Advances in multimodal large language models enable automatic video narration and question answering (VQA), offering scalable alternatives to labor-intensive, human-authored audio descriptions (ADs) for blind and low vision (BLV) viewers. However, prior AI-driven AD systems rarely adapt to the diverse needs and preferences of BLV individuals across videos and are typically evaluated in controlled, single-session settings. We present ViDscribe, a web-based platform that integrates AI-generated ADs with six types of user customizations and a conversational VQA interface for YouTube videos. Through a longitudinal, in-the-wild study with eight BLV participants, we examine how users engage with customization and VQA features over time. Our results show sustained engagement with both features and that customized ADs improve effectiveness, enjoyment, and immersion compared to default ADs, highlighting the value of personalized, interactive video access for BLV users.

翻译：多模态大语言模型的进展使得自动视频叙述与问答成为可能，为盲人及低视力群体提供了可扩展的替代方案，以取代劳动密集型的人工音频描述。然而，现有的人工智能音频描述系统很少能适应盲人及低视力群体在不同视频中的多样化需求和偏好，且通常仅在受控的单次会话环境中进行评估。本文介绍了ViDscribe，这是一个基于网络的平台，集成了人工智能生成的音频描述（提供六类用户定制选项）以及面向YouTube视频的对话式问答界面。通过对八位盲人及低视力参与者进行的长期真实场景研究，我们探究了用户如何随时间推移使用定制功能与问答功能。研究结果表明，用户对这两项功能均保持持续使用；与默认音频描述相比，定制化音频描述在效果、愉悦度和沉浸感方面均有提升，这凸显了为盲人及低视力用户提供个性化、交互式视频访问的重要价值。