Multi-Agent Systems and Artificial Intelligence supporting students throughout academic life
Resumo
Esse artigo descreve uma modelagem organizacional de metaversidade, e a implementação do Sistema Multi-Agentes com Inteligência Artificial. O intuito é explorar uma abordagem mais centrada no estudante, amparando-o ao longo da vida acadêmica. Essa proposta torna-se um meio complementar ao ensino tradicional, procurando engajar o aluno em um ambiente mais amigável e atencioso às suas necessidades. Abordagens similares são conferidas. Reflexões são reportadas sobre as contribuições, fragilidades, e perpectivas da abordagem. Essa abordagem apoia-se em Agent-Group-Role Model, MaDkit, e Large Language Models.
Palavras-chave:
Metaversity, Multi-Agent Systems, Artificial Intelligence
Referências
Abrami, G., Barreteau, O., and Cernesson, F. (2002). An Agent Group Role based modelling framework for participative water management support. In International Environmental Modelling and Software Society IEMSs , volume 3, Lugano , Switzerland. IEMSs .
Afzaal, M., Nouri, J., and Aayesha, A. (2023). A transformer-based approach for the automatic generation of concept-wise exercises to provide personalized learning support to students. In Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., and Papathoma, T., editors, Responsive and Sustainable Educational Futures, pages 16–31, Cham. Springer Nature Switzerland.
Akli, A. and Chougdali, K. (2025). Iota-assisted self-sovereign identity framework for decentralized authentication and secure data sharing. IEEE Access, 13:80191–80205.
Bartoletti, M., Benetollo, L., Bugliesi, M., Crafa, S., Sasso, G. D., Pettinau, R., Pinna, A., Piras, M., Rossi, S., Salis, S., Spanò, A., Tkachenko, V., Tonelli, R., and Zunino, R. (2025). Smart contract languages: A comparative analysis. Future Generation Computer Systems, 164:107563.
BDI4JADE (2021). Bdi4jade: Bdi framework para jade v2.0. Accessed August, 2025.
Bellifemine, F. L., Caire, G., and Greenwood, D. (2007). Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology). John Wiley & Sons, Inc., Hoboken, NJ, USA.
Chebout, M. S., Marir, T., and Gupta, V. (2023). Noragr: a normative agent groupe role model for organizational centered open multi-agent systems. In 2023 Third International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), pages 1–5.
Ferber, J. and Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160), pages 128–135.
GDPR (2018). General data protection regulation. [link].
Gutknecht, O. (2001). Proposition d’un modèle organisationnel générique de systèmes multi-agents et examen de ses conséquences formelles, implémentatoires et méthologiques. Theses, Université Montpellier II - Sciences et Techniques du Languedoc.
Kaledio, P., Robert, A., and Frank, L. (2024). The impact of artificial intelligence on students’ learning experience. SSRN Electronic Journal.
Kompan, M., Kassak, O., and Bielikova, M. (2019). The short-term user modeling for predictive applications. Journal on Data Semantics, 8.
Loewen, S., Crowther, D., Isbell, D. R., Kim, K. M., Maloney, J., Miller, Z. F., and Rawal, H. (2019). Mobile-assisted language learning: A duolingo case study. ReCALL, 31(3):293–311.
MaDkitTool (2025). Madkit tool. Accessed August, 2025.
Minh, D., Wang, H. X., Li, Y. F., and Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev., 55(5):3503–3568.
Nadrljanski, M., Vukić, , and Nadrljanski, D. (2018). Multi-agent systems in e-learning. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 0990–0995.
NET-LOGO (2025). Net-logo. Accessed August, 2025.
Nóbrega, G., Pains, A., and Cruz, F. (2024). Uma sociedade de companions inteligentes na metaversidade para incrementar a aprendizagem ao longo da vida. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 3085–3096, Porto Alegre, RS, Brasil. SBC.
Olugbade, D., Ojo, O. A., and Tolorunleke, A. E. (2023). Challenges and limitations of moodle lms in handling large-scale projects: West-african universities lecturers’ perspective. Journal of Educational Technology and Instruction, 2(2):47–66.
Parunak, H. V. D. and Odell, J. (2001). Representing social structures in uml. In Proceedings of the Fifth International Conference on Autonomous Agents, AGENTS ’01, page 100–101, New York, NY, USA. Association for Computing Machinery.
Peng, Z.-P. and Peng, H. (2005). An improved agent/group/role meta-model for building multti-agen systems. In 2005 International Conference on Machine Learning and Cybernetics, volume 1, pages 287–292.
Pokahr, A., Braubach, L., Haubeck, C., and Ladiges, J. (2014). Programming bdi agents with pure java. In Müller, J. P., Weyrich, M., and Bazzan, A. L. C., editors, Multiagent System Technologies, pages 216–233, Cham. Springer International Publishing.
Ross, E., Kansal, Y., Renzella, J., Vassar, A., and Taylor, A. (2025). Supervised fine-tuning llms to behave as pedagogical agents in programming education.
Seddari, N., Belaoued, M., and Bougueroua, S. (2017). Agent/group/role organizational model to simulate an industrial control system. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11(10):2376–2385.
Selker, T. (1994). Coach: a teaching agent that learns. Commun. ACM, 37(7):92–99.
Shanmugasundaram, M. and Tamilarasu, A. (2023). The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review. Frontiers in Cognition, Volume 2 - 2023.
