Drawing Dialogues Between Generative AI and Children with Autism: A Qualitative Study on the Externalization of “Understanding”

Abstract

Although individuals with autism may face challenges in social cognition, this does not imply a lack of capacity for making meaning or understanding emotions. Instead, it reflects a paradigmatic difference in the pathways of understanding, wherein their unique forms of expression diverge from the normative “modes of understanding” assumed by mainstream society (Grandin, 2006; Gilberts, 2006). Expressions situated within the “individual–local” quadrant can still embody a deep sense of structure and systemic meaning (Mottron et al., 2006). Therefore, it is essential to expand the definition of “understanding” and to construct a more inclusive and structurally responsive conceptual model.This study seeks to explore, through a qualitative lens, a central question: Can interaction with generative artificial intelligence (AI) in the context of drawing help make autistic children’s understanding more visible and amplified? It proposes that the understanding capacities of children with autism may be redefined through such mediated engagement. The primary aim is to uncover the types of responses, misalignments, misunderstandings, or unexpected outcomes that arise in generative AI systems during interactive creation.This study focuses on the role of generative AI in the drawing processes of children with autism, exploring its impact on the externalization of “understanding.” Through an interdisciplinary literature review, the research identifies three key dimensions of understanding: organizational constructiveness, social embeddedness, and sensory processing. Based on these dimensions, a comparative experiment was designed and conducted, contrasting traditional pen-and-paper drawing with interactive drawing involving generative AI.Multimodal data were collected from 15 autistic children, their teachers, and researchers. Drawing on teacher interviews and observer ratings, the study analyzes the modes of understanding exhibited by the participants and how these evolve during the drawing process. Findings suggest that generative AI can stimulate children's creative potential through low-intervention support, mediating guidance, or co-creative feedback. In specific contexts, it effectively enhances cognitive structures and expressive intent, allowing previously implicit forms of understanding to be visually externalized.The study also reveals two key paradoxes: first, the deeper a child’s understanding of a subject, the richer their mental imagery and narrative details, yet this increased cognitive load in selection and organization may lead to slower and less direct expression. Second, while AI assistance can help concretize vague ideas, it may also obscure children's unique non-verbal and non-visual modes of perception, resulting in overly uniform external representations.The theoretical significance of this study lies in proposing a multidimensional and paradigm-shifting model of “understanding” within the context of generative AI–autistic child interaction. Practically, it offers insights for educational interventions aimed at unlocking the creative potential of children with autism, and informs future design strategies for generative AI drawing tools tailored to special-needs users, calling for a more inclusive, diverse, and interactionally grounded understanding.

Keywords: generative AI, autistic children, dimensions of understanding, cognitive externalization

DOI: 10.54941/ahfe1006819