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The Impact of Task Difficulty, Environmental Color Complexity, and Teaching Models on AR-Assisted Subway Maintenance Training.

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Title: The Impact of Task Difficulty, Environmental Color Complexity, and Teaching Models on AR-Assisted Subway Maintenance Training.
Authors: Dandan, Du (AUTHOR), Kaidi, Yu (AUTHOR), Dongyu, Yu (AUTHOR), Jinyi, Zhi (AUTHOR), Yun, Wang (AUTHOR), Jing, Chunhui (AUTHOR)
Source: International Journal of Human-Computer Interaction. Sep2025, Vol. 41 Issue 18, p11459-11472. 14p.
Subjects: Cognitive load, Task performance, Color in nature, User-centered system design, Railroad maintenance & repair, Instructional systems
Abstract: This paper investigates whether different task difficulties, color complexity, and choice of instructional application mode affect cognitive load, usability, visual discomfort, and job performance in subway overhaul training tasks and compares the findings under AR learning modes with those under traditional paper-based learning manuals. This study had sixteen participants and used eight experimental conditions. Tests were completed for environmental color complexity, task difficulty, and instructional application mode. The experimental variables were analyzed using three-way analysis of variance. The results showed that the color complexity of the carriage environment did not affect the participants' cognitive load and performance, but the use of AR glasses in subway maintenance was influenced by task difficulty and instructional application mode. For example, the AR image format reduced task completion time by an average of 25.9% for easy tasks, while the AR real-time format reduced head deflections by an average of 83.9%, suggesting that there is reliable potential for applying AR to subway overhaul training. This paper can provide a theoretical basis for developing training strategies for the different environments and tasks difficulties of subway maintenance. HIGHLIGHTS: An AR learning model for subway maintenance with environmental color complexity at different task difficulties is provided. Environmental color complexity does not affect performance when learning with AR, but users prefer environments with low-complexity colors. The use of AR head-mounted display in subway maintenance had a positive impact on learning task performance. The AR image learning mode reduces task completion time by 17% compared to the AR real-time learning mode. The AR real-time learning mode reduces the number of head deflections by 83% compared to the AR image learning mode. [ABSTRACT FROM AUTHOR]
Copyright of International Journal 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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  Label: Title
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  Data: The Impact of Task Difficulty, Environmental Color Complexity, and Teaching Models on AR-Assisted Subway Maintenance Training.
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  Data: <searchLink fieldCode="AR" term="%22Dandan%2C+Du%22">Dandan, Du</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kaidi%2C+Yu%22">Kaidi, Yu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dongyu%2C+Yu%22">Dongyu, Yu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jinyi%2C+Zhi%22">Jinyi, Zhi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yun%2C+Wang%22">Yun, Wang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jing%2C+Chunhui%22">Jing, Chunhui</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Sep2025, Vol. 41 Issue 18, p11459-11472. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Cognitive+load%22">Cognitive load</searchLink><br /><searchLink fieldCode="DE" term="%22Task+performance%22">Task performance</searchLink><br /><searchLink fieldCode="DE" term="%22Color+in+nature%22">Color in nature</searchLink><br /><searchLink fieldCode="DE" term="%22User-centered+system+design%22">User-centered system design</searchLink><br /><searchLink fieldCode="DE" term="%22Railroad+maintenance+%26+repair%22">Railroad maintenance & repair</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper investigates whether different task difficulties, color complexity, and choice of instructional application mode affect cognitive load, usability, visual discomfort, and job performance in subway overhaul training tasks and compares the findings under AR learning modes with those under traditional paper-based learning manuals. This study had sixteen participants and used eight experimental conditions. Tests were completed for environmental color complexity, task difficulty, and instructional application mode. The experimental variables were analyzed using three-way analysis of variance. The results showed that the color complexity of the carriage environment did not affect the participants' cognitive load and performance, but the use of AR glasses in subway maintenance was influenced by task difficulty and instructional application mode. For example, the AR image format reduced task completion time by an average of 25.9% for easy tasks, while the AR real-time format reduced head deflections by an average of 83.9%, suggesting that there is reliable potential for applying AR to subway overhaul training. This paper can provide a theoretical basis for developing training strategies for the different environments and tasks difficulties of subway maintenance. HIGHLIGHTS: An AR learning model for subway maintenance with environmental color complexity at different task difficulties is provided. Environmental color complexity does not affect performance when learning with AR, but users prefer environments with low-complexity colors. The use of AR head-mounted display in subway maintenance had a positive impact on learning task performance. The AR image learning mode reduces task completion time by 17% compared to the AR real-time learning mode. The AR real-time learning mode reduces the number of head deflections by 83% compared to the AR image learning mode. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal 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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        Text: English
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      – TitleFull: The Impact of Task Difficulty, Environmental Color Complexity, and Teaching Models on AR-Assisted Subway Maintenance Training.
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              Text: Sep2025
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