LLM Use, Cheating, and Academic Integrity in Software Engineering Education
This research addresses the problem of academic integrity in software engineering education for educators and institutions, highlighting how assessment design influences LLM misuse, but it is incremental as it builds on existing cheating literature.
The study investigated software engineering students' experiences with using large language models (LLMs) in ways they perceived as inappropriate, finding that misuse primarily occurred in programming assignments and routine tasks due to time pressure and unclear guidance, with 116 students surveyed reporting limited formal sanctions.
Background: Cheating in university education is commonly described as context dependent and influenced by assessment design, institutional norms, and student interpretation. In software engineering education, programming oriented coursework has historically involved ambiguity around collaboration, reuse, and external assistance. Recently, large language models (LLMs) have introduced additional mediation in the production of code and related artifacts. Aims: This study investigates how software engineering students describe experiences of using LLMs in ways they perceived as inappropriate, disallowed, or misaligned with course expectations. Method: A cross sectional survey was conducted with 116 undergraduate software engineering students from multiple countries, combining quantitative summaries with qualitative data. Results: Reported LLM cheating practices occurred primarily in programming assignments, routine coursework, and documentation tasks, often in contexts of time pressure and unclear guidance. Use during quizzes and exams was less frequent and more consistently identified as a violation. Students reported awareness of academic and professional consequences regarding LLM cheating, while formal sanctions were perceived as limited. Conclusions: Our study indicates that reported LLM misuse in software engineering is associated with assessment and instructional conditions, suggesting a need for clearer alignment between assessment design, learning objectives, and expectations for LLM use.