A Screen-Based Multimodal Virtual Classroom Interface for Understanding Behavioral Sensory Responses in Autistic Adolescents: A Pilot Study

Autism impacts at least 1 in 100 children worldwide, with about 90% experiencing sensory processing difficulties. Virtual Reality (VR), which can precisely deliver controlled sensory stimuli, has emerged as a promising tool for studying sensory experiences. However, VR systems using a head-mounted display may cause discomfort and exacerbate sensory challenges for autistic children. Screen-based VR could offer a viable alternative, but research on designing multimodal sensory delivery systems that simulate real-life experiences remains limited. As a result, the impact of on-screen VR on children's behavioral sensory responses is not well understood. Therefore, as a pilot study to fill this gap, we designed a novel screen-based Multimodal Virtual Classroom Interface (MVCI) system. MVCI was designed to deliver well-controlled visual, auditory, and tactile stimuli that closely mimic a real classroom environment. The pilot study involved 9 autistic adolescents and 17 typically developing (TD) adolescents, all of whom reported 100% acceptance of the system. Quantitative behavioral analysis demonstrated that, even with the small sample size, the on-screen interaction showed significant differences ( ${p} \lt 0.05$ ) between the two groups in terms of eye gaze, fine motor movements, and eye-hand alignment. Additionally, several behavioral patterns were strongly correlated with participants' sensory profiles and ADHD symptom severity ( ${p} \lt 0.05$ , ${r}_{s}$ >0.7). Using a novel Fixation Sequence Modeling (FSM) framework, we were able to predict participants' near-future performance with high accuracy (97-98% proximity) based on their granular behavioral responses.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tnsre.2025.3619484
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Screen-Based Multimodal Virtual Classroom Interface for Understanding Behavioral Sensory Responses in Autistic Adolescents: A Pilot Study

Suzannah Iadarola, Samantha G. Daley, Zhiwei Yu, Zhi Zheng
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Autism Spectrum Disorder Research
article

A Screen-Based Multimodal Virtual Classroom Interface for Understanding Behavioral Sensory Responses in Autistic Adolescents: A Pilot Study

Suzannah Iadarola, Samantha G. Daley, Zhiwei Yu, Zhi Zheng
article en

Abstract

Autism impacts at least 1 in 100 children worldwide, with about 90% experiencing sensory processing difficulties. Virtual Reality (VR), which can precisely deliver controlled sensory stimuli, has emerged as a promising tool for studying sensory experiences. However, VR systems using a head-mounted display may cause discomfort and exacerbate sensory challenges for autistic children. Screen-based VR could offer a viable alternative, but research on designing multimodal sensory delivery systems that simulate real-life experiences remains limited. As a result, the impact of on-screen VR on children's behavioral sensory responses is not well understood. Therefore, as a pilot study to fill this gap, we designed a novel screen-based Multimodal Virtual Classroom Interface (MVCI) system. MVCI was designed to deliver well-controlled visual, auditory, and tactile stimuli that closely mimic a real classroom environment. The pilot study involved 9 autistic adolescents and 17 typically developing (TD) adolescents, all of whom reported 100% acceptance of the system. Quantitative behavioral analysis demonstrated that, even with the small sample size, the on-screen interaction showed significant differences ( ${p} \lt 0.05$ ) between the two groups in terms of eye gaze, fine motor movements, and eye-hand alignment. Additionally, several behavioral patterns were strongly correlated with participants' sensory profiles and ADHD symptom severity ( ${p} \lt 0.05$ , ${r}_{s}$ >0.7). Using a novel Fixation Sequence Modeling (FSM) framework, we were able to predict participants' near-future performance with high accuracy (97-98% proximity) based on their granular behavioral responses.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
University of Notre Dame (US), University of Rochester Medical Center (US), University of Rochester (US)
Openalex Percentile: Top 72%
Autism Spectrum Disorder Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.