Generative AI-Assisted Academic Transgressions: A Theoretical “Reflegration” on Student and Faculty Cheating - Meraji Oskuie, Shalaleh (AI and Tech in Behavioral and Social Sciences, 2025)
Academic transgression—any behavior, belief, or condition that violates social and academic norms—is a persistent concern in educational institutions. Cheating, a prevalent form of academic transgression, has been exacerbated by the advent of Generative Artificial Intelligence (GAI). While GAI offers remarkable opportunities, its incorporation into research, publication, and teaching and learning processes has also facilitated easier forms of cheating and fraud. By combining the fraud diamond framework with self-control theory, this article aimed to delve into the factors affecting GAI-assisted cheating by both students and faculty members. To this end, I intertwined the critical academic reflective writing method with side-by-side propositional theoretical integration—what I coin as Theoretical “Reflegration”—in a cross-level (macro–micro) and interdisciplinary manner, bridging the gap between practical experience and theory by providing personal examples that illustrate theoretical principles.  I discussed how perceived pressure (low self-efficacy, insufficient knowledge/skills, pressure for high attainment), rationalization (lack of interest/unwillingness, dissatisfaction, low motivation), role conflicts (a sub-factor shared by both perceived pressure and rationalization);  perceived opportunity (GAI affordances, others’ lack of time/knowledge/evidence), capacity (GAI literacy, sense of impunity), and low self-control with its six sub-factors, can influence academic cheating behaviors among students and faculty. The arguments presented in this article can inform the development of effective solutions and policies to deter and detect at-risk individuals, reduce cheating-conducive situations, and promote academic integrity.