Evidence map›Paper›PMID 42117046›Full record

ArticleExtended abstracts on Human factors in computing systems. CHI Conference2026

ViDscribe: Multimodal AI for Customizing Audio Description and Question Answering in Online Videos.

Maryam S Cheema, Pooyan Fazli, Sina Elahimanesh, Hasti Seifi

Abstract read
In one paragraph

Article in Extended abstracts on Human factors in computing systems. CHI Conference, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Maryam S CheemaArizona State University Tempe, Arizona, USA.ORCID 0009-0009-0556-8029
Pooyan FazliArizona State University Tempe, Arizona, USA.ORCID 0000-0002-2625-8216
Sina ElahimaneshSaarland University Saarbrücken, Saarland, Germany.ORCID 0000-0001-7251-6661
Hasti SeifiArizona State University Tempe, Arizona, USA.ORCID 0000-0001-6437-0463

Funding

From Human-Powered to Automated Video Description for Blind and Low Vision UsersR01EY034562 · NEI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI Pooyan Fazli · 2023 to 2026
$2.4M
NEI NIH HHS R01 EY034562
6 · The paper itself

Abstract

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

Indexed as

Audio DescriptionBlind and Low Vision UsersCustomizationLongitudinal StudyMultimodal Large Language ModelsVideo AccessibilityVisual Question Answering

Identifiers

PMID42117046
PMCPMC13157971

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.