Designing AI-Enhanced Interventions for Dysgraphia: An Instructional Framework Integrating Visual, Motor, and Cognitive Scaffolds

Dysgraphia is a specific learning disability that affects handwriting, written expression, and the cognitive organisation of written tasks. Students with Dysgraphia often face overlapping challenges in visual perception, fine motor coordination, and cognitive load management—factors that are frequently overlooked in general education settings, particularly for undiagnosed learners. This paper presents a conceptual and instructional design–driven framework for developing an AI-based intervention tool that provides personalised support for students with Dysgraphia. The framework integrates three interdependent domains—
visual, motor, and cognitive—guided by Gestalt Theory, Motor Learning Theory, and Scaffolding Theory. These domains are visualised as a triangular model to reflect their instructional interplay and mutual reinforcement. The proposed AI tool will incorporate adaptive features such as visual tracing, stroke sequencing, and scaffolded writing prompts, with support tailored to varying severity levels of Dysgraphia. While empirical testing is planned in a future phase of this research, this paper outlines the theoretical foundation, design principles, and inclusive pedagogy informing the intervention. By grounding artificial intelligence 
in evidence-based instructional strategies, this work contributes to the design of equitable, technology-enhanced learning tools for special education. Feedback from this conference will guide the next stage of prototype development and qualitative inquiry.