Same Rules, Mixed Messages: Exploring Community Perceptions of Academic Dishonesty in Computing Education
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 = 22), 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 responded to an open-ended question about motivations for academic dishonesty. Our findings reveal differences in perceptions across the three stakeholder groups. Among the instructors who participated, grade pressure and laziness were the most frequently mentioned motivations for academic dishonesty, whereas students and TAs more frequently emphasized gaps in prerequisite knowledge and time management challenges. Given the small instructor sample, these qualitative findings should be interpreted as descriptive and exploratory rather than representative of instructors more broadly. These results highlight differences in how academic dishonesty is understood across classroom stakeholders and underscore the need for clearer communication of academic-integrity expectations and instructional strategies that help students interpret and navigate those expectations 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.