Interactive VR-based mobile training framework for adolescents with autism spectrum disorder
Bibliographic record
Abstract
Virtual reality (VR) technologies have become powerful tools for delivering interactive learning experiences, offering controlled and adaptive environments for skill development. This study introduces an AI-enhanced standalone VR training framework designed for adolescents with autism spectrum disorder (ASD), integrating two interactive environments—a restaurant and a classroom—to support both life skills and educational competencies. The system was developed using Unity and Blender and delivered through Oculus Quest 3 standalone head-mounted displays with a dual-interface monitoring system for therapists and parents. A structured four-phase protocol (orientation, environment-specific training, integrated practice, and assessment) guided 15 participants (11 males, 4 females), aged 10–13 years, through 12 standardized sessions, producing 180 session-level records. Quantitative data included task completion time, error frequency, number of attempts, and interaction patterns, while qualitative feedback was collected from therapists and parents. Statistical analyses (paired t-tests, repeated measures ANOVA) revealed significant phase-related changes in completion time, error frequency, and task attempts, while success rates remained stable. An AI component using a Decision Tree classifier achieved 70.4% accuracy in predicting task outcomes, providing preliminary evidence for the role of machine learning in adaptive feedback and personalized interventions. Findings suggest that the proposed standalone VR framework enhances engagement and skill development among adolescents with ASD while offering valuable analytics for educators and therapists. The integration of VR, AI, and dual-environment design underscores the potential of immersive technologies to support scalable, adaptive, and data-driven interventions in special education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".