Campus Climate Counts: Insights on Institutional Versus Individual Predictors of Academic AI Integrity

• In Event: AERA Roundtable Session Friday 3:45 pm - Gold 1
  In Roundtable Session: Roundtable Session 5: Student Learning and Development in College and Beyond

Abstract

Generative-AI tools heighten academic-integrity dilemmas, yet evidence contrasting institutional and individual influences on students’ ethical judgment remains scarce. Guided by Self-Determination Theory and moral-climate research, we anonymously surveyed 167 students across six colleges at a Midwestern R1 university on technological self-efficacy, academic motivation, perceived institutional integrity, and evaluations of AI misconduct. Reliable scales (α =.77–.93) and a single Ethical-Judgment factor (52 % variance) were confirmed. Preliminary regressions show institutional integrity predicts ethical judgment most strongly (β ≈ .40, p < .001), overshadowing intrinsic/extrinsic motivation and a modest negative effect of self-efficacy (β ≈ –.17); motivation did not moderate the link. Pending robustness checks, findings highlight policy clarity and faculty messaging as key levers for fostering ethical AI use.

Authors

• Peyman Jahanbin, University of Kansas

• Nancy Jo Williams, University of Kansas

• Atiyeh Choobkar, University of Kansas