What influences college students using AI for academic writing? - A quantitative analysis based on HISAM and TRI theory
Open accessAbstract
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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- Authors
- Cui, Y.
- Affiliations
- Xinyang Normal University (CN)
- Corresponding author
- Cui, Yulu
- ORCID
- Cui, Yulu
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 8
- Pages
- 100391
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4408252164
- Cited by
- 26
- References
- 73
- Citations by year
- 2026: 15 · 2025: 11
- FWCI
- 26.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Education of the People's Republic of China (23C10205010)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- university students, Technology Readiness Index, perceived enjoyment, technological optimism, intention to use, perceived usefulness, perceived ease of use, AI tool design, academic dishonesty
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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