College Students' Use Behavior of Generative AI and Its Influencing Factors under the Unified Theory of Acceptance and Use of Technology Model
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| Title: | College Students' Use Behavior of Generative AI and Its Influencing Factors under the Unified Theory of Acceptance and Use of Technology Model |
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| Language: | English |
| Authors: | Jie Xu (ORCID |
| Source: | Education and Information Technologies. 2025 30(14):19961-19984. |
| 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: | 24 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | College Students, Technology Uses in Education, Artificial Intelligence, Intention, Student Attitudes, Computer Attitudes, Gender Differences, Majors (Students), Foreign Countries, Student Behavior, Instructional Program Divisions |
| Geographic Terms: | China |
| DOI: | 10.1007/s10639-025-13508-6 |
| ISSN: | 1360-2357 1573-7608 |
| Abstract: | Generative Artificial Intelligence (AI) is steadily gaining prominence in higher education and brings about huge impact on college students' daily life. However, limited studies paid attention to college students' use behavior of generative AI and its influencing factors. The study aimed to explore this issue by adopting an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. Two generative AI related variables, named "novelty value" and "perceived humanness," were added to the UTAUT model, and the potential moderating effects of gender, grade and major were also considered in the model. The model was validated by collecting data from 1,190 college students at a Chinese university. Results indicated significant positive correlations among performance expectancy, effort expectancy, novelty value, social influence, facilitating conditions, and behavioral intention. Additionally, facilitating conditions and behavioral intention significantly influenced use behavior, while perceived humanness had no significant impact on behavioral intention. Moderating variables like gender and grade significantly affected the acceptance of generative AI, whereas major did not. The findings provided nuanced insights to advance the practical application of generative AI. Considering limited sample in the study, future research may encompass diverse demographics across multiple countries to enable cross-cultural comparisons. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1484042 |
| Database: | ERIC |
| Abstract: | Generative Artificial Intelligence (AI) is steadily gaining prominence in higher education and brings about huge impact on college students' daily life. However, limited studies paid attention to college students' use behavior of generative AI and its influencing factors. The study aimed to explore this issue by adopting an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. Two generative AI related variables, named "novelty value" and "perceived humanness," were added to the UTAUT model, and the potential moderating effects of gender, grade and major were also considered in the model. The model was validated by collecting data from 1,190 college students at a Chinese university. Results indicated significant positive correlations among performance expectancy, effort expectancy, novelty value, social influence, facilitating conditions, and behavioral intention. Additionally, facilitating conditions and behavioral intention significantly influenced use behavior, while perceived humanness had no significant impact on behavioral intention. Moderating variables like gender and grade significantly affected the acceptance of generative AI, whereas major did not. The findings provided nuanced insights to advance the practical application of generative AI. Considering limited sample in the study, future research may encompass diverse demographics across multiple countries to enable cross-cultural comparisons. |
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| ISSN: | 1360-2357 1573-7608 |
| DOI: | 10.1007/s10639-025-13508-6 |
