Estimulando a Metacognição de Estudantes por meio da Tutoria Virtual com IA Generativa
Resumo
A IA generativa surgiu como uma solução promissora para os sistemas de tutoria. Porém, embora benéfica à primeira vista, características como alta flexibilidade e capacidade de gerar respostas prontas geram riscos para o processo educacional, podendo fomentar dependências que reduzem o pensamento crítico, a criatividade e a capacidade de resolução de problemas dos estudantes. Este artigo propõe o uso da IA generativa com base em um modelo de ensino metacognitivo, o qual incentiva estudantes a refletirem sobre seu próprio processo de aprendizagem por meio do planejamento e da avaliação de suas ações. Como validação, foi usado um modelo analítico em múltiplas camadas, o qual evidencia os potenciais benefícios da abordagem.
Referências
Andriessen, J., Baker, M., & Suthers, D. D. (Eds.). (2013). Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments (Vol. 1). Springer Science & Business Media.
Bai, Y., Li, J., Shen, J., & Zhao, L. (2024). Investigating the efficacy of ChatGPT-3.5 for tutoring in Chinese elementary education settings. IEEE Transactions on Learning Technologies.
Barron, B. (2003). When smart groups fail. The journal of the learning sciences, 12(3), 307-359.
Bauschard, S., & Quidwai, S. (2024). From insight to implementation: How to create your AI school guidance. Available at SSRN.
Chan, C. K. Y., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?. Smart learning environments, 10(1), 60.
Chen, S., Liu, Q., & He, B. (2023, November). A Generative AI-based Teaching Material System Using a Human-In-The-Loop Model. In 2023 International Conference on Intelligent Education and Intelligent Research (IEIR) (pp. 1-8). IEEE.
Chi, M. T. (2009). Active‐constructive‐interactive: A conceptual framework for differentiating learning activities. Topics in cognitive science, 1(1), 73-105.
Cox, Michael T. Metacognition in computation: A selected research review. Artificial intelligence, v. 169, n. 2, p. 104-141, 2005.
Cox, Michael et al. Computational metacognition. arXiv preprint arXiv:2201.12885, 2022.
Dai, Y. (2024). Dual-contrast pedagogy for AI literacy in upper elementary schools. Learning and Instruction, 91, 101899.
De Cremer, D., Bianzino, N. M., & Falk, B. (2023). How generative AI could disrupt creative work. Harvard Business Review, 13.
Degen, J. (2023). The rational speech act framework. Annual Review of Linguistics, 9(1), 519-540.
D’mello, S. K., Craig, S. D., Witherspoon, A., McDaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User modeling and user-adapted interaction, 18, 45-80.
Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3, 5-23.
Freire, P. (1997). Educação “bancária” e educação libertadora. Introdução à psicologia escolar, 3, 61-78.
Giray, L. (2023). Prompt engineering with ChatGPT: a guide for academic writers. Annals of biomedical engineering, 51(12), 2629-2633.
Griffiths, D., Frías-Martínez, E., Tlili, A., & Burgos, D. (2024). A cybernetic perspective on generative AI in education: From transmission to coordination.
Huang, C., & Yu, H. (2024, June). Technological Dialogue and Intelligent Generation: An Empirical Study of Primary Chinese Teachers Using Generative AI for Lesson Preparation. In Proceedings of the 2024 International Conference on Intelligent Education and Computer Technology (pp. 606-611).
Jurenka, I., Kunesch, M., McKee, K. R., Gillick, D., Zhu, S., Wiltberger, S., ... & Ibrahim, L. (2024). Towards responsible development of generative AI for education: An evaluation-driven approach. arXiv preprint arXiv:2407.12687.
Kasonde Ng’andu, F. H., Haambokoma, N., & Tomaida, M. (2013). The Contribution of Behaviourism Theory to Education. Journal: Zambia Journal of Education, 4(1), 5874.
Kumar, R., Rosé, C. P., Wang, Y. C., Joshi, M., & Robinson, A. (2007). Tutorial dialogue as adaptive collaborative learning support. Frontiers in artificial intelligence and applications, 158, 383.
Lee, Y. (2024). Development of Open Large Language Models for Artificial Intelligence Digital Textbooks. TEM Journal, 13(4), 2773-2783.
Lee, G. G., & Zhai, X. (2024). Using ChatGPT for science learning: A study on preservice teachers' lesson planning. IEEE Transactions on Learning Technologies.
