Uma Proposta de Sistema Tutor Inteligente para a Disciplina Cálculo Diferencial
Resumo
Disciplinas de Cálculo Diferencial impõem elevada carga conceitual e demandam acompanhamento docente contínuo. Este artigo apresenta o Solve With Me, um Sistema Tutor Inteligente que integra geração automática de questões por IA generativa, resolução de atividades de múltipla escolha e agrupamento de perfis de aprendizagem por K-Means. Trinta questões geradas por dois modelos foram avaliadas por dois professores quanto à corretude, clareza e relevância, obtendo boa aceitação (médias de 4,03 e 3,93 em 5) e concordância substancial sobre a aprovação; a corretude matemática foi o principal ponto de atenção. Dados fictícios de uso ilustram a viabilidade técnica do agrupamento para apoiar decisões pedagógicas. Espera-se que a ferramenta contribua para o aprimoramento do ensino de Cálculo e para a redução de evasão e reprovação.
Palavras-chave:
Sistema Tutor Inteligente, IA Generativa, Cálculo Diferencial
Referências
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Cheng, C., Huang, Z., Zhao, G., Guo, Y., Lin, X., Wu, J., Li, X., and Wang, S. (2025). From objectives to questions: A planning-based framework for educational mathematical question generation. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12836–12856, Vienna, Austria. Association for Computational Linguistics.
Feng, W., Lee, J., McNichols, H., Scarlatos, A., Smith, D., Woodhead, S., Ornelas, N., and Lan, A. (2024a). Exploring automated distractor generation for math multiple-choice questions via large language models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3067–3082, Mexico City, Mexico. Association for Computational Linguistics.
Feng, W., Lee, J., McNichols, H., Scarlatos, A., Smith, D., Woodhead, S., Ornelas, N. O., and Lan, A. (2024b). Exploring automated distractor generation for math multiple-choice questions via large language models. In Findings of the Association for Computational Linguistics: NAACL 2024.
Ferreira-Mello, R., André, M., Pinheiro, A., Costa, E., and Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6):e1332.
Grévisse, C., Pavlou, M. A. S., and Schneider, J.-G. (2024). Docimological quality analysis of llm-generated multiple choice questions in computer science and medicine. SN Computer Science.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., and Kasneci, G. (2023a). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103:102274.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., and Kasneci, G. (2023b). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103:102274.
Kurdi, G., Leo, J., Parsia, B., et al. (2020a). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1):121–204.
Kurdi, G., Leo, J., Parsia, B., Sattler, U., and Al-Emari, S. (2020b). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30:121–204.
Kurdi, G., Leo, J., Parsia, B., Sattler, U., and Al-Emari, S. (2020c). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30:121–204.
Lee, J., Smith, D., Woodhead, S., and Lan, A. (2024a). Math multiple choice question generation via human-large language model collaboration. In Proceedings of the 17th International Conference on Educational Data Mining.
Lee, J., Smith, D., Woodhead, S., and Lan, A. (2024b). Math multiple choice question generation via human-large language model collaboration. In Proceedings of the 17th International Conference on Educational Data Mining, pages 941–946, Atlanta, Georgia, USA. International Educational Data Mining Society.
Nogueira, J. V. d. C. M. F., Castro, J. A. P. d., Larcher, L. O., Ferreira, R. V. J., Barbosa, B. T., and Souza, J. F. d. (2025). Uma análise de qualidade do uso de grandes modelos de linguagem para geração automática de itens avaliativos em português. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação (SBIE 2025), pages 235–248. SBC.
Quincozes, C. B., Molinos, D., Araújo, R. D., Quincozes, S., and Guedes, G. T. A. (2025). Engenharia de prompt para a geração automatizada de questões assistida por llms: Uma análise comparativa. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação (SBIE 2025), pages 1347–1360. SBC.
Santos, M., Barros, A., Santos, E., Silva, J., Isotani, S., Bittencourt, I., Macario, V., Rodrigues, L., and Dermeval, D. (2025). Geração de questões com llms leves: Um estudo inicial sobre a percepção de educadores. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, pages 1636–1647, Porto Alegre, RS, Brasil. SBC.
SANTOS, R. D. d., MAGALHÃES, W. B. C., ANJOS, A. L. C. d., and LETTI, A. G. (2024). Evasão e retenção dos cursos de licenciatura em matemática na bahia no período de 2010 a 2022. In Anais do Seminário Nacional de Políticas Públicas, Gestão e Práxis Educacional, pages 189–199, Vitória da Conquista.
Scarlatos, A., Feng, W., Smith, D., Woodhead, S., and Lan, A. (2024a). Improving automated distractor generation for math multiple-choice questions with overgenerate-and-rank. In Kochmar, E., Bexte, M., Burstein, J., Horbach, A., Laarmann-Quante, R., Tack, A., Yaneva, V., and Yuan, Z., editors, Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 222–231, Mexico City, Mexico. Association for Computational Linguistics.
Scarlatos, A., Feng, W., Smith, D., Woodhead, S., and Lan, A. (2024b). Improving automated distractor generation for math multiple-choice questions with overgenerate-and-rank. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 222–231, Mexico City, Mexico. Association for Computational Linguistics.
Tall, D. (1993). Students' difficulties in calculus. In Proceedings of Working Group 3 on Students' Difficulties in Calculus, pages 13–28.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4):197–221.
Wang, L., Song, R., Guo, W., and Yang, H. (2025). Exploring prompt pattern for generative artificial intelligence in automatic question generation. Interactive Learning Environments, 33(3):2559–2584.
