Academic Integrity and Emotional Responses to Inappropriate LLM Use in Software Engineering Education

2026-06-05

AI Summary

This study investigates the emotional responses of undergraduate software engineering students following inappropriate use of large language models (LLMs) and examines the implications for academic integrity. Through a cross-sectional survey of 116 students, self-reported data were coded and quantitatively analyzed using an emotion classification framework, revealing significant heterogeneity in affective reactions to LLM misuse. Apathy emerged as the most prevalent response, while guilt and anxiety stemmed from moral conflict and fear of sanctions; notably, feelings of relief or even satisfaction arose under conditions of institutional ambiguity or deadline pressure. Challenging the assumption that LLM misuse reflects a uniform moral failing, the findings highlight how performance pressures and unclear norms shape both behavior and emotion, offering empirical grounding for more nuanced academic integrity policies.

Abstract

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.

Problem

• academic integrity
• large language models
• emotional responses
• software engineering education
• inappropriate use

Innovation

• academic integrity
• large language models
• emotional responses
• software engineering education
• socio-technical environment