Li, M., & Yuan, R. (2022). Enhancing students’ metacognitive development in higher education: A classroom-based inquiry. International Journal of Educational Research, 112, 101947.
Li, M., & Zhang, H. (2024). A Study on the Behavioral Intention of Primary and Secondary School Teachers Using Generative Artificial Intelligence in Teaching.
In Proceedings of the 2024 9th International Conference on Distance Education and Learning (pp. 35-41).
Mahdavi, M. (2014). An overview: Metacognition in education. International Journal of Multidisciplinary and current research, 2(6), 529-535.
Mercer, N., & Littleton, K. (2007). Dialogue and the development of children's thinking: A sociocultural approach. Routledge.
Mittal, U., Sai, S., & Chamola, V. (2024). A comprehensive review on generative ai for education. IEEE Access, 12, 142733-142759.
Nguyen, H. (2023). Role design considerations of conversational agents to facilitate discussion and systems thinking. Computers & Education, 192, 104661.
Santos, R. P. D. (2023). Enhancing physics learning with ChatGPT, bing chat, and bard as agents-to-think-with: a comparative case study. arXiv preprint arXiv:2306.00724.
Stahl, G. (2013). A model of collaborative knowledge-building. In International conference of the learning sciences (pp. 70-77). Psychology Press.
Steffe, L. P., & Gale, J. E. (Eds.). (1995). Constructivism in education. Psychology Press.
Suthers, D. D. (2003). Representational guidance for collaborative inquiry. In Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments (pp. 27-46). Dordrecht: Springer Netherlands.
Tsai, Y. H., Lin, C. H., Hong, J. C., & Tai, K. H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18-29.
Van Eemeren, F. H., Grootendorst, R., & Grootendorst, R. (2004). A systematic theory of argumentation: The pragma-dialectical approach. Cambridge University Press.
Von Aufschnaiter, C., Erduran, S., Osborne, J., & Simon, S. (2008). Arguing to learn and learning to argue: Case studies of how students' argumentation relates to their scientific knowledge. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 45(1), 101-131.
Walker, M., Anand, P., Abbott, R., & Grant, R. (2012, June). Stance classification using dialogic properties of persuasion. In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 592-596).
Xie, W., Li, J., Mu, Y., Zhang, H., Zhao, S., Zheng, X., & Song, K. (2025). The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. International Journal of Asian Language Processing, 2450017.
Zhou, X., & Schofield, L. (2024). Using social learning theories to explore the role of generative artificial intelligence (AI) in collaborative learning. Journal of Learning Development in Higher Education, (30).
Bai, Y., Li, J., Shen, J., & Zhao, L. (2024). Investigating the efficacy of ChatGPT-3.5 for tutoring in Chinese elementary education settings. IEEE Transactions on Learning Technologies.
Barron, B. (2003). When smart groups fail. The journal of the learning sciences, 12(3), 307-359.
Bauschard, S., & Quidwai, S. (2024). From insight to implementation: How to create your AI school guidance. Available at SSRN.
Chan, C. K. Y., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?. Smart learning environments, 10(1), 60.
Chen, S., Liu, Q., & He, B. (2023, November). A Generative AI-based Teaching Material System Using a Human-In-The-Loop Model. In 2023 International Conference on Intelligent Education and Intelligent Research (IEIR) (pp. 1-8). IEEE.
Chi, M. T. (2009). Active‐constructive‐interactive: A conceptual framework for differentiating learning activities. Topics in cognitive science, 1(1), 73-105.
Cox, Michael T. Metacognition in computation: A selected research review. Artificial intelligence, v. 169, n. 2, p. 104-141, 2005.
Cox, Michael et al. Computational metacognition. arXiv preprint arXiv:2201.12885, 2022.
Dai, Y. (2024). Dual-contrast pedagogy for AI literacy in upper elementary schools. Learning and Instruction, 91, 101899.
De Cremer, D., Bianzino, N. M., & Falk, B. (2023). How generative AI could disrupt creative work. Harvard Business Review, 13.
Degen, J. (2023). The rational speech act framework. Annual Review of Linguistics, 9(1), 519-540.
D’mello, S. K., Craig, S. D., Witherspoon, A., McDaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User modeling and user-adapted interaction, 18, 45-80.
Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3, 5-23.
Freire, P. (1997). Educação “bancária” e educação libertadora. Introdução à psicologia escolar, 3, 61-78.
