Uso De Inteligência Artificial Na Análise De Conteúdo: Uma Nova Capacidade Pública?

Background: This article addresses an applied study on the use of Artificial Intelligence for content analysis. The problem at hand involved identifying the reasons for the low execution of federal public funds transferred to states and municipalities in Brazil, aimed at extending students' school hours under the Full-Time School Program. As of June 2024, there was R$ 3.8 billion in the accounts of these entities, representing approximately 20% of the funds utilized, while 37.5% of the execution period had already elapsed. The need for a specific assessment targeting 26 states, one federal district, and 5,568 municipalities, without available personnel or time for analysis, led to the methodological approach chosen. Materials and Methods: A questionnaire was administered to 5,595 agents, containing objective questions focused on elementary aspects that would provide relevant information for the results and serve as a consistency check for the analysis. To capture unforeseen situations that could hinder the study, open-ended questions were also included, whose analysis would require significant dedication and substantial time. Therefore, prior to sending the questionnaire, the decision was made to use Artificial Intelligence, specifically the paid version of ChatGPT, to conduct the analyses, which required appropriate structuring of the questionnaire. Results: The survey generated nearly three thousand responses, which were consolidated into a structured document of 2,947 pages. This document was built directly from the response spreadsheet and transformed into a continuous document using the mail merge feature in Microsoft Word, then converted to PDF for analysis. The objective responses reflected optimism from the managers regarding the utilization and contribution of the funds; however, the execution forecast indicated that approximately six more months would be needed to significantly increase fund usage. Hypotheses were confirmed that procurement processes, challenges in school meal provision, and legal difficulties in hiring personnel were major limiting factors. The analysis of the openended questions also revealed specific problems related to school transportation, introducing a new element that was not previously hypothesized. The adopted methodology also enabled regional analyses, which enhanced the Brazilian Federal Government’s capacity to provide targeted technical support. Conclusion: The combination of conventional methods, such as the objective questions in the questionnaire, with open-ended questions designed to facilitate AI-based analysis, provided a fast-paced and balanced evaluation. This reinforces the importance of using AI tools to enhance public institutions' ability to listen to multiple managers, while maintaining a balance with other methods, in order to generate greater reliability alongside innovation.