AI-DRIVEN MULTIMODAL COGNITIVE SUPPORT: ADVANCING DIGITAL ACCESSIBILITY THROUGH ADAPTIVE TECHNOLOGIES

Abstract

This article comprehensively analyzes artificial intelligence applications in cognitive support technologies, focusing on three key modalities: voice interaction, color optimization, and screen size adaptation. This article explores how machine learning algorithms can enhance accessibility by creating responsive, user-centric digital environments that adapt to individual cognitive and sensory needs. This article examines the integration of natural language processing for intuitive voice interactions, dynamic color adjustment systems for visual accessibility, and intelligent screen layout optimization to reduce cognitive load. Through a detailed investigation of existing frameworks and emerging technologies, it demonstrates how AI-driven systems can process real-time user feedback to create personalized digital experiences. This article suggests that the combination of these adaptive technologies significantly improves digital accessibility and user engagement while addressing various cognitive and sensory challenges. This article contributes to the growing body of research on inclusive design and cognitive support technologies, offering insights into the development of more accessible digital interfaces that accommodate diverse user needs. The implications of this research extend beyond traditional accessibility solutions, suggesting a paradigm shift towards more intuitive, adaptive, and personalized digital experiences for all users.