Analysis of Professional Internship Evaluations using Artificial Intelligence to enhance the Graduate Profile of Industrial Civil Engineering

Abstract

The present document offers an analysis of evaluations by supervisors at internship sites and self-assessments by Industrial Civil Engineering students at the Temuco Campus of the Universidad Autónoma de Chile during their pre-internship and professional internship periods in the summer of 2024–2025. This analysis is supported by Microsoft Copilot and GPT-5. The objective of the initiative is to identify progress in students' competencies during their final years of study. This contributes to the continuous improvement of the graduate profile and its integration into the program's curriculum. A substantial degree of consistency and advancement were evident from the fourth to the fifth year, with mean differences per competency amounting to less than 0.15 points. The socio-emotional competencies of adaptability, positive attitude, teamwork, responsibility, cooperation, and integration were consolidated as strengths in both periods. Meanwhile, the technical-instrumental competencies progressed from the fourth to the fifth year. However, room for improvement was identified in advanced tools, time management, technical communication, initiative, and applied creativity. The integration of artificial intelligence (AI) capabilities has enabled the expeditious processing and synthesis of a substantial volume of quantitative and qualitative data. This has facilitated the identification of patterns and the formulation of well-supported conclusions. These findings are of significant value for the contemporary and prospective graduate profile, thereby enabling graduates to effectively address prevailing workplace challenges.