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<p>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.</p>

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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

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Zhu Zhu,
  • Yingying Ren,
  • An ran Shen

摘要

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.