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Exploring the application of LLM-based AI in UX design: an empirical case study of ChatGPT.

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Title: Exploring the application of LLM-based AI in UX design: an empirical case study of ChatGPT.
Authors: Zhou, Zhibin (AUTHOR), Li, Yaoqi (AUTHOR), Yu, Junnan (AUTHOR)
Source: Human-Computer Interaction. 2026, Vol. 41 Issue 5, p287-319. 33p.
Subjects: ChatGPT, Human-artificial intelligence interaction, Design services, Artificial intelligence, Language models
Abstract: Large language model (LLM) based AI applications are being rapidly adopted by various creativity-related sectors, including UX design. However, as newly emerged applications, how UX designers work with LLMs and how to optimize the benefits of LLMs in UX design remain unclear. Leveraging the widely adopted LLM application ChatGPT as a case study, we recruited 12 experienced frontline UX practitioners from IT companies to use ChatGPT in their everyday design activities for 4 to 6 weeks. Through use logs, questionnaires, and interviews, we collected participants' usage and reflection data. Our analysis reveals the different functions of ChatGPT in facilitating UX design, such as offering design guidelines, constructing user profiles, and simulating stakeholders. Challenges also emerged, such as limitations in understanding complex design problems and prototyping design ideas. Based on the findings, we reflect on the working dynamics between human workers and AI tools and suggest implications for designing more usable LLM-based AI applications. [ABSTRACT FROM AUTHOR]
Copyright of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Exploring the application of LLM-based AI in UX design: an empirical case study of ChatGPT.
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  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Zhibin%22">Zhou, Zhibin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yaoqi%22">Li, Yaoqi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Junnan%22">Yu, Junnan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Human-Computer+Interaction%22">Human-Computer Interaction</searchLink>. 2026, Vol. 41 Issue 5, p287-319. 33p.
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  Data: <searchLink fieldCode="DE" term="%22ChatGPT%22">ChatGPT</searchLink><br /><searchLink fieldCode="DE" term="%22Human-artificial+intelligence+interaction%22">Human-artificial intelligence interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Design+services%22">Design services</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink>
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  Label: Abstract
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  Data: Large language model (LLM) based AI applications are being rapidly adopted by various creativity-related sectors, including UX design. However, as newly emerged applications, how UX designers work with LLMs and how to optimize the benefits of LLMs in UX design remain unclear. Leveraging the widely adopted LLM application ChatGPT as a case study, we recruited 12 experienced frontline UX practitioners from IT companies to use ChatGPT in their everyday design activities for 4 to 6 weeks. Through use logs, questionnaires, and interviews, we collected participants' usage and reflection data. Our analysis reveals the different functions of ChatGPT in facilitating UX design, such as offering design guidelines, constructing user profiles, and simulating stakeholders. Challenges also emerged, such as limitations in understanding complex design problems and prototyping design ideas. Based on the findings, we reflect on the working dynamics between human workers and AI tools and suggest implications for designing more usable LLM-based AI applications. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/07370024.2024.2420991
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        Text: English
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        PageCount: 33
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        Type: general
      – SubjectFull: Human-artificial intelligence interaction
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      – SubjectFull: Design services
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              Text: 2026
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