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This study investigates the prevalence and context of LLM misuse among 116 software engineering students across multiple countries via a cross-sectional survey combining quantitative and qualitative data. The research found that LLM cheating was most common in programming assignments and documentation tasks, particularly when students faced time pressure or unclear instructions. Students were generally aware of the risks associated with LLM misuse, but perceived formal sanctions as weak.
Software engineering students are most likely to misuse LLMs on programming assignments and documentation, especially when they feel squeezed for time or lack clear guidance.
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.
