Same Rules, Mixed Messages: Exploring Community Perceptions of Academic Dishonesty in Computing Education
This research addresses misaligned perceptions of cheating in computing education, which is an incremental contribution to improving communication and strategies in post-COVID learning environments.
The study investigated perceptions of academic dishonesty among instructors, teaching assistants, and students in computer science courses, finding notable discrepancies in how groups classify cheating scenarios and attribute motivations, such as instructors focusing on grade pressure and laziness while students and TAs cite knowledge gaps and time management.
Academic dishonesty has long been a concern in computing education, and the rapid growth of online learning and generative artificial intelligence (AI) has further complicated how cheating is perceived and addressed. We report on a study examining how different actors in the computer science (CS) classroom interpret potential cheating scenarios and the motivations behind academic dishonesty. Participants included instructors (n = 6), teaching assistants (TAs; n = 21), and undergraduate students (n = 538) enrolled in two CS courses at a large Southeastern institution in the United States. Respondents classified scenarios as serious cheating, trivial cheating, or not cheating and answered to an open-ended question about motivations for academic dishonesty. Our findings reveal notable discrepancies across groups: instructors most often attribute cheating to grade pressure and laziness, while students and TAs emphasize gaps in prerequisite knowledge and time management challenges. These results highlight misaligned perceptions of academic dishonesty and underscore the need for clearer communication and curricular strategies in computing education, particularly in post-COVID learning environments where hybrid instruction, increased reliance on digital resources, and AI-assisted tools have reshaped students' approaches to coursework and learning.