Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education

Aim/Purpose: To examine how ethical awareness, cognitive appraisal (trust and perceived usefulness), digital competence, academic performance, and gender influence university students’ ethical use of ChatGPT and academic integrity. Background: This study explores how university students’ use of ChatGPT influences academic integrity in higher education. It responds to emerging integrity challenges posed by generative AI by empirically testing a model that links transparency, plagiarism avoidance, bias awareness, and responsible use to academic integrity outcomes across universities in the Gulf region. Methodology: PLS-SEM analysis of survey data from 318 students across five Gulf-region universities; tests direct effects of four ethical variables, mediation by trust in AI and perceived usefulness, and moderation by digital literacy and CGPA. Contribution: This study provides empirical evidence that core ethical-use dimensions significantly enhance academic integrity; clarifies the mediating roles of trust/usefulness and the moderating roles of digital literacy/CGPA; and documents gender and discipline differences in usage. Findings: All four ethical variables positively and significantly predict academic integrity: Trust in AI and perceived usefulness act as partial mediators. Digital literacy and CGPA significantly moderate several relationships. High-performing and senior students report more frequent and effective use of ChatGPT; gender and discipline differences are evident. Recommendations for Practitioners: Embed digital literacy and ethical-AI training (transparency, anti-plagiarism, bias awareness, responsible use) into curricula; implement inclusive AI policies; and guide trust calibration and verification workflows to support responsible use. Recommendation for Researchers: Extend the model across countries and disciplines; examine longitudinal effects; test additional mediators (e.g., AI anxiety, institutional policy clarity) and moderators (e.g., year of study, assessment type); compare alternative SEM and causal designs. Impact on Society: Promotes responsible AI adoption that safeguards academic ethics, supports equitable student outcomes, and informs policy for trustworthy AI use in higher education ecosystems. Future Research: Conduct multi-institutional replications, experimental interventions on ethics/digital literacy training, and studies of assessment design that balance AI use with integrity (e.g., oral/ authentic assessments).