Personalized Study Guide with LLM and Multi-Agent Architecture

• Kleber José Araujo Galvão Filho UFAL
• Ranilson Oscar Araújo Paiva UFAL
• Giseldo da Silva Neo IFAL
• Alana Viana Borges da Silva Neo IFMS
• Evandro de Barros Costa UFAL
• Olival de Gusmão Freitas Júnior UFAL

DOI: https://doi.org/10.5753/semish.2025.9254

Abstract

Teacher overload and the need for personalization are constant challenges in technical education. This study proposes to mitigate such issues through the use of a recommendation system based on large language models (LLMs) and intelligent agents. The tool collects data provided by students and automatically generates study plans adapted to each learner’s time availability and focus. An evaluation with 16 students, based on the Technology Acceptance Model (TAM), revealed high acceptance, with emphasis on interface clarity and ease of use. The system contributes to more effective pedagogical practices, reduces teacher workload, and preserves critical instructional mediation. It is a viable and scalable solution for contemporary educational contexts.

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