Author
Listed:
- Mei Jiang
(Department of Psychology and Special Education, East Texas A&M University, Commerce, TX 75428, USA)
- Shifang Tang
(Department of Psychology and Special Education, East Texas A&M University, Commerce, TX 75428, USA)
- Qingwei Wang
(Department of Psychology, University of North Texas, Denton, TX 76205, USA)
Abstract
The purpose of this study was to examine the multivariate relationship between college students’ Big Five personality traits and their perceptions of generative artificial intelligence (AI). Guided by sustainable digital education and expectancy-value theory, this study investigated whether personality profiles were associated with students’ knowledge of AI, attainment value, intrinsic value, utility value, perceived cost, and intention to use AI. Using a cross-sectional survey design, data were collected from 375 students enrolled at a Southwestern doctoral-granting public university. Participants completed an adapted measure of generative AI perceptions and the Big Five Inventory, and canonical correlation analysis (CCA) was conducted to examine the multivariate relationship between the two variable sets. The results indicated that the full canonical model was statistically significant and that three interpretable canonical functions were retained. The first and strongest function showed that higher openness, agreeableness, and conscientiousness were associated primarily with greater AI knowledge and, to a lesser extent, with higher perceived cost. The second function indicated that higher neuroticism was associated with greater perceived cost and lower utility and attainment value. The third function showed that lower neuroticism, together with higher openness and conscientiousness, was associated with a stronger attainment value, greater intention to use AI, and lower perceived cost. Our findings suggest that students differ meaningfully in how they understand and value generative AI. These results have important implications for higher education because they highlight the potential value of differentiated, human-centered AI literacy efforts in supporting more equitable and responsible AI integration.
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