Woolf, B. P. (2009). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann, Burlington.
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., and Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1):90–112.
BRASIL (2023). Notas sobre o brasil no pisa 2022.
Carbonell, J. R. (1970). Ai in cai: An artificial-intelligence approach to computer-assisted instruction. In IEEE Transactions on Man-Machine Systems, volume 11, pages 190–202.
Cheng, C., Huang, Z., Zhao, G., Guo, Y., Lin, X., Wu, J., Li, X., and Wang, S. (2025). From objectives to questions: A planning-based framework for educational mathematical question generation. In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12836–12856, Vienna, Austria. Association for Computational Linguistics.
Feng, W., Lee, J., McNichols, H., Scarlatos, A., Smith, D., Woodhead, S., Ornelas, N., and Lan, A. (2024a). Exploring automated distractor generation for math multiple-choice questions via large language models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3067–3082, Mexico City, Mexico. Association for Computational Linguistics.
Feng, W., Lee, J., McNichols, H., Scarlatos, A., Smith, D., Woodhead, S., Ornelas, N. O., and Lan, A. (2024b). Exploring automated distractor generation for math multiple-choice questions via large language models. In Findings of the Association for Computational Linguistics: NAACL 2024.
Ferreira-Mello, R., André, M., Pinheiro, A., Costa, E., and Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6):e1332.
Grévisse, C., Pavlou, M. A. S., and Schneider, J.-G. (2024). Docimological quality analysis of llm-generated multiple choice questions in computer science and medicine. SN Computer Science.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., and Kasneci, G. (2023a). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103:102274.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., and Kasneci, G. (2023b). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103:102274.
Kurdi, G., Leo, J., Parsia, B., et al. (2020a). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1):121–204.
Kurdi, G., Leo, J., Parsia, B., Sattler, U., and Al-Emari, S. (2020b). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30:121–204.
Kurdi, G., Leo, J., Parsia, B., Sattler, U., and Al-Emari, S. (2020c). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30:121–204.
Lee, J., Smith, D., Woodhead, S., and Lan, A. (2024a). Math multiple choice question generation via human-large language model collaboration. In Proceedings of the 17th International Conference on Educational Data Mining.
Lee, J., Smith, D., Woodhead, S., and Lan, A. (2024b). Math multiple choice question generation via human-large language model collaboration. In Proceedings of the 17th International Conference on Educational Data Mining, pages 941–946, Atlanta, Georgia, USA. International Educational Data Mining Society.
Nogueira, J. V. d. C. M. F., Castro, J. A. P. d., Larcher, L. O., Ferreira, R. V. J., Barbosa, B. T., and Souza, J. F. d. (2025). Uma análise de qualidade do uso de grandes modelos de linguagem para geração automática de itens avaliativos em português. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação (SBIE 2025), pages 235–248. SBC.
Quincozes, C. B., Molinos, D., Araújo, R. D., Quincozes, S., and Guedes, G. T. A. (2025). Engenharia de prompt para a geração automatizada de questões assistida por llms: Uma análise comparativa. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação (SBIE 2025), pages 1347–1360. SBC.
Santos, M., Barros, A., Santos, E., Silva, J., Isotani, S., Bittencourt, I., Macario, V., Rodrigues, L., and Dermeval, D. (2025). Geração de questões com llms leves: Um estudo inicial sobre a percepção de educadores. In Anais do XXXVI Simpósio Brasileiro de Informática na Educação, pages 1636–1647, Porto Alegre, RS, Brasil. SBC.
SANTOS, R. D. d., MAGALHÃES, W. B. C., ANJOS, A. L. C. d., and LETTI, A. G. (2024). Evasão e retenção dos cursos de licenciatura em matemática na bahia no período de 2010 a 2022. In Anais do Seminário Nacional de Políticas Públicas, Gestão e Práxis Educacional, pages 189–199, Vitória da Conquista.
Scarlatos, A., Feng, W., Smith, D., Woodhead, S., and Lan, A. (2024a). Improving automated distractor generation for math multiple-choice questions with overgenerate-and-rank. In Kochmar, E., Bexte, M., Burstein, J., Horbach, A., Laarmann-Quante, R., Tack, A., Yaneva, V., and Yuan, Z., editors, Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 222–231, Mexico City, Mexico. Association for Computational Linguistics.
Scarlatos, A., Feng, W., Smith, D., Woodhead, S., and Lan, A. (2024b). Improving automated distractor generation for math multiple-choice questions with overgenerate-and-rank. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 222–231, Mexico City, Mexico. Association for Computational Linguistics.
Tall, D. (1993). Students' difficulties in calculus. In Proceedings of Working Group 3 on Students' Difficulties in Calculus, pages 13–28.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4):197–221.
Wang, L., Song, R., Guo, W., and Yang, H. (2025). Exploring prompt pattern for generative artificial intelligence in automatic question generation. Interactive Learning Environments, 33(3):2559–2584.
Woolf, B. P. (2009). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann, Burlington.
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., and Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1):90–112.
Publicado
05/10/2026
Como Citar
CRUZ, Pedro Gabriel de Carvalho; CAVALCANTI, Anderson Pinheiro; GUARNIZ, Roy Percy Tocto; VIZARRETA, Eber Daniel Chuño.
Uma Proposta de Sistema Tutor Inteligente para a Disciplina Cálculo Diferencial. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2918-2928.
DOI: https://doi.org/10.5753/sbie.2026.28387.
