR game framework is publicly available in GitHub and allows researchers to measure and synchronize other physiological signals such as electroencephalogram, electromyogram, and pupillometry. 

Block-based programming applications, such as MIT’s Scratch and Blockly Games, are commonly used to teach K-12 students to code. Due to the COVID-19 pandemic, many K-12 students are attending online coding camps, which teach programming using these block-based applications. However, these applications are not accessible to the Blind/Low Vision (BLV) population since they neither produce audio output nor are screen reader accessible. In this paper, we describe a solution to make block-based programming accessible to BLV students using Google’s latest Keyboard Navigation and present its evaluation with four individuals who are BLV. We distill our findings as recommendations to developers who may want to make their Block-based programming application accessible to individuals who are BLV. 

Environmental sounds can provide important information about surrounding activity, yet recognizing sounds can be challenging for Deaf and Hard-of-Hearing (DHH) individuals. Prior work has examined the preferences of DHH users for various sound-awareness methods. However, these preferences have been observed to vary along some demographic factors. Thus, in this study we investigate the preferences of a specific group of DHH users: current assistive listening devices users. Through a survey of 38 participants, we investigated their challenges and requirements for sound-awareness applications, as well as which type of sounds and what aspects of the sounds are of importance to them. We found that users of assistive listening devices still often miss sounds and rely on other people to obtain information about them. Participants indicated that the importance of awareness of different types of sounds varied according to the environment and the form factor of the sound-awareness technology. Congruent with prior work, participants reported that the location and urgency of the sound were of importance, as well as the confidence of the technology in its identification of that sound.