Avaliação da Aprendizagem em Programação com IA Generativa na Educação Profissional e Tecnológica
Resumo
Este artigo apresenta um modelo avaliativo mediado por Inteligência Artificial Generativa (GenAI) para o ensino de programação na Educação Profissional e Tecnológica (EPT). Fundamentado em princípios de avaliação formativa e autorregulação da aprendizagem, o modelo AURAIA integra personalização epistemológica, feedback automatizado e mediação docente por meio de LLMs, sistemas de gestão de aprendizagem e tutoria virtual. A avaliação é ancorada nas produções reais dos estudantes. Já foram elaborados o modelo conceitual e seu protótipo funcional, ambos em fase de validação preliminar junto a docentes e discentes de pós-graduação.
Referências
Bido, Y., Wiese, I., and Nakamura, W. (2024). IAs generativas na educação: Usos, percepções, desafios e adaptações nas práticas pedagógicas do ponto de vista de professores do ensino fundamental, médio e superior. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1701–1714, Porto Alegre, RS, Brasil. SBC.
Bower, M., Torrington, J., Lai, J. W. M., Petocz, P., and Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies, 29(12):15403–15439.
Currie, G. M. (2023). Academic integrity and artificial intelligence: is chatgpt hype, hero or heresy? Seminars in Nuclear Medicine, 53(5):719–730. Preclinical.
Deus, W., Marcolino, A., Avellar, G., Oliveira, K., and Barbosa, E. (2024). Avaliando resoluções de exercícios introdutórios de programação na era das IAs generativas: Um estudo de caso com o ChatGPT. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1645–1657, Porto Alegre, RS, Brasil. SBC.
Freire, P. (2014). Pedagogia da autonomia: Saberes necessários à prática educativa. Paz e Terra, São Paulo.
Griffiths, D., Frías-Martínez, E., Tlili, A., and Burgos, D. (2024). A cybernetic perspective on generative AI in education: From transmission to coordination. International Journal of Interactive Multimedia and Artificial Intelligence, 8(5):15–24.
Hermans, F. and Smit, M. (2018). Explicit direct instruction in programming education. In Proceedings of the 29th Annual Workshop of the Psychology of Programming Interest Group, PPIG 2018, London, UK, September 5 - 7, 2018, pages 32–41. Psychology of Programming Interest Group.
Hu, X., Xu, S., Tong, R. J., and Graesser, A. (2025). Generative AI in education: from foundational insights to the Socratic Playground for learning. ArXiv, abs/2501.06682.
Kilinç, S. (2024). Comprehensive AI assessment framework: Enhancing educational evaluation with ethical AI integration. Journal of Educational Technology and Online Learning, 7(4 - ICETOL 2024 Special Issue):521–540.
Lishinski, A., Yadav, A., Enbody, R., and Good, J. (2016). The influence of problem solving abilities on students’ performance on different assessment tasks in CS1. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education, SIGCSE ’16, page 329–334, New York, NY, USA. Association for Computing Machinery.
Logacheva, E., Hellas, A., Prather, J., Sarsa, S., and Leinonen, J. (2024). Evaluating contextually personalized programming exercises created with generative ai. In Proceedings of the 2024 ACM Conference on International Computing Education Research Volume 1, ICER ’24, page 95–113, New York, NY, USA. Association for Computing Machinery.
Maturana, H. R. and Varela, F. J. (2011). A árvore do conhecimento: as bases biológicas da compreensão humana. Palas Athena, São Paulo, 9 edition.
Moorhouse, B. and Wong, K. (2023). Educators’ assessment literacy in the age of AI: ethical considerations and pedagogical shifts. AI and Education Journal, 2(1):10–25.
Pesovski, I., Santos, R., Henriques, R., and Trajkovik, V. (2024). Generative AI for customizable learning experiences. Sustainability, 16(7):1–23.
Saini, A. K., Cope, B., Kalantzis, M., and Zapata, G. C. (2024). The future of feedback: Integrating peer and generative AI reviews to support student work.
Sardi, J., Darmansyah, Candra, O., Yuliana, D. F., Habibullah, Yanto, D. T. P., and Eliza, F. (2025). How generative AI influences students’ self-regulated learning and critical thinking skills? a systematic review. International Journal of Engineering Pedagogy (iJEP), 15(1):pp. 94–108.
Sarsa, S., Denny, P., Hellas, A., and Leinonen, J. (2022). Automatic generation of programming exercises and code explanations using large language models. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1, ICER ’22, page 27–43, New York, NY, USA. Association for Computing Machinery.
Schacht, S., Kamath Barkur, S., and Lanquillon, C. (2023). Generative agents to support students’ learning progress. In Proceedings of the 5th International Conference Business Meets Technology, pages 179–197, Valencia.
Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press, Urbana, IL.
Silvestre, A., Amaral, E., Holanda, M., and Canedo, E. (2023). Students’ perception about ChatGPT’s impact on their academic education. In Anais do XXXIV Simpósio Brasileiro de Informática na Educação, pages 1260–1270, Porto Alegre, RS, Brasil. SBC.
Sousa, H. and Cruz, D. (2024). Capacitando educadores com IA generativa: Implicações na educação. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1931–1941, Porto Alegre, RS, Brasil. SBC.
