Video-based learning (VBL) has become a dominant method for learning practical skills, yet accessibility guidelines provide limited guidance for users with cognitive differences. In particular, challenges that individuals with Borderline Intellectual Functioning (BIF) encounter in video-based learning remain largely underexplored, despite VBL’s potential to support their learning through features like self-paced viewing and visual demonstration. To address this gap, we conducted a series of studies with BIF individuals and caretakers to comprehensively understand their VBL challenges. Our analysis revealed challenges stemming from misalignment between user cognitive characteristics and video elements (e.g., overwhelmed by pacing and density, difficulty inferring omitted content), and experiential factors intensifying challenges (e.g., low self-efficacy). While participants employed coping strategies such as repetitive viewing to address these challenges, these strategies could not overcome fundamental gaps with video. We further discuss the design implications on both content and UI-level features for BIF and broader groups with cognitive diversities.

Audio Description (AD) is a narration designed to enhance accessibility for visually impaired individuals by conveying the key visual elements of a video. Thus, automating AD generation for long-form videos, such as movies and dramas, provides high social value but is a challenging task. First, AD must reflect the narrative context of the entire movie, including the storyline, names of characters and places, and the cultural setting. Second, to avoid disrupting the immersive experience of the movie, AD must not overlap with the characters' dialogues, requiring the delivery of numerous visual elements in concise sentences. This paper presents NarrAD, a training-free AD generation framework that satisfies both of the requirements by leveraging rich narrative context in movie scripts and curating information across narration slots. Experiments on the MAD dataset demonstrate that our approach outperforms prior works in both captioning and LLM-based metrics. In the user study with 600 subjects, NarrAD achieves the highest user experience and movie comprehension. NarrAD's AD samples are available at https://bit.ly/4aSwOTr.