Promoting Self-Efficacy of Individuals With Autism in Practicing Social Skills in the Workplace Using Virtual Reality and Physiological Sensors: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Individuals with autism often experience heightened anxiety in workplace environments because of challenges in communication and sensory overload. As these experiences can result in negative self-image, promoting their self-efficacy in the workplace is crucial. Virtual reality (VR) systems have emerged as promising tools for enhancing the self-efficacy of individuals with autism in navigating social scenarios, aiding in the identification of anxiety-inducing situations, and preparing for real-world interactions. However, there is limited research exploring the potential of VR to enhance self-efficacy by facilitating an understanding of emotional and physiological states during social skills practice. OBJECTIVE: This study aims to develop and evaluate a VR system that enabled users to experience simulated work-related social scenarios and reflect on their behavioral and physiological data through data visualizations. We intended to investigate how these data, combined with the simulations, can support individuals with autism in building their self-efficacy in social skills. METHODS: We developed WorkplaceVR, a comprehensive VR system designed for engagement in simulated work-related social scenarios, supplemented with data-driven reflections of users' behavioral and physiological responses. A within-subject deployment study was subsequently conducted with 14 young adults with autism to examine WorkplaceVR's feasibility. A mixed methods approach was used, compassing pre- and postsystem use assessments of participants' self-efficacy perceptions. RESULTS: The study results revealed WorkplaceVR's effectiveness in enhancing social skills and self-efficacy among individuals with autism. First, participants exhibited a statistically significant increase in perceived self-efficacy following their engagement with the VR system (P=.02). Second, thematic analysis of the interview data confirmed that the VR system and reflections on the data fostered increased self-awareness among participants about social situations that trigger their anxiety, as well as the behaviors they exhibit during anxious moments. This increased self-awareness prompted the participants to recollect their related experiences in the real world and articulate anxiety management strategies. Furthermore, the insights uncovered motivated participants to engage in self-advocacy, as they wanted to share the insights with others. CONCLUSIONS: This study highlights the potential of VR simulations enriched with physiological and behavioral sensing as a valuable tool for augmenting self-efficacy in workplace social interactions for individuals with autism. Data reflection facilitated by physiological sensors helped participants with autism become more self-aware of their emotions and behaviors, advocate for their characteristics, and develop positive self-beliefs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".