Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning

High accuracy with transparency: The framework achieved 99.65% test precision while using Grad-CAM visualizations to show why the AI made its decisions, making it possible for educators and parents to understand which handwriting features the system identified as indicators of dyslexia rather than treating it as a "black box."

Potential for early detection and intervention: By reliably identifying dyslexia through handwriting analysis, this tool could enable earlier diagnosis, allowing students to receive targeted educational support sooner rather than waiting for traditional assessment methods.

Global applicability across languages: The framework's adaptability to different languages and writing systems suggests it could be deployed across diverse populations and educational contexts worldwide, rather than being limited to English-speaking regions.

Building trust through explainability: The emphasis on transparency and interpretability helps build stakeholder confidence—teachers, parents, and students are more likely to trust and accept an AI diagnostic tool when they can see the specific handwriting patterns it's analyzing rather than relying on unexplained predictions.

Supporting personalized education: By identifying specific handwriting features associated with dyslexia, the framework enables educators to design more targeted, personalized learning strategies rather than applying one-size-fits-all interventions.