Understanding What Reduces Academic Misconduct Intention Among College Students in the AI Era

Abstract

The rapid development of artificial intelligence (AI) has created new challenges for academic integrity in higher education. Although previous studies have primarily examined academic misconduct among university students, less attention has been paid to college students and the factors that may mitigate their intentions to engage in such misconduct. Accordingly, this study examines the relationships between four factors and college students’ intentions to violate academic integrity in the context of AI use. A survey was conducted with 358 college students in Ho Chi Minh City, Vietnam. The data were analyzed using Cronbach’s alpha, exploratory factor analysis (EFA), Pearson’s correlation analysis, and multiple linear regression. The results indicate that perceived risk, perceived behavioral control, and attitudes toward academic misconduct are significantly and negatively associated with misconduct intentions, whereas subjective norms are not statistically significant. Perceived risk exhibits the strongest negative association with misconduct intentions (β = −0.514). This study contributes empirical evidence from a college student population and extends the application of the theory of planned behavior (TPB) by incorporating perceived risk alongside its original components. The findings suggest that educational institutions should establish clearer academic integrity policies, communicate the consequences of misconduct, and provide students with practical guidance on the acceptable use of AI.