Large multimodal models (LMMs) have enabled new AI-powered applications that help people with visual impairments (PVI) receive natural language descriptions of their surroundings through audible text. We investigated how this emerging paradigm of visual assistance transforms how PVI perform and manage their daily tasks. Moving beyond basic usability assessments, we examined both the capabilities and limitations of LMM-based tools in personal and social contexts, while exploring design implications for their future development. Through interviews with 14 visually impaired users and analysis of image descriptions from both participants and social media using Be My AI (an LMM-based application), we identified two key limitations. First, these systems’ context awareness suffers from hallucinations and misinterpretations of social contexts, styles, and human identities. Second, their intent-oriented capabilities often fail to grasp and act on users’ intentions. Based on these findings, we propose design strategies for improving both human-AI and AI-AI interactions, contributing to the development of more effective, interactive, and personalized assistive technologies.

Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI. Unlike existing applications such as Be My AI and Seeing AI, NaviGPT combines image recognition and contextual navigation guidance into a single system, offering continuous feedback on the user’s surroundings without the need for app-switching. Meanwhile, NaviGPT compensates for the response delays of LLM by using location and sensor data, aiming to provide practical and efficient navigation support for PVI in dynamic environments.

Remote sighted assistance (RSA) has emerged as a conversational technology aiding people with visual impairments (VI) through real-time video chat communication with sighted agents. We conducted a literature review and interviewed 12 RSA users to understand the technical and navigational challenges faced by both agents and users. The technical challenges were categorized into four groups: agents’ difficulties in orienting and localizing users, acquiring and interpreting users’ surroundings and obstacles, delivering information specific to user situations, and coping with poor network connections. We also presented 15 real-world navigational challenges, including 8 outdoor and 7 indoor scenarios. Given the spatial and visual nature of these challenges, we identified relevant computer vision problems that could potentially provide solutions. We then formulated 10 emerging problems that neither human agents nor computer vision can fully address alone. For each emerging problem, we discussed solutions grounded in human–AI collaboration. Additionally, with the advent of large language models (LLMs), we outlined how RSA can integrate with LLMs within a human–AI collaborative framework, envisioning the future of visual prosthetics.

Remote Sighted Assistance (RSA) is a popular smartphone-mediated aid for people with blindness, where a sighted individual converses with a blind individual in a one-on-one (1:1) session. Since sighted assistants outnumber blind individuals (13:1), this paper investigates what happens when more than one sighted individual assists a single blind individual in a session. Specifically, we propose paired-volunteer RSA, a new paradigm where two sighted volunteers assist a single user with blindness. We investigate the feasibility, desirability, and challenges of this paradigm and explore its opportunities. Our study with 8 sighted volunteers and 9 blind users reveals that the proposed paradigm extends the one-on-one RSA to cover a broader range of more intellectual and experiential tasks, providing new and distinctive opportunities in supporting complex, open-ended tasks (e.g., pursuing hobbies, appreciating arts, and seeking entertainment). These opportunities can not only enrich the blind users’ quality of life and independence but also offer a fun and engaging experience for the sighted volunteers. The study also reveals the costs of extended collaboration in this paradigm. Finally, we synthesize a taxonomy of tasks where the proposed RSA paradigm can succeed and outline how HCI researchers and system designers can realize this paradigm.

Remote sighted assistance (RSA) offers prosthetic support to people with visual impairments (PVI) through image- or video-based conversations with remote sighted assistants. While useful, RSA services introduce privacy concerns, as PVI may reveal private visual content inadvertently. Solutions have emerged to address these concerns on image-based asynchronous RSA, but exploration into solutions for video-based synchronous RSA remains limited. In this study, we developed BubbleCam, a high-fidelity prototype allowing PVI to conceal objects beyond a certain distance during RSA, granting them privacy control. Through an exploratory field study with 24 participants, we found that 22 appreciated the privacy enhancements offered by BubbleCam. The users gained autonomy, reducing embarrassment by concealing private items, messy areas, or bystanders, while assistants could avoid irrelevant content. Importantly, BubbleCam maintained RSA’s primary function without compromising privacy. Our study highlighted a cooperative approach to privacy preservation, transitioning the traditionally individual task of maintaining privacy into an interactive, engaging privacy preserving experience.