Wei Du, Xi-Yuan Peng, Wei Li, Ur-Rehman Attiq, Xiao-Min Li, Rui Feng, Kang Zhong, Xin-Rui Lu, Hong-Lin Chen
A total of 72 relevant studies were identified. AI applications spanned five key areas: "Mobility and Navigation Assistance" (n = 20, 41.7%), "Communication and Information Accessibility" (n = 14, 29.2%), "Smart Assistance for Daily Living" (n = 7, 14.6%), "Education and Vocational Empowerment" (n = 5, 8.3%), and "Mental Health Support and Social Inclusion" (n = 3, 6.3%). Domains addressing psychosocial participation were represented by very few studies, limiting the generalizability of findings. Critical challenges included ethical concerns regarding data privacy (58.3%), inadequate training datasets (25.0%), high implementation costs (25.0%), algorithmic biases (20.8%), and limited real-world evidence of benefits (20.8%).
PURPOSE/OBJECTIVES: An increasing number of studies have verified the application potential of artificial intelligence (AI) in advancing rights protection and social inclusion for persons with disabilities. This study maps AI applications in social support for persons with disabilities through the lens of participation, autonomy, and environmental fit and identifies structural patterns, evidence gaps, and implications for the field. Research Methods/Design: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, a systematic scoping review was conducted in PubMed and Web of Science to comprehensively evaluate the current research landscape within this emerging domain.
RESULTS: A total of 72 relevant studies were identified. AI applications spanned five key areas: "Mobility and Navigation Assistance" (n = 20, 41.7%), "Communication and Information Accessibility" (n = 14, 29.2%), "Smart Assistance for Daily Living" (n = 7, 14.6%), "Education and Vocational Empowerment" (n = 5, 8.3%), and "Mental Health Support and Social Inclusion" (n = 3, 6.3%). Domains addressing psychosocial participation were represented by very few studies, limiting the generalizability of findings. Critical challenges included ethical concerns regarding data privacy (58.3%), inadequate training datasets (25.0%), high implementation costs (25.0%), algorithmic biases (20.8%), and limited real-world evidence of benefits (20.8%).
CONCLUSION/IMPLICATIONS: The review reveals a structural imbalance favoring functional over psychosocial support, a predominantly medical-model orientation in AI development, and systematic underrepresentation of persons with intellectual and psychosocial disabilities. These findings underscore the need for psychosocial applications, social-model approaches, real-world validation, and inclusive codesign. (PsycInfo Database Record (c) 2026 APA, all rights reserved).