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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
Language: English
Authors: Jie Xu (ORCID 0000-0003-3345-4116), Yan Li (ORCID 0000-0002-0640-1783), Rustam Shadiev (ORCID 0000-0001-5571-1158), Cuixin Li
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
FullText Text:
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  Data: 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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  Data: <searchLink fieldCode="AR" term="%22Jie+Xu%22">Jie Xu</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-3345-4116">0000-0003-3345-4116</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yan+Li%22">Yan Li</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-0640-1783">0000-0002-0640-1783</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rustam+Shadiev%22">Rustam Shadiev</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5571-1158">0000-0001-5571-1158</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cuixin+Li%22">Cuixin Li</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Education+and+Information+Technologies%22"><i>Education and Information Technologies</i></searchLink>. 2025 30(14):19961-19984.
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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: <searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Intention%22">Intention</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Attitudes%22">Computer Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Majors+%28Students%29%22">Majors (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Program+Divisions%22">Instructional Program Divisions</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
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  Data: 10.1007/s10639-025-13508-6
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  Data: 1360-2357<br />1573-7608
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  Data: 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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      – Text: English
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        Type: general
      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Intention
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      – SubjectFull: Student Attitudes
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Instructional Program Divisions
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      – SubjectFull: China
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