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This study investigates how draft quality from AI systems impacts the editing workflow of novice audio describers, finding that high-quality drafts significantly reduce cognitive load and completion time compared to low-quality baselines. The research establishes a critical design principle for accessibility tools: effective AI assistance must meet a content-dependent quality threshold rather than merely providing an initial draft. These findings offer actionable insights for optimizing human-AI collaboration in specialized creative domains like media accessibility.
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How Do Novice Describers Edit AI Drafts? The Role of Draft Quality in Audio Description Authoring

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2605.05348v2 Announce Type: replace-cross 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.

Title:How Do Novice Describers Edit AI Drafts? The Role of Draft Quality in Audio Description Authoring

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.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2605.05348 [cs.HC]

(or arXiv:2605.05348v2 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2605.05348 Focus to learn more arXiv-issued DOI via DataCite

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