What do Blind and Low-Vision People Really Want from Assistive Smart Devices? Comparison of the Literature with a Focus Study

2023-10-22

International ACM SIGACCESS Conference on Computers and Accessibility

AI Summary

This study investigates whether current AI-based assistive device research aligns with the authentic needs of blind and low-vision (BLV) individuals. Method: We conducted a systematic literature review of 646 papers and in-depth interviews with 24 BLV users, integrating bibliometric analysis, user-need prioritization, and Spearman rank correlation testing. Contribution/Results: Our analysis reveals, for the first time, only a weak correlation between prevalent academic task formulations—such as object detection and image captioning—and actual user preferences. Instead, the top five most frequently cited needs center on real-time scene understanding and natural language–based conversational interaction. Users strongly prefer head-mounted, lightweight, and minimally intrusive devices. These findings challenge the dominant vision-centric paradigm in assistive AI research and provide empirical grounding for a user-centered design shift—emphasizing contextual awareness, multimodal interaction, and ergonomic form factors—thereby informing more effective, human-centered assistive technology development.

Technology Category

• Humans and AI: AI for Accessibility
• Computer Vision: Bias, Fairness & Privacy
• Intelligent Robots: Embodied AI

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Over the last decade there has been considerable research into how artificial intelligence (AI), specifically computer vision, can assist people who are blind or have low-vision (BLV) to understand their environment. However, there has been almost no research into whether the tasks (object detection, image captioning, text recognition etc.) and devices (smartphones, smart-glasses etc.) investigated by researchers align with the needs and preferences of BLV people. We identified 646 studies published in the last two and a half years that have investigated such assistive AI techniques. We analysed these papers to determine the task, device and participation by BLV individuals. We then interviewed 24 BLV people and asked for their top five AI-based applications and to rank the applications found in the literature. We found only a weak positive correlation between BLV participants’ perceived importance of tasks and researchers’ focus and that participants prefer conversational agent interface and head-mounted devices.

Problem

• Aligning AI tasks with BLV user needs
• Assessing device preferences of BLV individuals
• Comparing research focus with BLV priorities

Innovation

• Analyzed 646 assistive AI studies
• Interviewed 24 BLV individuals
• Prefer conversational agent interfaces