Driving Factors of AI-Assisted Academic Cheating among Undergraduate Students

This dataset contains the survey data used in the study examining the driving factors of AI-assisted academic cheating among undergraduate students. The data were collected through an anonymous online questionnaire administered to undergraduate students enrolled at a university of science and technology in Vietnam between October 25 and November 22, 2025.The dataset includes responses from 863 undergraduate students after removing incomplete or invalid responses from the initial sample of 881 participants. The survey measured four categories of AI-assisted academic cheating behaviors and four groups of potential driving factors associated with such behaviors. In addition, demographic variables such as gender and student seniority were collected.The dataset was used to test a conceptual model of AI-assisted academic cheating using Partial Least Squares Structural Equation Modeling (PLS-SEM). The variables in the dataset correspond to the measurement items used in the survey instrument described in the associated research article.All responses were collected anonymously, and no personally identifiable information is included in the dataset.