Academic integrity in higher education is increasingly shaped by complex socio-technical environments marked by automated tools, evolving institutional practices, and heightened performance pressures. Within this context, large language models (LLMs) are becoming prevalent in software engineering education, further blurring boundaries around acceptable assistance and authorship. This study investigates how software engineering students describe their emotional experiences after using LLMs in ways they perceive as academically inappropriate. We conducted a cross-sectional survey with 116 undergraduate students. Results show emotionally heterogeneous responses. Indifference was most frequent, including among students who recognized risks to learning and academic standing. Guilt and anxiety were reported in relation to moral discomfort and concern about penalties. Relief and satisfaction were evident primarily in deadline-driven contexts and situations of unclear guidance.

翻译：高等教育中的学术诚信日益受到自动化工具、不断演变的制度实践以及加剧的绩效压力等复杂社会技术环境的影响。在此背景下，大语言模型在软件工程教育中日趋普及，进一步模糊了可接受帮助与作者身份之间的界限。本研究调查了软件工程学生在以自认为学术不当的方式使用大语言模型后，如何描述其情绪体验。我们对116名本科生进行了横断面调查。结果显示情绪反应具有异质性。冷漠最为常见，甚至在认识到学习和学业成绩风险的学生中也如此。内疚和焦虑与道德不适及对惩罚的担忧相关。解脱和满足感主要出现在截止日期驱动及指导不明确的情境中。