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Designing a Digital Visual-Analogy Motor Learning Program for Children with Autism Spectrum Disorder: Development, Implementation, and Preliminary Outcomes

Document Type : Original Article

Authors

1 IR International University

2 University of Calgary, Canada

3 SSRC

4 Islamic Azad University

APSS/apss.2026.2089744.1025
Abstract
Children with Autism Spectrum Disorder (ASD) often experience significant motor-learning difficulties, including impairments in coordination, balance, motor planning, movement timing, and postural control. Traditional motor-learning approaches frequently rely on explicit verbal instruction, which may be less effective for children with ASD due to challenges in language processing, working memory, attention regulation, and executive functioning. In contrast, many children with ASD demonstrate strengths in visual processing and visually guided learning, suggesting that visual-based instructional approaches may better support motor-skill acquisition.

The present study developed a Digital Visual-Analogy Motor Learning Program (DVAML) specifically designed for children with ASD. The program integrated principles of visual-analogy learning, implicit motor learning, and sensory-sensitive digital design. Using a developmental–applied framework,

Preliminary findings demonstrated improvements in motor accuracy, movement consistency, engagement, and short-term retention following participation in the intervention program. Children showed increased attention to visual demonstrations, greater willingness to repeat movements, reduced reliance on verbal prompting, and stronger participation during sessions. Personalized visual themes and digital reward systems also appeared to enhance motivation and engagement. The findings suggest that DVAML may represent a promising, accessible, and scalable intervention for improving motor learning in children with ASD.

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Articles in Press, Accepted Manuscript
Available Online from 02 June 2026

  • Receive Date 26 May 2026
  • Revise Date 02 June 2026
  • Accept Date 02 June 2026
  • First Publish Date 02 June 2026
  • Publish Date 02 June 2026