Existing assistive technologies (AT) often adopt a one-size-fits-all approach, overlooking the diverse needs of people with visual impairments (PVI). Do-it-yourself AT (DIY-AT) toolkits offer one path toward customization, but most remain limited—targeting co-design with engineers or requiring programming expertise. Non-professionals with disabilities, including PVI, also face barriers such as inaccessible tools, lack of confidence, and insufficient technical knowledge. These gaps highlight the need for prototyping technologies that enable PVI to directly make their own AT. Building on emerging evidence that large language models (LLMs) can serve not only as visual aids but also as co-design partners, we present an exploratory study of how LLM-based AI can support PVI in the tangible DIY-AT co-making process. Our findings surface key challenges and design opportunities: the need for greater spatial and visual support, strategies for mitigating novel AI errors, and implications for designing more accessible AI-assisted prototypes.

intent taxonomy with five visual intents characterized by participants' gaze behaviors.We demonstrated the difference between low vision and sighted participants' gaze behaviors and how visual ability affected low vision participants' gaze patterns across visual intents.Our findings underscore the importance of combining visual ability information, visual context, and eye tracking data in visual intent recognition, setting up a foundation for intent-aware assistive technologies for low vision people.

Reading is a challenging task for low vision people. While conventional low vision aids (e.g., magnification) offer certain support, they cannot fully address the difficulties faced by low vision users, such as locating the next line and distinguishing similar words. To fill this gap, we present GazePrompt, a gaze-aware reading aid that provides timely and targeted visual and audio augmentations based on users’ gaze behaviors. GazePrompt includes two key features: (1) a Line-Switching support that highlights the line a reader intends to read; and (2) a Difficult-Word support that magnifies or reads aloud a word that the reader hesitates with. Through a study with 13 low vision participants who performed well-controlled reading-aloud tasks with and without GazePrompt, we found that GazePrompt significantly reduced participants’ line switching time, reduced word recognition errors, and improved their subjective reading experiences. A follow-up silent-reading study showed that GazePrompt can enhance users’ concentration and perceived comprehension of the reading contents. We further derive design considerations for future gaze-based low vision aids.

Cooking is a vital yet challenging activity for blind and low vision (BLV) people, which involves many visual tasks that can be difficult and dangerous. BLV training services, such as vision rehabilitation, can effectively improve BLV people’s independence and quality of life in daily tasks, such as cooking. However, there is a lack of understanding on the practices employed by the training professionals and the barriers faced by BLV people in such training. To fill the gap, we interviewed six professionals to explore their training strategies and technology recommendations for BLV clients in cooking activities. Our findings revealed the fundamental principles, practices, and barriers in current BLV training services, identifying the gaps between training and reality.

While being able to read with screen magnifiers, low vision people have slow and unpleasant reading experiences. Eye tracking has the potential to improve their experience by recognizing fine-grained gaze behaviors and providing more targeted enhancements. To inspire gaze-based low vision technology, we investigate the suitable method to collect low vision users’ gaze data via commercial eye trackers and thoroughly explore their challenges in reading based on their gaze behaviors. With an improved calibration interface, we collected the gaze data of 20 low vision participants and 20 sighted controls who performed reading tasks on a computer screen; low vision participants were also asked to read with different screen magnifiers. We found that, with an accessible calibration interface and data collection method, commercial eye trackers can collect gaze data of comparable quality from low vision and sighted people. Our study identified low vision people’s unique gaze patterns during reading, building upon which, we propose design implications for gaze-based low vision technology.