Creating a Dataset of Autistic Individuals to Recommend Evidence-Based Practices through Generative Artificial Intelligence

Abstract

Autism is the fastest-growing developmental condition, and as a spectrum disorder, individuals vary in severity in communication and behavior. The rapid development of artificial intelligence (AI) and machine learning (ML) presents opportunities to leverage such technology to support autistic individuals in multiple contexts, including education, healthcare, and disability research. However, very limited research has focused on developing AI or ML-based solutions for autistic individuals largely due to the scarcity of autism-related datasets. In order for autistic people to equally benefit from the breakthroughs in AI, the data representation gap in AI and ML must be addressed. The project proposes an innovative human expert and AI combined approach to generate synthetic data about autistic individuals. Through this approach, we are developing the first dataset of case studies of autistic individuals to effectively identify evidence-based practices (EBPs) to address the unique needs of autistic individuals. The project improves the representation and inclusion of autistic individuals in the rapidly evolving field of ML and AI through both methodological innovations (i.e., the expert/AI combined data generation approach) and practical contributions (i.e., the EBP dataset that will be made publicly accessible). The innovative data generation method and supporting materials developed in this project can be generalized to individuals with other disabilities.

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