Proposal for a Generative Artificial Intelligence Model for Administrative Management in Higher Education Institutions in the Central Jungle, Junín

Abstract

To propose a generative artificial intelligence model for administrative management in higher education institutions in the Central Jungle region of Junín. Methodology: The study was conducted using a systematic review with a qualitative-documentary approach and a descriptive-interpretative scope, adhering to PRISMA criteria for the selection, screening, and analysis of sources. The sample consisted of 22 scientific articles from indexed databases and academic repositories, selected based on thematic relevance, currency, and methodological quality. Results: Findings demonstrated that generative AI enables the automation of procedures, reduces errors in documents and records, enhances institutional transparency, and strengthens decision-making through data analytics. Furthermore, these technologies were found to help reduce workloads, optimize resources, and establish more agile and traceable administrative processes. The evidence revealed a regional trend toward adopting intelligent systems to support institutional modernization. Conclusions: The study determined that generative artificial intelligence serves as a strategic resource for transforming administrative management in contexts facing technological gaps, fostering efficiency, document consistency, and greater institutional responsiveness. Additionally, the study highlights the need for further research into adapting the model to limited infrastructures, training administrative staff, and addressing ethical challenges associated with automated decision-making, thereby opening new avenues of study to strengthen digital transformation in education.