🤖 AI Summary
This study investigates the potential of agent-based web browsers (AWBs) powered by large language models to enhance web accessibility and interactive experiences for people with visual impairments. Through a case study involving a low-vision technology expert, the research pioneers the application of AWBs as assistive technology for this population, demonstrating their capacity to support natural language interaction and autonomous navigation. Findings indicate that, despite existing technical limitations, the user achieved a notably fluid and flexible browsing experience, substantially reducing barriers to web access. The results underscore the innovative value and promising applicability of agent-driven browsers in advancing accessible interaction and intelligent user experiences for individuals with visual disabilities.
📝 Abstract
Agentic Web Browsers (AWBs), powered by Large Language Models (LLMs), are emerging as autonomous systems capable of navigating the Web on behalf of users. Beyond enhancing productivity, they could also offer significant promise as Assistive Technologies (ATs) for visually-impaired individuals, transforming web interaction into a fluid conversational exchange. In this paper, we present a case study with a low-vision technology expert, examining how AWBs can support visually-impaired users in web navigation. The findings show that, despite the current limitations, the navigation experience is notably fluid and flexible, underscoring the strong potential of AWBs to enhance accessibility and reduce barriers in web interaction, with implications that may extend beyond accessibility to agentic UX more broadly.