A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools
As a critical factor in determining a product’s success, users’ willingness to adopt artificial intelligence-generated content (AIGC) tools is a key driver for their sustainable development. As a new productivity tool, AIGC tool adoption by design students is influenced by vari...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11048922/ |
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| author | Xiang Wang Mingxing Li Yu Yao Xiaoyang Zhu |
| author_facet | Xiang Wang Mingxing Li Yu Yao Xiaoyang Zhu |
| author_sort | Xiang Wang |
| collection | DOAJ |
| description | As a critical factor in determining a product’s success, users’ willingness to adopt artificial intelligence-generated content (AIGC) tools is a key driver for their sustainable development. As a new productivity tool, AIGC tool adoption by design students is influenced by various factors. However, there is a lack of systematic research on this topic in the academic community, which hinders the sustainable development of AIGC tools. Based on this, the study combines the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to construct a dual-perspective model—integrating “technology (external)” and “individual (internal)” dimensions—to investigate the key factors influencing design students’ willingness to adopt AIGC tools in their professional learning. A questionnaire was distributed to Chinese design students, resulting in 517 valid responses. The structural equation model (SEM) analysis yielded the following findings: Characteristics of AIGC Technology (CoAT) and students’ task characteristics positively influence task-technology fit. CoAT positively affect performance expectations (PE) but have no significant impact on effort expectations (EE). Task-technology fit positively influences use behaviors and performance expectations. Additionally, performance expectations, effort expectations, and social influences (SI) significantly enhance willingness to use, which in turn drives use behaviors (UB). Conversely, facilitating conditions (FC) do not affect use behaviors. Based on the empirical findings, this study discusses strategies for enhancing design students’ adoption of AIGC tools and provides recommendations for promoting their adoption and sustainability in design education.eing rejected by search engines. |
| format | Article |
| id | doaj-art-ca53d4c958ef49fa9ba1315c3de5e3e7 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ca53d4c958ef49fa9ba1315c3de5e3e72025-08-20T02:36:30ZengIEEEIEEE Access2169-35362025-01-011311775311777010.1109/ACCESS.2025.358259911048922A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC ToolsXiang Wang0https://orcid.org/0000-0002-2819-6936Mingxing Li1Yu Yao2Xiaoyang Zhu3School of Art and Design, Nanjing Institute of Technology, Nanjing, ChinaDepartment of Design, Shanghai Academy of Fine Arts, Shanghai University, Shanghai, ChinaDesign and Craft Graduate School, Hongik University, Seoul, South KoreaSchool of Arts and Design, Sanming University, Sanming, ChinaAs a critical factor in determining a product’s success, users’ willingness to adopt artificial intelligence-generated content (AIGC) tools is a key driver for their sustainable development. As a new productivity tool, AIGC tool adoption by design students is influenced by various factors. However, there is a lack of systematic research on this topic in the academic community, which hinders the sustainable development of AIGC tools. Based on this, the study combines the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to construct a dual-perspective model—integrating “technology (external)” and “individual (internal)” dimensions—to investigate the key factors influencing design students’ willingness to adopt AIGC tools in their professional learning. A questionnaire was distributed to Chinese design students, resulting in 517 valid responses. The structural equation model (SEM) analysis yielded the following findings: Characteristics of AIGC Technology (CoAT) and students’ task characteristics positively influence task-technology fit. CoAT positively affect performance expectations (PE) but have no significant impact on effort expectations (EE). Task-technology fit positively influences use behaviors and performance expectations. Additionally, performance expectations, effort expectations, and social influences (SI) significantly enhance willingness to use, which in turn drives use behaviors (UB). Conversely, facilitating conditions (FC) do not affect use behaviors. Based on the empirical findings, this study discusses strategies for enhancing design students’ adoption of AIGC tools and provides recommendations for promoting their adoption and sustainability in design education.eing rejected by search engines.https://ieeexplore.ieee.org/document/11048922/Artificial intelligencegenerative modelshuman computer interactioneducational technologyuser interfaces |
| spellingShingle | Xiang Wang Mingxing Li Yu Yao Xiaoyang Zhu A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools IEEE Access Artificial intelligence generative models human computer interaction educational technology user interfaces |
| title | A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools |
| title_full | A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools |
| title_fullStr | A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools |
| title_full_unstemmed | A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools |
| title_short | A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools |
| title_sort | study of factors influencing chinese design students x2019 adoption of aigc tools |
| topic | Artificial intelligence generative models human computer interaction educational technology user interfaces |
| url | https://ieeexplore.ieee.org/document/11048922/ |
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