What influences college students using AI for academic writing? - A quantitative analysis based on HISAM and TRI theory
Artificial intelligence (AI) technologies have been widely adopted as tools for academic writing, sparking interest in the factors that influence their adoption. However, existing research has predominantly focused on cognitive factors, such as perceived usefulness and ease of use, while giving insu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X25000311 |
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| Summary: | Artificial intelligence (AI) technologies have been widely adopted as tools for academic writing, sparking interest in the factors that influence their adoption. However, existing research has predominantly focused on cognitive factors, such as perceived usefulness and ease of use, while giving insufficient attention to emotional motivations (e.g., perceived enjoyment, emotional engagement) in students' use of AI tools. This gap has led to a somewhat oversimplified or incomplete understanding of the factors influencing students' adoption of AI technologies. This study constructs a theoretical framework based on the Hedonic Information System Acceptance Model (HISAM) and the Technology Readiness Index (TRI), and employs Structural Equation Modeling (SEM) to analyze survey data from 148 university students. The study finds that students' intention to use AI tools is influenced not only by cognitive evaluations, such as perceived usefulness and ease of use, but also by emotional factors, including technological optimism and perceived enjoyment. Perceived enjoyment, as a positive emotional factor in the interaction process, significantly promotes students' usage intention. Additionally, technological optimism, as a positive aspect of technology readiness, has a direct positive effect on perceived enjoyment. Furthermore, although negative experiences such as technical discomfort and insecurity have a minor impact on the evaluation of AI tool functionality, they still affect students' emotional experiences, thereby influencing their usage intention. The study suggests that future AI tool designs should prioritize enhancing students' emotional experiences by providing personalized feedback and optimizing user interfaces to improve ease of use. Moreover, increasing the tool's interpretability and transparency can help reduce the risks of misuse, such as academic dishonesty or a decline in learning ability, which may arise from negative emotional experiences. |
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| ISSN: | 2666-920X |