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This study investigates the patterns of AI use and attitudes among students, faculty, and administrative staff at a teacher education university, utilizing a sample of 2,121 individuals. The findings reveal a significant AI adaptation gap, with students exhibiting higher AI usage and perceived usefulness compared to faculty and staff, who expressed greater concerns regarding academic integrity and endorsed responsible-use norms more strongly. Notably, perceived usefulness was found to have the strongest positive association with trust in AI, highlighting the importance of user perceptions in fostering trust across different university roles.
Students embrace AI more enthusiastically than faculty and staff, but this enthusiasm is tempered by significant concerns over academic integrity.
The purpose of this study was to analyze patterns of artificial intelligence (AI) use and attitudes toward AI among students, faculty, and administrative staff at a large university specializing in teacher education. The analytical sample comprised 1809 students, 250 faculty members, and 62 administrative staff members (N = 2121). Three role-adapted 75-item questionnaires covered the frequency and contexts of AI use, perceived usefulness, trust and control, academic integrity concerns, responsible-use norms, institutional policy clarity, and perceived improvement in output quality. Data analysis included descriptive statistics, Welch group comparisons, pooled ordinary least squares (OLS) models, reliability and dimensionality checks for observed indices, and exploratory student-only K-means clustering. The results revealed a pronounced AI adaptation gap across university groups. Students reported higher current AI-use intensity and perceived usefulness than faculty and administrative staff, whereas faculty and administrative staff reported stronger academic integrity concerns and greater endorsement of responsible-use norms. In the pooled OLS trust model, perceived usefulness had the strongest standardized positive association with trust in AI (beta = 0.402); institutional policy clarity also had a positive but weaker association (beta = 0.223). Students reported higher perceived policy clarity than faculty, while neither group differed significantly from administrative staff. Exploratory clustering indicated heterogeneity among students in experience, competence, usefulness, trust, and control, but did not establish a latent typology across university groups. The cross-sectional, self-reported data show associations and group differences rather than causal effects on learning or objective outcomes.
