Understanding University Students' Use of Generative AI: The Roles of Demographics and Personality Traits
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ID: 282517
2025
The use of generative AI (GAI) among university students is rapidly
increasing, yet empirical research on students' GAI use and the factors
influencing it remains limited. To address this gap, we surveyed 363
undergraduate and graduate students in the United States, examining their GAI
usage and how it relates to demographic variables and personality traits based
on the Big Five model (i.e., extraversion, agreeableness, conscientiousness,
and emotional stability, and intellect/imagination). Our findings reveal: (a)
Students in higher academic years are more inclined to use GAI and prefer it
over traditional resources. (b) Non-native English speakers use and adopt GAI
more readily than native speakers. (c) Compared to White, Asian students report
higher GAI usage, perceive greater academic benefits, and express a stronger
preference for it. Similarly, Black students report a more positive impact of
GAI on their academic performance. Personality traits also play a significant
role in shaping perceptions and usage of GAI. After controlling demographic
factors, we found that personality still significantly predicts GAI use and
attitudes: (a) Students with higher conscientiousness use GAI less. (b)
Students who are higher in agreeableness perceive a less positive impact of GAI
on academic performance and express more ethical concerns about using it for
academic work. (c) Students with higher emotional stability report a more
positive impact of GAI on learning and fewer concerns about its academic use.
(d) Students with higher extraversion show a stronger preference for GAI over
traditional resources. (e) Students with higher intellect/imagination tend to
prefer traditional resources. These insights highlight the need for
universities to provide personalized guidance to ensure students use GAI
effectively, ethically, and equitably in their academic pursuits.
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Authors | Newnew Deng; Edward Jiusi Liu; Xiaoming Zhai |
Journal | arXiv |
Year | 2025 |
DOI | DOI not found |
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