Recent Advances in AI for Inclusive Web Design: A Performance-Optimized Framework for Real-Time Accessibility Adaptations for Neurodivergent Users

Abstract

Because of problems including erratic interfaces, sensory overload, and inconsistent layouts, neurodivergent consumers encounter particular difficulties when utilizing digital platforms. Although there are some partial solutions provided by current AI-powered accessibility technologies, many of them have delay, processing overhead, and little customisation. This study suggests an AI-powered framework that is performance-optimized and designed for real-time web accessibility adjustments. The framework maintains high responsiveness while enabling dynamic personalization through the use of edge computing, adaptive learning, and modular design. The findings indicate a 30% rise in user happiness, a 25% improvement in personalization accuracy, and a 40% decrease in latency. By offering a scalable, user-centric approach to web accessibility, this article advances AI and digital inclusion theory, policy, and practice.