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Exploring the Acceptance of Generative Artificial Intelligence-Assisted Learning and Design Creation among Students in Art Design Specialties: Based on the Extended TAM Model

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Title: Exploring the Acceptance of Generative Artificial Intelligence-Assisted Learning and Design Creation among Students in Art Design Specialties: Based on the Extended TAM Model
Language: English
Authors: Zhu Zhu (ORCID 0009-0001-1182-7375), Yingying Ren, An ran Shen
Source: Education and Information Technologies. 2025 30(13):18651-18678.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 28
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Computer Assisted Design, Computer Assisted Instruction, Art Education, Computer Attitudes, Usability, Student Attitudes, Intention, Influences
DOI: 10.1007/s10639-025-13551-3
ISSN: 1360-2357
1573-7608
Abstract: Current educational trends leverage artificial intelligence (AI) to provide high-quality teaching and enhance students' learning competitiveness. This study aimed to evaluate the acceptance of artificial intelligence generated content (AIGC) for assisted learning and design creation among art and design students. Based on an extended technology acceptance model (ETAM), this study explored how external variables influence perceived usefulness (PU) and perceived ease of use (PEOU), which in turn affect attitude towards use (ATT) and behavioral intention (BI). Data were collected from 382 students via a questionnaire survey and analyzed using a structural equation model. The results confirmed 12 out of the 14 hypotheses. Among them, facility condition (FC), output quality (OQ), task-technology fit (TTF), and hedonic motivation (HM) positively influenced PU and PEOU, whereas AI anxiety (AIA) negatively affected PU and PEOU. ATT had a significant positive effect on BI. This study provides theoretical support and practical insights for promoting AIGC applications, advancing sustainable education, and optimizing user engagement.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480848
Database: ERIC
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  Data: Exploring the Acceptance of Generative Artificial Intelligence-Assisted Learning and Design Creation among Students in Art Design Specialties: Based on the Extended TAM Model
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  Data: <searchLink fieldCode="AR" term="%22Zhu+Zhu%22">Zhu Zhu</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0001-1182-7375">0009-0001-1182-7375</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yingying+Ren%22">Yingying Ren</searchLink><br /><searchLink fieldCode="AR" term="%22An+ran+Shen%22">An ran Shen</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Current educational trends leverage artificial intelligence (AI) to provide high-quality teaching and enhance students' learning competitiveness. This study aimed to evaluate the acceptance of artificial intelligence generated content (AIGC) for assisted learning and design creation among art and design students. Based on an extended technology acceptance model (ETAM), this study explored how external variables influence perceived usefulness (PU) and perceived ease of use (PEOU), which in turn affect attitude towards use (ATT) and behavioral intention (BI). Data were collected from 382 students via a questionnaire survey and analyzed using a structural equation model. The results confirmed 12 out of the 14 hypotheses. Among them, facility condition (FC), output quality (OQ), task-technology fit (TTF), and hedonic motivation (HM) positively influenced PU and PEOU, whereas AI anxiety (AIA) negatively affected PU and PEOU. ATT had a significant positive effect on BI. This study provides theoretical support and practical insights for promoting AIGC applications, advancing sustainable education, and optimizing user engagement.
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