Authors: Hugo Roger Paz
Abstract: The emergence of Generative Artificial Intelligence (GenAI) has reshaped higher education, presenting both opportunities and ethical-pedagogical challenges. This article presents an empirical case study on the complete cycle (design, initial failure, redesign, and re-evaluation) of an intervention using an AI Tutor (ChatGPT) in the “Hydrology and Hydraulic Works” course (Civil Engineering, UTN-FRT, Argentina). The study documents two interventions in the same cohort (n=23). The first resulted in widespread failure (0% pass rate) due to superficial use and serious academic integrity issues (65% similarity, copies > 80%). This failure forced a comprehensive methodological redesign. The second intervention, based on a redesigned prompt (Prompt V2) with strict evidence controls (mandatory Appendix A with exported chat, minimum time $\geq$ 120 minutes, verifiable numerical exercise) and a refined rubric (Rubric V2), showed significantly better results: a median score of 88/100 and verifiable compliance with genuine interaction processes. Using a mixed-methods approach (reproducible document analysis and rubric analysis), the impact of the redesign on integrity and technical performance is evaluated. The results demonstrate that, without explicit process controls, students prioritize efficiency over deep learning, submitting documents without real traceability. A transferable assessment protocol for STEM courses is proposed, centered on “auditable personal zones,” to foster higher-order thinking. The study provides key empirical evidence from the context of a public Latin American university.
