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

arXiv: https://arxiv.org/abs/2601.18092 · HTML: https://mlsanity.com/paper/2601.18092/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2026.

## Built on by (most cited first, 2 of 2)

- [Navigation Alone Is Not Enough: Evaluating Explanatory Assistive UI Agents](https://mlsanity.com/paper/2608.09944/) (2026)
- [Are We There Yet? Assessing Computer-Use Agents for Blind Users' Accessible Interaction with Desktop Applications](https://mlsanity.com/paper/2609.00524/) (2026)

## Abstract (arXiv)

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.