Empowering Differently-Abled Individuals with AI: Advancing Accessibility, Mobility, and Inclusion

Structured Abstract Purpose This study examines the role of AI-driven assistive technologies in enhancing accessibility, mobility, education, and employment for differently-abled individuals. It evaluates their effectiveness, adoption barriers, and policy implications while addressing challenges such as affordability, algorithmic bias, and infrastructure limitations. By integrating empirical research, case studies, and policy analysis, the study aims to bridge the gap between technological advancements and real-world accessibility solutions. Approach A mixed-methods research design was employed, incorporating a systematic literature review, statistical analysis, case study evaluations, expert interviews, and comparative policy assessments. Data were sourced from global disability organizations, AI research institutions, and government agencies to measure AI accuracy, adoption rates, and socio-economic impact. Case studies examined real-world applications of AI-driven speech recognition, smart prosthetics, adaptive learning platforms, and workplace accessibility solutions, while expert interviews provided insights into ethical considerations and policy challenges. Findings The study confirms that AI-driven assistive technologies significantly improve accessibility outcomes. AI-powered speech recognition tools achieve over 95% accuracy for widely spoken languages, while AI-enabled sign language recognition and mobility aids enhance real-time communication and independence. AI-integrated prosthetics improve dexterity by 35%, and AI-driven job-matching platforms contribute to a 22% rise in employment rates. However, affordability remains a major challenge, with 60% of potential users unable to access AI solutions due to high costs. Algorithmic bias affects hiring and speech recognition accuracy, while infrastructure gaps limit AI adoption in low-income regions. Countries with structured AI accessibility policies demonstrate up to 40% higher adoption rates compared to those without regulatory frameworks. Research Limitations/Implications The study relies on secondary data, which may be subject to regional disparities in AI adoption and accessibility reporting. Future research should focus on longitudinal studies to assess the long-term impact of AI-driven assistive technologies. Findings underscore the need for targeted policy interventions, including increased government funding, ethical AI regulations, and inclusive AI development practices. Practical Implications This research provides actionable recommendations for policymakers, AI developers, and disability advocacy organizations to improve AI accessibility. Expanding funding programs, developing open-source AI solutions, and enforcing standardized ethical guidelines can mitigate affordability barriers and ensure AI technologies are more inclusive. Originality/Value Unlike previous studies that focus solely on AI's technical capabilities, this research integrates empirical data, real-world case studies, and policy evaluations to provide a holistic perspective on AI-driven assistive technologies. By addressing effectiveness, adoption barriers, and regulatory challenges, this study contributes valuable insights for advancing accessibility, equity, and inclusion in AI-driven solutions.