Generative AI in University Mathematics: Attitudes and Academic-Leisure Use Patterns

Abstract

The rapid spread of generative artificial intelligence has reshaped university e-learning, yet its incorporation into mathematics learning remains constrained by disciplinary demands for precision, justification, and epistemic scrutiny. This study examined the relationship between socio-demographic variables, access conditions, and attitudes towards AI in mathematics, and analyzed how these factors were associated with reported use of AI tools in two differentiated contexts: academic learning and leisure. A quantitative, observational, non-experimental, cross-sectional study was conducted with 869 students from the University of Granada across the Melilla, Ceuta, and Granada campuses. Data were collected through an online questionnaire that included the IAMAT scale and two dichotomous indicators of AI use. Inferential analyses were estimated with between 834 and 846 complete cases, depending on the procedure. The IAMAT scale showed adequate internal consistency (α = .796), high sampling adequacy (KMO = .888), and an interpretable two-factor structure that distinguished between usefulness/confidence and uncertainty/errors. AI use was more frequent in mathematics learning (62.6%) than in leisure (28.3%). Logistic regression models indicated that positive attitudes towards AI in mathematics increased the likelihood of use in both contexts. In academic use, older age, lack of Wi-Fi access, and membership of the Melilla campus were associated with a lower probability of use, whereas participation in voluntary work was associated with a higher probability. In leisure use, women showed a lower probability of use than men. In addition, K-means clustering identified six differentiated profiles defined by age, perceived usefulness, and distrust. These profiles discriminated academic use significantly, but not leisure use. The findings suggest that the adoption of AI in mathematics cannot be reduced to technological access alone, because it is also shaped by domain-specific cognitive and affective dispositions. From an e-learning perspective, these findings contribute to AI-supported mathematics education by showing that digital learning environments should move beyond mere access to GenAI and embed critical AI literacy, mathematical verification criteria, and reasoning-oriented tasks in which students explain, check, and revise AI-generated solutions rather than delegate the full intellectual workload to the tool.