How the Educator 5.0 Will Not Be Replaced by AI: An Adaptive Microlearning Architecture Based on Augmented Intelligence

• Emanuel Ferreira UNIPAMPA
• Silvio E. Quincozes UNIPAMPA / UFU
• Matheus M. Ciocca UNIPAMPA
• Gabriel Silva UNIPAMPA
• Paulo Souza UNIPAMPA
• Gilleanes Guedes UNIPAMPA
• Williamson Silva UFU / UFCA

DOI: https://doi.org/10.5753/sbie.2025.12439

Resumo

Education 5.0 proposes a human-centered model that integrates personalization, technology, and the active roles of both instructors and students. However, current educational tools still face limitations such as a lack of flexibility and monitoring capabilities. This work proposes an educational architecture that supports the Educator 5.0 in generating, customizing, and monitoring adaptive microlearning paths. The concept of augmented intelligence enhances the educator’s role as a critical curator of AI-generated content, based on their teaching materials, and as a mediator of formative learning experiences. As a result, learners obtain customized, real-time adaptive content blocks, ensuring learner diversity is respected and enabling the generation of pedagogical alerts.

Referências

Agarwal, N. K. and Islam, M. A. (2015). Knowledge retention and transfer: how libraries manage employees leaving and joining. Vine, 45(2):150–171.

Agarwal, V., Verma, P., and Ferrigno, G. (2025). Education 5.0 challenges and sustainable development goals in emerging economies: A mixed-method approach. Technology in Society, 81:102814.

Alfirević, N., Praničević, D. G., and Mabić, M. (2024). Custom-trained large language models as open educational resources: An exploratory research of a business management educational chatbot in croatia and bosnia and herzegovina. Sustainability, 16(12).

Alhawiti, K. M. (2014). Natural language processing and its use in education. International Journal of Advanced Computer Science and Applications, 5(12).

Bezerra, E. T., Caitano, T. F., Gonçalves, R., Damacena, R., Cortes, V. R. R., and Scabeni, R. S. (2024). O impacto do uso da inteligência artificial nos processos de ensino e aprendizagem. Revista Ibero-Americana de Humanidades, Ciências e Educação, 10(7):1211–1220.

Carvalho, M., Nevado, R., and Menezes, C. (2005). Arquiteturas pedagógicas para educação a distância: Concepções e suporte telemático. Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), 1(1):351–360.

Cuellar, O., Contero, M., and Hincapié, M. (2025). Personalized and timely feedback in online education: Enhancing learning with deep learning and large language models. Multimodal Technologies and Interaction, 9(5).

Damaševičius, R. (2025). Quantum pedagogy: A 21st century framework for education 5.0. In Impacts of AI on Students and Teachers in Education 5.0, pages 479–522. IGI Global Scientific Publishing.

Franqueira, A. d. S., da Silva, K. G., da Silva, L. G., Marcondes, P., Barbosa, R. A., and da Silva, R. G. (2024). Inteligência artificial na educação: personalização e adaptatividade no processo de ensino-aprendizagem. CONTRIBUCIONES A LAS CIENCIAS SOCIALES, 17(9).

Isotani, S., Challco, G. C., Cruz, W. M., and Bittencourt, I. I. (2025). Inteligência artificial generativa na educação. Nota técnica, IA.Edu, NEES/UFAL, Fundação Tellescom. Disponível em: [link].

Jyothy, S. N., Kolil, V. K., Raman, R., and and, K. A. (2024). Exploring large language models as an integrated tool for learning, teaching, and research through the fogg behavior model: a comprehensive mixed-methods analysis. Cogent Engineering, 11(1):2353494.

Kohnke, L., Zou, D., and Xie, H. (2025). Microlearning and generative ai for pre-service teacher education: a qualitative case study. Education and Information Technologies.

Kwon, T. (2022). Interfaces for personalized language learning with generative language models. Master of science, Columbia University.

Le Cunff, A.-L., Martis, B.-L., Glover, C., Ahmed, E., Ford, R., Giampietro, V., and Dommett, E. J. (2025). Cognitive load and neurodiversity in online education: a preliminary framework for educational research and policy. Frontiers in Education, Volume 9 - 2024.

Levy, M. (2011). Knowledge retention: minimizing organizational business loss. Journal of knowledge management, 15(4):582–600.

Mrabet, J., Studholme, R., and Thompson, N. (2024). Educational innovation in the information age: Ai tutoring and micro-learning. In Fostering Industry-Academia Partnerships for Innovation-Driven Trade, pages 176–212. IGI Global.

Pestana, D. M. A. D. A. and Santos, D. (2023). Inteligência artificial na educação: potencialidades e desafios. SCIAS-Educação, Comunicação e Tecnologia, 5(2):74–89.

Silva, A. and Janes, D. (2020). Exploring the role of artificial intelligence in education: A comprehensive perspective. Review of Artificial Intelligence in Education, 1:e05.

Sirwan Mohammed, G., Wakil, K., and Sirwan Nawroly, S. (2018). The effectiveness of microlearning to improve students’ learning ability. International Journal of Educational Research Review, 3(3):32–38.

Toivonen, T., Jormanainen, I., and Tukiainen, M. (2019). Augmented intelligence in educational data mining. Smart Learning Environments, 6(1):10.

Wang, H. (2025). Leveraging machine learning for personalised learning, automated feedback, and predictive analytics in college english education. International Journal of Information and Communication Technology, 26(16):16–37.

Zhu, Y., Ye, Z., Yuan, Y., Tang, W., Yu, C., and Shi, Y. (2025). Autopbl: An llm-powered platform to guide and support individual learners through self project-based learning. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pages 1–26.