Guia de estudo personalizado com LLM e arquitetura multi-agente
Resumo
A sobrecarga docente e a necessidade de personalização representam desafios constantes no ensino técnico. Este estudo propõe minimizar tais problemas com o uso de um sistema de recomendação baseado em LLMs e agentes inteligentes. A ferramenta coleta dados fornecidos pelos estudantes e gera automaticamente planos de estudo adaptados ao tempo e foco de cada um. A avaliação com 16 alunos, baseada no modelo TAM, revelou alta aceitação, com destaque para a clareza da interface e facilidade de uso. O sistema contribui para práticas pedagógicas mais eficazes, reduz a carga do professor e mantém sua mediação crítica. Trata-se de uma solução viável e escalável para o contexto educacional contemporâneo.Referências
Bhatt, S. M., Verbert, K., and Van Den Noortgate, W. (2025). Teachercentric educational recommender systems in K12 practice: Usage and evaluation. Heliyon, 11(2):e42012.
Chen, E., Lee, J.-E., Lin, J., and Koedinger, K. (2024). GPTutor: Great personalized tutor with Large Language Models for personalized learning content generation. arXiv preprint arXiv:2407.09484.
Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., and Papadopoulos, G. A. (2023). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in greece. Education and Information Technologies, 28:6453–6483.
Dantas, E., Costa, A. A. M., Vinicius, M., Perkusich, M. B., de Almeida, H. O., and Perkusich, A. (2019). An effort estimation support tool for agile software development: An empirical evaluation. In Proceedings of the 31st International Conference on Software Engineering and Knowledge Engineering (SEKE), pages 82–116.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319–340.
Ferreira, L. C. (2023). Pesquisa mostra que 71% dos professores estão estressados. Agência Brasil (Notícia).
Groq Inc. (2024). The future of AI is agentic... and GROQ. White paper.
Luo, Y. (2024). The use of chatgpt in education: A new path to personalized instruction. Science Insights Education Frontiers, 25(1).
Ma, Y., Wang, L., Zhang, J., Liu, F., and Jiang, Q. (2023). A personalized learning path recommendation method incorporating multi-algorithm. Applied Sciences, 13(10):5946.
Mai, D. T. T., Da, C. V., and Hanh, N. V. (2024). The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach. Frontiers in Education, 9.
Marangunić, N. and Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1):81–95.
Nascimento, A. L., Miranda, A. K. P., da Silva, L. S., Lima, I. Â. d. S. B., Pereira, C. L., Santos, I. V. M., and Feitosa, P. d. C. (2023). Ser docente na pandemia: vivência, sobrecarga e desafios de professores do ensino básico, técnico e tecnológico. Saúde Coletiva (Barueri), 13(149):9417–9427.
Neil, D. (2024). CrewAI based DSA Tutor: Personalized learning with multi-agent systems. Analytics Vidhya Blog.
Ozamiz-Etxebarria, N., Legorburu, I. M., Lipnicki, D. M., and Idoiaga, N. (2023). Prevalence of burnout among teachers during the covid-19 pandemic: A meta-analysis. International Journal of Environmental Research and Public Health, 20(6):4866.
Silva, F. L. d., Slodkowski, B. K., Silva, K. K. A. d., and Cazella, S. C. (2023). A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities. Education and Information Technologies, 28:3289–3328.
Wang, X. J., Lee, C., and Mutlu, B. (2025). Learnmate: Enhancing online education with llm-powered personalized learning plans and support. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25), April 26-May 1, 2025, Yokohama, Japan.
Winland, V., Syed, M., and Gutowska, A. (2024). What is CrewAI? IBM Technology Blog.
Zabala, A. (1998). A prática educativa: como ensinar. Artmed.
Zach, A. (2023). A simple explanation of internal consistency. [link]. Acesso em: 23 mar. 2025.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2):64–70.
Chen, E., Lee, J.-E., Lin, J., and Koedinger, K. (2024). GPTutor: Great personalized tutor with Large Language Models for personalized learning content generation. arXiv preprint arXiv:2407.09484.
Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., and Papadopoulos, G. A. (2023). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in greece. Education and Information Technologies, 28:6453–6483.
Dantas, E., Costa, A. A. M., Vinicius, M., Perkusich, M. B., de Almeida, H. O., and Perkusich, A. (2019). An effort estimation support tool for agile software development: An empirical evaluation. In Proceedings of the 31st International Conference on Software Engineering and Knowledge Engineering (SEKE), pages 82–116.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319–340.
Ferreira, L. C. (2023). Pesquisa mostra que 71% dos professores estão estressados. Agência Brasil (Notícia).
Groq Inc. (2024). The future of AI is agentic... and GROQ. White paper.
Luo, Y. (2024). The use of chatgpt in education: A new path to personalized instruction. Science Insights Education Frontiers, 25(1).
Ma, Y., Wang, L., Zhang, J., Liu, F., and Jiang, Q. (2023). A personalized learning path recommendation method incorporating multi-algorithm. Applied Sciences, 13(10):5946.
Mai, D. T. T., Da, C. V., and Hanh, N. V. (2024). The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach. Frontiers in Education, 9.
Marangunić, N. and Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1):81–95.
Nascimento, A. L., Miranda, A. K. P., da Silva, L. S., Lima, I. Â. d. S. B., Pereira, C. L., Santos, I. V. M., and Feitosa, P. d. C. (2023). Ser docente na pandemia: vivência, sobrecarga e desafios de professores do ensino básico, técnico e tecnológico. Saúde Coletiva (Barueri), 13(149):9417–9427.
Neil, D. (2024). CrewAI based DSA Tutor: Personalized learning with multi-agent systems. Analytics Vidhya Blog.
Ozamiz-Etxebarria, N., Legorburu, I. M., Lipnicki, D. M., and Idoiaga, N. (2023). Prevalence of burnout among teachers during the covid-19 pandemic: A meta-analysis. International Journal of Environmental Research and Public Health, 20(6):4866.
Silva, F. L. d., Slodkowski, B. K., Silva, K. K. A. d., and Cazella, S. C. (2023). A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities. Education and Information Technologies, 28:3289–3328.
Wang, X. J., Lee, C., and Mutlu, B. (2025). Learnmate: Enhancing online education with llm-powered personalized learning plans and support. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25), April 26-May 1, 2025, Yokohama, Japan.
Winland, V., Syed, M., and Gutowska, A. (2024). What is CrewAI? IBM Technology Blog.
Zabala, A. (1998). A prática educativa: como ensinar. Artmed.
Zach, A. (2023). A simple explanation of internal consistency. [link]. Acesso em: 23 mar. 2025.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2):64–70.
Publicado
20/07/2025
Como Citar
GALVÃO FILHO, Kleber José Araujo; PAIVA, Ranilson Oscar Araújo; NEO, Giseldo da Silva; NEO, Alana Viana Borges da Silva; COSTA, Evandro de Barros; FREITAS JÚNIOR, Olival de Gusmão.
Guia de estudo personalizado com LLM e arquitetura multi-agente. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 52. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 561-572.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2025.9254.
