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

Background: 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. Methods: 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. Results: Perceived ease of use was positively associated with perceived usefulness ( Conclusion: 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.