This single-case study explores generative AI disclosure's effects on perceived work quality in students, suggesting improvements in academic integrity practices.
Abstract This single-case study examines a course-level disclosure mechanism for generative AI assistance—the Declaration of Artificial Intelligence Uses (Declaración de Usos de Inteligencia Artificial, DUIA)—in a Bachelor’s capstone thesis course (trabajo final de licenciatura) at a private Argentine university (Semesters: 2, 2024; 1, 2025). The intervention requires students to declare which predefined AI-supported tasks were used in their final submission. Data come from administrative records, a post-course student survey (n = 38 of 46 completers), and an instructor survey (n = 3 of 3). We use descriptive statistics and cross-tabulations, plus inductive thematic analysis of open responses, integrating strands abductively. Process indicators show high reach and fidelity with low burden; most students reported perceived improvements in work quality and acknowledged gains in transparency. Exploratory associations suggest that a broader, relevant mix of AI-supported tasks co-occurs with higher perceived quality and with interest in AI training. Instructors highlighted value for dialogue on authorship and flagged the need for procedures for AI-assisted feedback. We interpret findings with acceptance/adoption, task–technology fit, and integrity/governance lenses and outline implications for course design and institutional policy.
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Sánchez et al. (2025) studied this question.
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