Utility judgments, risk concerns, and generative AI use intention among first-year university students

Generative AI is increasingly embedded in university learning, requiring students to judge its usefulness, how their input data are handled, and whether its outputs are reliable. Adoption research often treats risk as a single broad construct, leaving unclear whether distinct risk concerns relate differently to use intention. This study examined how utility judgments and two distinct risk concerns (privacy and data concerns and output reliability concerns) were associated with generative AI use intention, and whether intolerance of uncertainty was associated with these concerns, among first-year university students.

Cross-sectional online survey data from 504 first-year students at six public universities in central China were analyzed using confirmatory factor analysis and planned composite-score path modeling. Parallel latent-variable models were estimated to assess sensitivity to score aggregation, measurement error, and estimator choice.

Perceived ease of use was positively associated with perceived usefulness (β = 0.507), and perceived usefulness was positively associated with use intention (β = 0.602). The indirect association from perceived ease of use to use intention through perceived usefulness was positive (B = 0.286, 95% CI [0.210, 0.370]). Intolerance of uncertainty was positively associated with privacy and data concerns and with output reliability concerns, and both associations were stable across composite-score and latent-variable models. Neither concern showed a stable negative association with use intention. In the ordered-categorical analyses, a statistically supported negative coefficient for privacy and data concerns emerged only when output reliability concerns were included simultaneously, whereas the positive coefficient for output reliability concerns received statistical support only in those analyses.

Utility judgments were the most consistent correlates of generative AI use intention, whereas intolerance of uncertainty was consistently associated with stronger risk appraisal. Separating privacy and data concerns from output reliability concerns clarifies that stronger risk concerns do not uniformly correspond to lower use intention.