Same Rules, Mixed Messages: Exploring Community Perceptions of Academic Dishonesty in Computing Education

2026-03-31

AI Summary

This study investigates discrepancies in perceptions of academic misconduct among instructors, teaching assistants, and students within computing education, situated in the context of widespread online learning and generative AI adoption. Combining quantitative survey data with qualitative open-ended responses, it offers the first systematic comparison of how these three key stakeholder groups define cheating scenarios and attribute their underlying causes. The findings reveal a pronounced cognitive gap: instructors predominantly attribute misconduct to grade pressure and laziness, whereas students and teaching assistants emphasize insufficient prior knowledge and difficulties with time management. These insights underscore the need for course designs and academic integrity policies that align with the evolving learning ecosystems of the post-pandemic, AI-integrated era, providing empirical grounding for more supportive and effective approaches to fostering academic honesty.

Technology Category

Application Domains: Humanities & Computational Social ScienceHumans and AI: Crowd Sourcing and Human ComputationCognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIResponsible Web: Fairness, accountability, transparency and ethics of web technologiesSocial Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• academic dishonesty
• computing education
• perception gap
• generative AI
• online learning

Innovation

• academic dishonesty
• computing education
• generative AI
• perception alignment
• hybrid learning