Sharon Y Lin

Jazmin Collins, Sharon Y Lin, Tianqi Liu et al.

As social virtual reality (VR) grows more popular, addressing accessibility for blind and low vision (BLV) users is increasingly critical. Researchers have proposed an AI "sighted guide" to help users navigate VR and answer their questions, but it has not been studied with users. To address this gap, we developed a large language model (LLM)-powered guide and studied its use with 16 BLV participants in virtual environments with confederates posing as other users. We found that when alone, participants treated the guide as a tool, but treated it companionably around others, giving it nicknames, rationalizing its mistakes with its appearance, and encouraging confederate-guide interaction. Our work furthers understanding of guides as a versatile method for VR accessibility and presents design recommendations for future guides.

Ricardo E. Gonzalez Penuela, Crescentia Jung, Sharon Y Lin et al.

Multimodal large language models (MLLMs) are changing how Blind and Low Vision (BLV) people access visual information. Unlike traditional visual interpretation tools that only provide descriptions, MLLM-enabled applications offer conversational assistance, where users can ask questions to obtain goal-relevant details. However, evidence about their performance in the real-world and implications for BLV people's daily lives remains limited. To address this, we conducted a two-week diary study, where we captured 20 BLV participants' use of an MLLM-enabled visual interpretation application. Although participants rated the visual interpretations of the application as "trustworthy" (mean=3.76 out of 5, max=extremely trustworthy) and "somewhat satisfying" (mean=4.13 out of 5, max=very satisfying), the AI often produced incorrect answers (22.2%) or abstained (10.8%) from responding to users' requests. Our findings show that while MLLMs can improve visual interpretations' descriptive accuracy, supporting everyday use also depends on the "visual assistant" skill: behaviors for providing goal-directed, reliable assistance. We conclude by proposing the "visual assistant" skill and guidelines to help MLLM-enabled visual interpretation applications better support BLV people's access to visual information.