Silva, O., Souza, T., Duda, J., Barros, B., and Nóbrega, G. (2024). Você decide quem pod: empoderando a/o estudante de computação quanto à propriedade de seus dados. In Anais do IV Simpósio Brasileiro de Educação em Computação, pages 367–374, Porto Alegre, RS, Brasil. SBC.
SoftwareTools (2025). Software tools. Accessed August, 2025.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. Wiley Publishing, 2nd edition.
Afzaal, M., Nouri, J., and Aayesha, A. (2023). A transformer-based approach for the automatic generation of concept-wise exercises to provide personalized learning support to students. In Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., and Papathoma, T., editors, Responsive and Sustainable Educational Futures, pages 16–31, Cham. Springer Nature Switzerland.
Akli, A. and Chougdali, K. (2025). Iota-assisted self-sovereign identity framework for decentralized authentication and secure data sharing. IEEE Access, 13:80191–80205.
Bartoletti, M., Benetollo, L., Bugliesi, M., Crafa, S., Sasso, G. D., Pettinau, R., Pinna, A., Piras, M., Rossi, S., Salis, S., Spanò, A., Tkachenko, V., Tonelli, R., and Zunino, R. (2025). Smart contract languages: A comparative analysis. Future Generation Computer Systems, 164:107563.
BDI4JADE (2021). Bdi4jade: Bdi framework para jade v2.0. Accessed August, 2025.
Bellifemine, F. L., Caire, G., and Greenwood, D. (2007). Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology). John Wiley & Sons, Inc., Hoboken, NJ, USA.
Chebout, M. S., Marir, T., and Gupta, V. (2023). Noragr: a normative agent groupe role model for organizational centered open multi-agent systems. In 2023 Third International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), pages 1–5.
Ferber, J. and Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160), pages 128–135.
GDPR (2018). General data protection regulation. [link].
Gutknecht, O. (2001). Proposition d’un modèle organisationnel générique de systèmes multi-agents et examen de ses conséquences formelles, implémentatoires et méthologiques. Theses, Université Montpellier II - Sciences et Techniques du Languedoc.
Kaledio, P., Robert, A., and Frank, L. (2024). The impact of artificial intelligence on students’ learning experience. SSRN Electronic Journal.
Kompan, M., Kassak, O., and Bielikova, M. (2019). The short-term user modeling for predictive applications. Journal on Data Semantics, 8.
Loewen, S., Crowther, D., Isbell, D. R., Kim, K. M., Maloney, J., Miller, Z. F., and Rawal, H. (2019). Mobile-assisted language learning: A duolingo case study. ReCALL, 31(3):293–311.
MaDkitTool (2025). Madkit tool. Accessed August, 2025.
Minh, D., Wang, H. X., Li, Y. F., and Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev., 55(5):3503–3568.
Nadrljanski, M., Vukić, , and Nadrljanski, D. (2018). Multi-agent systems in e-learning. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 0990–0995.
NET-LOGO (2025). Net-logo. Accessed August, 2025.
Nóbrega, G., Pains, A., and Cruz, F. (2024). Uma sociedade de companions inteligentes na metaversidade para incrementar a aprendizagem ao longo da vida. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 3085–3096, Porto Alegre, RS, Brasil. SBC.
Olugbade, D., Ojo, O. A., and Tolorunleke, A. E. (2023). Challenges and limitations of moodle lms in handling large-scale projects: West-african universities lecturers’ perspective. Journal of Educational Technology and Instruction, 2(2):47–66.
Parunak, H. V. D. and Odell, J. (2001). Representing social structures in uml. In Proceedings of the Fifth International Conference on Autonomous Agents, AGENTS ’01, page 100–101, New York, NY, USA. Association for Computing Machinery.
Peng, Z.-P. and Peng, H. (2005). An improved agent/group/role meta-model for building multti-agen systems. In 2005 International Conference on Machine Learning and Cybernetics, volume 1, pages 287–292.
Pokahr, A., Braubach, L., Haubeck, C., and Ladiges, J. (2014). Programming bdi agents with pure java. In Müller, J. P., Weyrich, M., and Bazzan, A. L. C., editors, Multiagent System Technologies, pages 216–233, Cham. Springer International Publishing.
Ross, E., Kansal, Y., Renzella, J., Vassar, A., and Taylor, A. (2025). Supervised fine-tuning llms to behave as pedagogical agents in programming education.
Seddari, N., Belaoued, M., and Bougueroua, S. (2017). Agent/group/role organizational model to simulate an industrial control system. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11(10):2376–2385.
Selker, T. (1994). Coach: a teaching agent that learns. Commun. ACM, 37(7):92–99.
Shanmugasundaram, M. and Tamilarasu, A. (2023). The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review. Frontiers in Cognition, Volume 2 - 2023.
Silva, O., Souza, T., Duda, J., Barros, B., and Nóbrega, G. (2024). Você decide quem pod: empoderando a/o estudante de computação quanto à propriedade de seus dados. In Anais do IV Simpósio Brasileiro de Educação em Computação, pages 367–374, Porto Alegre, RS, Brasil. SBC.
SoftwareTools (2025). Software tools. Accessed August, 2025.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. Wiley Publishing, 2nd edition.
Publicado
24/11/2025
Como Citar
ROSA, Yasmim; SERRANO, Milene; SERRANO, Maurício.
Multi-Agent Systems and Artificial Intelligence supporting students throughout academic life. In: WORKSHOP SOBRE DESCENTRALIZAÇÃO EM INFORMÁTICA NA EDUCAÇÃO (WDESCENTRAIE), 1. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 01-07.
DOI: https://doi.org/10.5753/wdescentraie.2025.15922.
