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Research Article

The AI Adaptation Gap in Higher Education: Students, Faculty, and Administrative Staff

Synthesis: Braun and Khafizov (2026) analyze patterns of Generative AI use and attitudes toward AI among students, faculty, and administrative staff at a large university specializing in teacher education, using three role-adapted 75-item questionnaires (N = 2,121: 1,809 students, 250 faculty, 62 staff). They document a pronounced "AI adaptation gap": students report higher current AI-use intensity and perceived usefulness, while faculty and administrative staff report 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 (β = 0.402), with institutional policy clarity also positive but weaker (β = 0.223). The cross-sectional, self-reported design supports associative and group-difference conclusions rather than causal claims.

Key Findings

  1. A pronounced AI adaptation gap exists across university groups: students report higher current AI-use intensity and perceived usefulness than faculty and administrative staff.
  2. Faculty and administrative staff report stronger academic integrity concerns and greater endorsement of responsible-use norms than students.
  3. In the pooled OLS trust model, perceived usefulness had the strongest standardized positive association with trust in AI (β = 0.402); institutional policy clarity was positive but weaker (β = 0.223).
  4. Students reported higher perceived policy clarity than faculty, while neither group differed significantly from administrative staff.
  5. Exploratory K-means clustering indicated heterogeneity among students in experience, competence, usefulness, trust, and control, but did not establish a stable latent typology across university groups.

Study Design & Method

The study surveyed 1,809 students, 250 faculty, and 62 administrative staff at a single large university specializing in teacher education. Three role-adapted 75-item questionnaires covered 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. Analyses included descriptive statistics, Welch group comparisons, pooled ordinary least squares (OLS) models, reliability and dimensionality checks, and exploratory student-only K-means clustering. The authors emphasize that the cross-sectional, self-reported data show associations and group differences rather than causal effects on learning or objective outcomes.

Implications for AI in Education

The findings support stakeholder-aware, task-sensitive institutional AI policies that address academic integrity requirements and transparent disclosure of AI use. The misalignment among students, faculty, and administrative staff — the three groups that jointly shape university practice — may determine whether AI is perceived as an educational resource, a convenient administrative service, or a source of academic and ethical risk. For Higher Ed governance and institutional policy, the results suggest that perceived usefulness drives trust more strongly than policy clarity, so efforts to build trust should attend to demonstrable usefulness while clarifying responsible-use norms. The study also connects to technology adoption and AI Literacy literatures.

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Citation

Braun, Y. S., & Khafizov, S. M. (2026). The AI adaptation gap in higher education: Students, faculty, and administrative staff.

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