Quintana: Ferramenta de Avaliação de Redações
Resumo
This article presents Quintana, an AI-based essay assessment tool for Portuguese-language argumentative texts, based on the competencies of the Exame Nacional do Ensino Médio (ENEM). The tool uses machine learning models trained on essays extracted from the “Brasil Escola” platform and allows students to submit essays and receive predicted scores for each competency. The results are presented in a pedagogically organized way, with scores and feedback grouped by competency, supporting formative use by students and teachers without replacing teacher assessment. This demo showcases how modern Natural Language Processing techniques can be integrated into a practical and accessible educational solution with meaningful social impact.
Palavras-chave:
avaliação automática de redações, processamento de linguagem natural, inteligência artificial na educação, ENEM, feedback formativo, aprendizagem de máquina
Referências
Amorim, E. and Veloso, A. (2017). A multi-aspect analysis of automatic essay scoring for brazilian portuguese. In Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 94–102.
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., and Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. CoRR, abs/1911.02116.
Fonseca, E., Medeiros, I., Kamikawachi, D., and Bokan, A. (2018). Automatically grading brazilian student essays. In International Conference on Computational Processing of the Portuguese Language, pages 170–179. Springer.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al. (2022). Lora: Low-rank adaptation of large language models. ICLR, 1(2):3.
Lim, C. T., Bong, C. H., Wong, W. S., and Lee, N. K. (2021). A comprehensive review of automated essay scoring (aes) research and development. Pertanika Journal of Science and Technology, 29(3):1875–1899.
Silveira, I. C., Barbosa, A., and Mauá, D. D. (2024). A new benchmark for automatic essay scoring in Portuguese. In PROPOR 2024, pages 228–237. Association for Computational Lingustics.
Ye, X. and Manoharan, S. (2021). Performance comparison of automated essay graders based on various language models. In ICOCO 2021, pages 152–157.
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., and Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. CoRR, abs/1911.02116.
Fonseca, E., Medeiros, I., Kamikawachi, D., and Bokan, A. (2018). Automatically grading brazilian student essays. In International Conference on Computational Processing of the Portuguese Language, pages 170–179. Springer.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al. (2022). Lora: Low-rank adaptation of large language models. ICLR, 1(2):3.
Lim, C. T., Bong, C. H., Wong, W. S., and Lee, N. K. (2021). A comprehensive review of automated essay scoring (aes) research and development. Pertanika Journal of Science and Technology, 29(3):1875–1899.
Silveira, I. C., Barbosa, A., and Mauá, D. D. (2024). A new benchmark for automatic essay scoring in Portuguese. In PROPOR 2024, pages 228–237. Association for Computational Lingustics.
Ye, X. and Manoharan, S. (2021). Performance comparison of automated essay graders based on various language models. In ICOCO 2021, pages 152–157.
Publicado
08/09/2026
Como Citar
SOARES, Vanessa et al.
Quintana: Ferramenta de Avaliação de Redações. In: DEMONSTRAÇÕES E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 251-256.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249547.
