arXiv cs.AI· Lana Do, Shasta Ihorn, Charity M. Pitcher-Cooper, Sanjay Mirani, Gio Jung, Hyunjoo Shim, Zhenzhen Qin, Kien T. Nguyen, Vassilis Athitsos, Ilmi Yoon·· 3 天前AI 评分26
新手如何编辑 AI 草稿？草稿质量在音频描述创作中的作用
How Do Novice Describers Edit AI Drafts? The Role of Draft Quality in Audio Description Authoring
AI 导读
研究通过 GenAD 音频描述生成管线和 RefineAD 编辑界面，比较新手从零创作与编辑不同质量 AI 草稿的差异。GenAD 草稿将完成时间缩短一半以上并显著降低认知负荷，而简单无引导提示生成的基线草稿收益有限，表明有效辅助存在最低质量阈值。该阈值随内容视觉复杂度上升而提高，作者据此提出设计原则：AI 辅助应达到与目标内容匹配的质量阈值，而非仅仅存在。
正文 · 原文
Abstract:Audio description (AD) narrates visual elements in video for blind and low-vision audiences. Recent work has shown that giving novice describers an AI-generated draft to start from helps produce higher-quality AD and lowers the barrier to entry. What remains an open question is how draft quality shapes the editing process. We investigate this through GenAD, an AD generation pipeline that incorporates accessibility guidelines and contextual video information, and RefineAD, an editing interface for human revisions. Human-AI contributions are measured across text, timing, and delivery. In a within-subjects study, we compared authoring from scratch against editing AI drafts of varying quality. GenAD drafts cut completion time by more than half and significantly reduced cognitive load. In contrast, baseline drafts generated from simple, unguided prompts offered only modest benefits, pointing to a minimum quality threshold for effectiveness. Qualitative findings suggest this threshold is content-dependent; as visual complexity increases, so does the quality needed from AI drafts. We propose this as a design principle: effective AI assistance should clear a quality threshold suited to the target content, rather than simply be present.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.05348 [cs.HC] |
| (or arXiv:2605.05348v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05348 arXiv-issued DOI via DataCite |
Submission history
From: Lana Do [view email]
[v1]
Wed, 6 May 2026 18:18:15 UTC (13,293 KB)
[v2]
Mon, 12 Oct 2026 02:05:02 UTC (12,648 KB)
来源：arXiv cs.AI · arxiv.org