AI-Driven Approaches to Improve Accessibility Testing Across IoT Devices

As IoT-enabled devices and smart technologies become increasingly integrated into daily life, ensuring accessibility for all users, including those with disabilities, is critical. Traditional accessibility testing methods, such as manual testing and automated tools, have scope, accuracy, and scalability limitations, especially for dynamic and context-aware IoT environments. This research paper proposes a novel AI-driven approach to automated accessibility testing that leverages machine learning, Natural Language Processing (NLP), and computer vision techniques to identify and predict complex accessibility issues in real time for IoT devices. By learning from real user interactions and continuously updating its knowledge base, this AI-powered system can provide more context-aware and comprehensive accessibility assessments for connected devices, enhancing user usability. The proposed approach aims to enhance accessibility testing by improving the detection of context-specific issues, reducing dependency on manual testing, and promoting more inclusive IoT environments.