Navigating Accessibility and Inclusivity: Perceptions and Usability of Artificial Intelligence-Enabled Job Application Systems for Persons With Disabilities

Abstract

Artificial intelligence (AI)-enabled job application systems are transforming the hiring process by automating candidate screening and improving efficiency. However, not much research is available on how these systems are perceived by Persons with Disabilities (PwDs). This study has explored the use-intention of AI-enabled job application systems among PwDs using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework with key predictors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Perceived Inclusivity (PI) and Self-efficacy (SE). Data were obtained from a web-based survey with 303 PwDs. Hierarchical regression analysis was applied for data analysis and hypothesis testing. The results showed that EE and SI were significant and positively associated with behavioural intention, while PE, PI and SE had no significant association, thus highlighting the need for greater personalisation, adaptive features and human intervention in AI-enabled job application systems to ensure fair and accessible hiring experiences for PwDs.