Examining academic integrity policy and practice in the era of AI: a case study of faculty perspectives - Alsharefeen, Rami; Al Sayari, Naji (Frontiers in Education, 2025)
IntroductionThis study investigates faculty perceptions of academic integrity policies addressing traditional and Generative Artificial Intelligence (GenAI) plagiarism at an internationalized higher education institution in the UAE.MethodologyEmploying a mixed-methods approach, quantitative data were collected from 71 faculty members representing 37 nations via an online survey, supplemented by qualitative insights from 17 semi-structured interviews. The research assessed perceptions across five dimensions—availability, visibility, clarity, adequacy, and effectiveness—based on Bretag et al.’s (2011) framework.ResultsResults revealed significant disparities between traditional and GenAI policies, with both rated only moderately effective. Faculty demonstrated a preference for educative over punitive approaches while identifying workload constraints, insufficient institutional support, detection challenges, cultural leniency, and systemic limitations as barriers to policy enforcement. A hierarchical perception of GenAI plagiarism emerged, with direct content copying considered most serious and AI-assisted practices involving minimal student contribution viewed less severely. Lower response rates to Gen-AI scenarios reflected faculty uncertainty amid insufficient guidelines.DiscussionThe findings underscore the necessity for agile, comprehensive policies that address technological advancements while emphasizing faculty engagement and contextual support. This research contributes novel insights into underexplored scenarios including language assistance, translation, peer idea sharing, and citation errors, illuminating the evolving landscape of academic integrity in digital and collaborative environments. The study advocates for balanced frameworks integrating educative strategies, technological tools, and cultural sensitivities to maintain academic integrity standards in the AI era.