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

2026-01-26

AI Summary

This work addresses the significant accessibility barriers faced by screen reader users due to the poor adaptation of mainstream graphical user interfaces, which impose steep learning curves and operational challenges for people with visual impairments. To overcome these limitations, the authors propose AskEase—the first on-demand AI assistant that integrates large language models with multimodal context awareness. By dynamically fusing on-screen content, interaction history, and user task goals, AskEase infers user intent in real time and delivers low-interruption, step-by-step contextual voice guidance. In a controlled study with 12 blind and visually impaired participants, AskEase significantly improved task success rates while markedly reducing physical demand, mental effort, and frustration, thereby demonstrating its effectiveness and innovation in empowering visually impaired users to independently accomplish computer-based tasks.

Technology Category

• Humans and AI: AI for Accessibility
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• screen reader users
• accessible computing
• visual impairments
• digital accessibility
• user guidance

Innovation

• context-aware assistance
• screen reader accessibility
• LLM-powered AI assistant
• on-demand guidance
• accessible computing