In contemporary education, ensuring classroom accessibility is fundamental to fostering equitable learning opportunities for all students. Despite advancements in inclusive education, students with disabilities continue to encounter substantial barriers that hinder their academic progress. According to the Centers for Disease Control and Prevention (CDC), 24.8% of U.S. adults have a disability, with 12% of graduate students reporting similar challenges [1]. In 2022, approximately 15% of college students disclosed having ADD or ADHD; however, a significant proportion (15-43%) of students who disclose disabilities do not receive necessary accommodations [1][2][3][4]. These statistics underscore the urgent need for a systematic approach to assessing and improving classroom accessibility. To address this challenge, researchers developed a mobile application to audit classroom accessibility. The application incorporates structured questionnaires that evaluate physical classroom accessibility, generating a quantitative accessibility score based on user responses. This score provides data-driven insights into the inclusivity of classroom environments, enabling educational institutions to identify and address accessibility barriers. The application, compatible with both iOS and Android platforms, facilitates real-time feedback collection from students, educators, and staff. This study involves the collection and analysis of real-world physical classroom accessibility data at a Midwestern university using our developed mobile application. Analysis of the accessibility scores reveals significant trends and identifies key areas requiring improvement. The findings indicate that accessible and user-friendly evaluation tools can effectively identify and address physical barriers in classroom environments. Future developments will be directed at incorporating Explainable Artificial Intelligence (XAI) to generate comprehensive reports, providing deeper insights into accessibility challenges for a diverse array of individuals with impairments. This research highlights the potential of technology-driven approaches in capturing firsthand experiences and facilitating substantial improvements in classroom accessibility, ultimately fostering a more inclusive learning environment.

Rehabilitation and disability researchers are increasingly considering utlizing machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) as they interact with their environments. PwD often experience environmental barriers in the community and private buildings due to a lack of accessible infrastructure design and prior accessibility information. Such environmental barriers may inhibit PwD from full participation and impede in one’s overall independence and quality of life. The availability of healthcare services and information are essential for increased participation in occupations and optimal independence. In the current era of connected health, information has the ability to be accessed anywhere and providing accessibility content for PwD to use can enhance occupational performance factors. The purpose of this study was to 1) identify existing accessibility measurement challenges and barriers and 2) propose an intelligent accessibility evaluation and assessment for buildings, and 3) leverage mobile applications to address major challenges of accessibility measurement. This research aims to improve the accuracy and reliability of the accessibility measurement using a smarter system.