<i>MultiUX</i> : A Human-AI Collaborative Tool to Facilitate Multiple Usability Test Video Analyses

Analyzing multiple usability videos is essential for identifying common problems and prioritizing them. While existing tools assist in identifying problems within single videos, the manual process of comparing and categorizing them remains time-consuming. Inspired by prior work that suggested a human-AI collaborative approach could improve the efficiency and completeness of analyzing single videos compared to either humans or AI alone, we extended this approach to investigate its effectiveness in analyzing multiple videos. We designed MultiUX to assist in strategically analyzing multiple videos by comparing and categorizing problems across them, and optimizing the analysis sequence through video recommendations. MultiUX was compared to a baseline in a between-subjects study involving 20 UX evaluators. Results showed that MultiUX supported analyzing more videos and identifying more common problems that were encountered by more users in a given time. Additionally, MultiUX guided participants to analyze multiple videos more strategically.

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Publication Details

Journal
International Journal of Human-Computer Interaction
Published
2026-01-02
DOI
https://doi.org/10.1080/10447318.2025.2605526
Primary Topic
Usability and User Interface Design
Type
article
Field-Weighted Citation Impact
0.00
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article

MultiUX : A Human-AI Collaborative Tool to Facilitate Multiple Usability Test Video Analyses

Pan Hui, Luyao Shen, Shixu Zhou, Emily Kuang et al.
International Journal of Human-Computer Interaction
Usability and User Interface Design
article

MultiUX : A Human-AI Collaborative Tool to Facilitate Multiple Usability Test Video Analyses

Pan Hui, Luyao Shen, Shixu Zhou, Emily Kuang, Mingming Fan, Qing Shi, Linjie Qiu
article en

Abstract

Analyzing multiple usability videos is essential for identifying common problems and prioritizing them. While existing tools assist in identifying problems within single videos, the manual process of comparing and categorizing them remains time-consuming. Inspired by prior work that suggested a human-AI collaborative approach could improve the efficiency and completeness of analyzing single videos compared to either humans or AI alone, we extended this approach to investigate its effectiveness in analyzing multiple videos. We designed MultiUX to assist in strategically analyzing multiple videos by comparing and categorizing problems across them, and optimizing the analysis sequence through video recommendations. MultiUX was compared to a baseline in a between-subjects study involving 20 UX evaluators. Results showed that MultiUX supported analyzing more videos and identifying more common problems that were encountered by more users in a given time. Additionally, MultiUX guided participants to analyze multiple videos more strategically.

International Journal of Human-Computer Interaction
York University (CA), Hong Kong University of Science and Technology (HK), University of Hong Kong (HK)
Openalex Percentile: Top 98%
Usability and User Interface Design
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<i>MultiUX</i> : A Human-AI Collaborative Tool to Facilitate Multiple Usability Test Video Analyses — Pan Hui, Luyao Shen, et al. · International Journal of Human-Computer Interaction (2026) | TGRS Research Map | TGRS