(untitled) (SCIÉNDO INGENIUM, 2025)
This study presents a comparative analysis of Text Mining applications in R and the ChatGPT language model for processing open-ended responses in teaching performance evaluations at the National University (UNA) of Costa Rica. Simulated data based on historical student evaluations were used to ensure confidentiality. The analysis in R included techniques such as tokenization, term frequency analysis, word association, and sentiment analysis, while ChatGPT was employed for semantic interpretation, summary generation, and automatic categorization of strengths and areas for improvement. The results show that R provides statistical precision and the ability to explore textual patterns, whereas ChatGPT excels in interpretative flexibility and its potential to automate analytical report generation. The high correlation between both approaches in the detection of emotions and key topics demonstrates the complementarity of their outcomes. It is concluded that integrating text mining tools and language models can optimize qualitative analysis in educational contexts, promoting more objective, efficient, and evidence-based evaluation processes. This combined approach offers a replicable framework to strengthen academic management and foster continuous improvement in university teaching quality.