GENERATIVE ARTIFICIAL INTELLIGENCE AND MICROLEARNING FOR PERSONALIZED TRAINING OF MUNICIPAL PUBLIC EMPLOYEES – DEVELOPMENT, VALIDATION AND IMPACTS ON THE MODERNIZATION OF PUBLIC MANAGEMENT
City Halls; Public Service; Microlearning; Innovation in Public Management; Training; Servers; Municipal; Governance; Technology; Generative Artificial Intelligence.
Institutional fragility and the low capacity for continuous training in small and medium-sized Brazilian municipalities result in inefficiency and regulatory errors, such as in the application of the New Bidding Law. Given this problem, this research proposes the development and validation of a technological solution that uses Generative Artificial Intelligence (GAI) and Microlearning to promote personalized training for public servants. The central objective is to create a scalable and economically viable socio-technical artifact that employs GAI in the production of micro-lessons and adaptive learning paths, overcoming the limitations of traditional training models, which are generic and disconnected from routine. The adopted methodology is quali-quantitative, structured in phases of Design Science Research (DSR), including requirements analysis with Design Thinking, business modeling (TAM-SAM-SOM), and evaluation of a Minimum Viable Product (MVP) in a pilot study in city halls. The scope aims to validate the hypothesis that the personalization of Microlearning, driven by GAI, significantly increases the retention of regulatory knowledge, the perception of competencies, and the procedural agility of the servers. Competitor analysis and socioeconomic modeling indicate the market viability of the platform as a low-cost and highly adherent alternative for people development in the municipal public sector.