From Struggle to Success: Context-Aware Guidance for Screen Reader Users in Computer Use

TL;DR

This paper presents AskEase, an AI-powered on-demand assistant that provides context-aware, step-by-step guidance to screen reader users, significantly improving task success and reducing workload in accessible computing.

Contribution

It introduces AskEase, a novel AI assistant that infers user intent from multiple contexts to deliver tailored guidance for visually impaired users, enhancing independence.

Findings

AskEase improved task success rates in user studies.

Participants reported reduced effort and frustration.

The system demonstrated robustness across scenarios.

Abstract

Equal access to digital technologies is critical for education, employment, and social participation. However, mainstream interfaces are visually oriented, creating steep learning curves and frequent obstacles for screen reader users, and limiting their independence and opportunities. Existing support is inadequate -- tutorials mainly target sighted users, while human assistance lacks real-time availability. We introduce AskEase, an on-demand AI assistant that provides step-by-step, screen reader user-friendly guidance for computer use. AskEase manages multiple sources of context to infer user intent and deliver precise, situation-specific guidance. Its seamless interaction design minimizes disruption and reduces the effort of seeking help. We demonstrated its effectiveness through representative usage scenarios and robustness tests. In a within-subjects study with 12 screen reader users, AskEase significantly improved task success while reducing perceived workload, including physical demand, effort, and frustration. These results demonstrate the potential of LLM-powered assistants to promote accessible computing and expand opportunities for users with visual impairments.