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Cited by 81Usability and User Interface DesignInnovative Human-Technology InteractionHuman-Automation Interaction and Safety

Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization

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Fan, Mingming, Yang, Xianyou, Yu, TszTung, Liao, Q. Vera, Zhao, Jian·2022·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3512943
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Abstract

Analyzing usability test videos is arduous. Although recent research showed the promise of AI in assisting with such tasks, it remains largely unknown how AI should be designed to facilitate effective collaboration between user experience (UX) evaluators and AI. Inspired by the concepts of agency and work context in human and AI collaboration literature, we studied two corresponding design factors for AI-assisted UX evaluation: explanations and synchronization. Explanations allow AI to further inform humans how it identifies UX problems from a usability test session; synchronization refers to the two ways humans and AI collaborate: synchronously and asynchronously. We iteratively designed a tool-AI Assistant-with four versions of UIs corresponding to the two levels of explanations (with/without) and synchronization (sync/async). By adopting a hybrid wizard-of-oz approach to simulating an AI with reasonable performance, we conducted a mixed-method study with 24 UX evaluators identifying UX problems from usability test videos using AI Assistant. Our quantitative and qualitative results show that AI with explanations, regardless of being presented synchronously or asynchronously, provided better support for UX evaluators' analysis and was perceived more positively; when without explanations, synchronous AI better improved UX evaluators' performance and engagement compared to the asynchronous AI. Lastly, we present the design implications for AI-assisted UX evaluation and facilitating more effective human-AI collaboration.

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  • Fan et al. found that explanations foster trust and synchronization improves engagement in AI-assisted UX evaluation, but their Wizard-of-Oz setup left unclear how such systems would operate in practice.

    “Fan et al. [26] studied AI-assisted UX evaluation, fi nding that explanations foster trust and synchronization improves engagement, but their Wizard-of-Oz setup left it unclear how such systems would operate in practice.”

    From Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation (2026) · cites this paper as [26]

    The Conclusion reports only tentative evidence that explanations may improve trust; survey results on trust were not significant. Section 5.2.2 finds greater engagement with synchronous than asynchronous AI specifically when explanations were absent. Section 6.4 says the Wizard-of-Oz study leaves open how to implement an AI that generates explanations of comparable quality, rather than how such systems would operate in general. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Fan, M., Yang, X., Yu, T., Liao, Q. V., & Zhao, J.
Affiliations
  • The Hong Kong University of Science and Technology (Guangzhou) (CN)
  • Rochester Institute of Technology (US)
  • University of Waterloo (CA)
  • Microsoft Research Montréal (Canada) (CA)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2022-03-30
Volume
6
Issue
CSCW1
Pages
1-32
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3512943
OpenAlex
W4200633865
Cited by
81
References
22
Citations by year
2026: 28 · 2025: 25 · 2024: 17 · 2023: 8 · 2022: 3
FWCI
6.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Keywords
human-AI collaboration, AI explanations, agency, work context, user engagement

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