Stockard, J., Wood, T. W., Coughlin, C., and Khoury, C. R. (2018). The effectiveness of direct instruction curricula: A meta-analysis of a half century of research. Review of Educational Research, 88(4):479–507.
Wang, Y. (2024). Cognitive and sociocultural dynamics of self-regulated use of machine translation and generative AI tools in academic EFL writing. System, 126:103505.
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., and Chiu, T. K. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1):40.
Bower, M., Torrington, J., Lai, J. W. M., Petocz, P., and Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies, 29(12):15403–15439.
Currie, G. M. (2023). Academic integrity and artificial intelligence: is chatgpt hype, hero or heresy? Seminars in Nuclear Medicine, 53(5):719–730. Preclinical.
Deus, W., Marcolino, A., Avellar, G., Oliveira, K., and Barbosa, E. (2024). Avaliando resoluções de exercícios introdutórios de programação na era das IAs generativas: Um estudo de caso com o ChatGPT. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1645–1657, Porto Alegre, RS, Brasil. SBC.
Freire, P. (2014). Pedagogia da autonomia: Saberes necessários à prática educativa. Paz e Terra, São Paulo.
Griffiths, D., Frías-Martínez, E., Tlili, A., and Burgos, D. (2024). A cybernetic perspective on generative AI in education: From transmission to coordination. International Journal of Interactive Multimedia and Artificial Intelligence, 8(5):15–24.
Hermans, F. and Smit, M. (2018). Explicit direct instruction in programming education. In Proceedings of the 29th Annual Workshop of the Psychology of Programming Interest Group, PPIG 2018, London, UK, September 5 - 7, 2018, pages 32–41. Psychology of Programming Interest Group.
Hu, X., Xu, S., Tong, R. J., and Graesser, A. (2025). Generative AI in education: from foundational insights to the Socratic Playground for learning. ArXiv, abs/2501.06682.
Kilinç, S. (2024). Comprehensive AI assessment framework: Enhancing educational evaluation with ethical AI integration. Journal of Educational Technology and Online Learning, 7(4 - ICETOL 2024 Special Issue):521–540.
Lishinski, A., Yadav, A., Enbody, R., and Good, J. (2016). The influence of problem solving abilities on students’ performance on different assessment tasks in CS1. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education, SIGCSE ’16, page 329–334, New York, NY, USA. Association for Computing Machinery.
Logacheva, E., Hellas, A., Prather, J., Sarsa, S., and Leinonen, J. (2024). Evaluating contextually personalized programming exercises created with generative ai. In Proceedings of the 2024 ACM Conference on International Computing Education Research Volume 1, ICER ’24, page 95–113, New York, NY, USA. Association for Computing Machinery.
Maturana, H. R. and Varela, F. J. (2011). A árvore do conhecimento: as bases biológicas da compreensão humana. Palas Athena, São Paulo, 9 edition.
Moorhouse, B. and Wong, K. (2023). Educators’ assessment literacy in the age of AI: ethical considerations and pedagogical shifts. AI and Education Journal, 2(1):10–25.
Pesovski, I., Santos, R., Henriques, R., and Trajkovik, V. (2024). Generative AI for customizable learning experiences. Sustainability, 16(7):1–23.
Saini, A. K., Cope, B., Kalantzis, M., and Zapata, G. C. (2024). The future of feedback: Integrating peer and generative AI reviews to support student work.
Sardi, J., Darmansyah, Candra, O., Yuliana, D. F., Habibullah, Yanto, D. T. P., and Eliza, F. (2025). How generative AI influences students’ self-regulated learning and critical thinking skills? a systematic review. International Journal of Engineering Pedagogy (iJEP), 15(1):pp. 94–108.
Sarsa, S., Denny, P., Hellas, A., and Leinonen, J. (2022). Automatic generation of programming exercises and code explanations using large language models. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1, ICER ’22, page 27–43, New York, NY, USA. Association for Computing Machinery.
Schacht, S., Kamath Barkur, S., and Lanquillon, C. (2023). Generative agents to support students’ learning progress. In Proceedings of the 5th International Conference Business Meets Technology, pages 179–197, Valencia.
Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press, Urbana, IL.
Silvestre, A., Amaral, E., Holanda, M., and Canedo, E. (2023). Students’ perception about ChatGPT’s impact on their academic education. In Anais do XXXIV Simpósio Brasileiro de Informática na Educação, pages 1260–1270, Porto Alegre, RS, Brasil. SBC.
Sousa, H. and Cruz, D. (2024). Capacitando educadores com IA generativa: Implicações na educação. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 1931–1941, Porto Alegre, RS, Brasil. SBC.
Stockard, J., Wood, T. W., Coughlin, C., and Khoury, C. R. (2018). The effectiveness of direct instruction curricula: A meta-analysis of a half century of research. Review of Educational Research, 88(4):479–507.
Wang, Y. (2024). Cognitive and sociocultural dynamics of self-regulated use of machine translation and generative AI tools in academic EFL writing. System, 126:103505.
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., and Chiu, T. K. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1):40.
Publicado
24/11/2025
Como Citar
GOMES, Paulo César Rodacki; HÜBNER, Jomi Fred.
Avaliação da Aprendizagem em Programação com IA Generativa na Educação Profissional e Tecnológica. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1529-1539.
DOI: https://doi.org/10.5753/sbie.2025.12395.
