Italo Santos

Ronnie de Souza Santos, Italo Santos, Giuseppe Destefanis et al.

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.

Rodolfo Gil-Pereira, Ronnie de Souza Santos, Cleyton Magalahes et al.

This paper presents SLRMentor, a conversational assistant designed to support both learning about the systematic literature review process and the execution of planning activities in software engineering. The tool offers general guidance on SLR methodology and supports key planning tasks, including search string construction and reasoning about inclusion and exclusion criteria, with explanations grounded in established SLR guidelines. A pilot validation with graduate students suggests that SLRMentor helps clarify the SLR process and planning decisions, lowers initial barriers for novice researchers, and supports learning while still requiring active methodological judgment.