Giray, L. (2023). Prompt engineering with ChatGPT: a guide for academic writers. Annals of biomedical engineering, 51(12), 2629-2633.
Griffiths, D., Frías-Martínez, E., Tlili, A., & Burgos, D. (2024). A cybernetic perspective on generative AI in education: From transmission to coordination.
Huang, C., & Yu, H. (2024, June). Technological Dialogue and Intelligent Generation: An Empirical Study of Primary Chinese Teachers Using Generative AI for Lesson Preparation. In Proceedings of the 2024 International Conference on Intelligent Education and Computer Technology (pp. 606-611).
Jurenka, I., Kunesch, M., McKee, K. R., Gillick, D., Zhu, S., Wiltberger, S., ... & Ibrahim, L. (2024). Towards responsible development of generative AI for education: An evaluation-driven approach. arXiv preprint arXiv:2407.12687.
Kasonde Ng’andu, F. H., Haambokoma, N., & Tomaida, M. (2013). The Contribution of Behaviourism Theory to Education. Journal: Zambia Journal of Education, 4(1), 5874.
Kumar, R., Rosé, C. P., Wang, Y. C., Joshi, M., & Robinson, A. (2007). Tutorial dialogue as adaptive collaborative learning support. Frontiers in artificial intelligence and applications, 158, 383.
Lee, Y. (2024). Development of Open Large Language Models for Artificial Intelligence Digital Textbooks. TEM Journal, 13(4), 2773-2783.
Lee, G. G., & Zhai, X. (2024). Using ChatGPT for science learning: A study on preservice teachers' lesson planning. IEEE Transactions on Learning Technologies.
Li, M., & Yuan, R. (2022). Enhancing students’ metacognitive development in higher education: A classroom-based inquiry. International Journal of Educational Research, 112, 101947.
Li, M., & Zhang, H. (2024). A Study on the Behavioral Intention of Primary and Secondary School Teachers Using Generative Artificial Intelligence in Teaching.
In Proceedings of the 2024 9th International Conference on Distance Education and Learning (pp. 35-41).
Mahdavi, M. (2014). An overview: Metacognition in education. International Journal of Multidisciplinary and current research, 2(6), 529-535.
Mercer, N., & Littleton, K. (2007). Dialogue and the development of children's thinking: A sociocultural approach. Routledge.
Mittal, U., Sai, S., & Chamola, V. (2024). A comprehensive review on generative ai for education. IEEE Access, 12, 142733-142759.
Nguyen, H. (2023). Role design considerations of conversational agents to facilitate discussion and systems thinking. Computers & Education, 192, 104661.
Santos, R. P. D. (2023). Enhancing physics learning with ChatGPT, bing chat, and bard as agents-to-think-with: a comparative case study. arXiv preprint arXiv:2306.00724.
Stahl, G. (2013). A model of collaborative knowledge-building. In International conference of the learning sciences (pp. 70-77). Psychology Press.
Steffe, L. P., & Gale, J. E. (Eds.). (1995). Constructivism in education. Psychology Press.
Suthers, D. D. (2003). Representational guidance for collaborative inquiry. In Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments (pp. 27-46). Dordrecht: Springer Netherlands.
Tsai, Y. H., Lin, C. H., Hong, J. C., & Tai, K. H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18-29.
Van Eemeren, F. H., Grootendorst, R., & Grootendorst, R. (2004). A systematic theory of argumentation: The pragma-dialectical approach. Cambridge University Press.
Von Aufschnaiter, C., Erduran, S., Osborne, J., & Simon, S. (2008). Arguing to learn and learning to argue: Case studies of how students' argumentation relates to their scientific knowledge. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 45(1), 101-131.
Walker, M., Anand, P., Abbott, R., & Grant, R. (2012, June). Stance classification using dialogic properties of persuasion. In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 592-596).
Xie, W., Li, J., Mu, Y., Zhang, H., Zhao, S., Zheng, X., & Song, K. (2025). The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. International Journal of Asian Language Processing, 2450017.
Zhou, X., & Schofield, L. (2024). Using social learning theories to explore the role of generative artificial intelligence (AI) in collaborative learning. Journal of Learning Development in Higher Education, (30).
Publicado
24/11/2025
Como Citar
CAMELO, Vitória C. S.; SIEBRA, Clauirton A..
Estimulando a Metacognição de Estudantes por meio da Tutoria Virtual com IA Generativa. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 523-537.
DOI: https://doi.org/10.5753/sbie.2025.12488.
