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A DeepSeek cross-modal platform for personalized art education in Autism Spectrum Disorder
Yaoyao Ding1, Zichang Li2,3, Yuntao Zou4,5
1School of Design and Art, Communication University of Zhejiang, Hangzhou, China.
Scientific Reports
|December 30, 2025
Summary
This study presents an AI platform offering personalized art education for neurodivergent learners, particularly those with Autism Spectrum Disorder (ASD). It successfully improves learning outcomes and sensory comfort, addressing educational inequities in arts access.
Area of Science:
- Artificial Intelligence in Education
- Neurodiversity and Learning
- Arts Education Accessibility
Background:
- Educational inequity in arts learning disproportionately affects students with Autism Spectrum Disorder (ASD).
- Traditional pedagogies often fail to provide the structured, predictable environments necessary for ASD learners' aesthetic development and sensory regulation.
- Systematic barriers limit access to personalized art education for neurodivergent learners.
Purpose of the Study:
- To introduce an AI-powered e-learning platform designed to mitigate educational inequities in arts learning for neurodivergent students.
- To demonstrate how intelligent cross-modal integration can democratize access to personalized art education.
- To accommodate individual sensory processing patterns, cultural backgrounds, and neurodevelopmental profiles for ASD learning success.
Main Methods:
- Development of a DeepSeek-based AI system transforming visual art features into structured musical accompaniments.
- Utilization of enhanced ResNet-50 architecture, high-dimensional manifold mapping, and conditional generation models.
- Optimization for sensory regulation principles and personalized accommodation of neurodevelopmental profiles.
Main Results:
- Substantial improvements observed in sensory comfort (4.6/5) and learning satisfaction (4.3/5) among participants.
- Significant educational outcome gains: 20.5% NAEP score improvement versus 8.2% for traditional methods (p < 0.008).
- High technical performance with superior cross-modal consistency (MSE 0.05, PCC 0.92) and 89% accommodation success.
Conclusions:
- The AI-powered platform offers a promising model for inclusive digital education, mitigating inequities for neurodivergent populations.
- AI can effectively contribute to accessible arts education by embracing neurodiversity while maintaining academic rigor.
- The research provides a scalable framework for advancing personalized and equitable arts learning experiences.
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