{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T02:58:06Z","timestamp":1785466686296,"version":"3.56.0"},"reference-count":58,"publisher":"Sociedade Brasileira de Computacao - SB","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["RBIE"],"abstract":"<jats:p>Este trabalho realiza uma an\u00e1lise quantitativa e qualitativa de modelos de linguagem de grande escala (LLM) para o ensino de computa\u00e7\u00e3o, avaliando seu desempenho em diversos aspectos educacionais. Inicialmente, avaliamos o desempenho de quatro modelos (ChatGPT, Claude, Copilot e Gemini) para o desenvolvimento de interfaces gr\u00e1ficas em Python no ambiente Google Colab, explorando diversos formatos de requisi\u00e7\u00f5es e definindo m\u00e9tricas para compar\u00e1-los. As m\u00e9tricas de avalia\u00e7\u00e3o incluem erros de compila\u00e7\u00e3o, execu\u00e7\u00e3o e funcionalidade, al\u00e9m do tamanho da requisi\u00e7\u00e3o, do n\u00famero de intera\u00e7\u00f5es e do n\u00famero de linhas de c\u00f3digo geradas. A partir de mais de 350 ensaios, foram geradas mais de 250 ferramentas funcionais, alcan\u00e7ando uma taxa de sucesso de 84%. Posteriormente, avaliamos a capacidade de tradu\u00e7\u00e3o de c\u00f3digo de Python para JavaScript, explorando os recursos variados para interface gr\u00e1fica em navegadores e o desempenho das LLMs para JavaScript, no qual inclu\u00edmos os modelos Gemini-Pro e DeepSeek. Avaliamos tamb\u00e9m o desempenho em interfaces com recursos de edi\u00e7\u00e3o para problemas de grafos e cria\u00e7\u00e3o de linguagens de dom\u00ednio espec\u00edfico para ensino de grafos e modelos de aprendizado de m\u00e1quina. Elaboramos tamb\u00e9m exemplos de outros usos, como gera\u00e7\u00e3o de question\u00e1rios, avalia\u00e7\u00e3o de question\u00e1rios e avalia\u00e7\u00e3o de c\u00f3digo gerado. Os exemplos avaliados abrangem diversos dom\u00ednios, todos documentados e disponibilizados como um conjunto de dados. Os resultados indicam uma acelera\u00e7\u00e3o significativa na cria\u00e7\u00e3o de ferramentas educacionais interativas e atrativas, destacando dire\u00e7\u00f5es promissoras para o uso de LLMs no desenvolvimento de recursos did\u00e1ticos para computa\u00e7\u00e3o.<\/jats:p>","DOI":"10.5753\/rbie.2026.6311","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:47:47Z","timestamp":1775134067000},"page":"279-313","source":"Crossref","is-referenced-by-count":1,"title":["Desenvolvimento de Ferramentas Educacionais para Computa\u00e7\u00e3o com Apoio de Modelos Generativos de Linguagem"],"prefix":"10.5753","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6781-8679","authenticated-orcid":false,"given":"Hugo Costa","family":"Ara\u00fajo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7689-4297","authenticated-orcid":false,"given":"Matheus Ot\u00e1vio","family":"Lisboa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3115-1561","authenticated-orcid":false,"given":"Pedro Henrique Coura","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2686-8825","authenticated-orcid":false,"given":"Isabela de Castro","family":"Freitas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6189-1300","authenticated-orcid":false,"given":"Maria L\u00facia Bento","family":"Villela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-7829","authenticated-orcid":false,"given":"Ricardo","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3742","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"1","unstructured":"Al-Shetairy, M., Hindy, H., Khattab, D., & Aref, M. 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M., Gebru, T., McMillan-Major, A., & Mitchell, M. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT). <a href=\"https:\/\/doi.org\/10.1145\/3442188.3445922\">https:\/\/doi.org\/10.1145\/3442188.3445922<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=On+the+Dangers+of+Stochastic+Parrots\">GS Search<\/a>].","DOI":"10.1145\/3442188.3445922"},{"key":"4","unstructured":"Cai, Y., Mao, S., Wu, W., Wang, Z., Liang, Y., Ge, T., Wu, C., You, W., Song, T., Xia, Y., Duan, N., & Wei, F. (2023). 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Google Colab CAD4U: Hands-on cloud laboratories for digital design. 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1\u20135. <a href=\"https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401151\">https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401151<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Google+Colab+CAD4U+Hands-on+cloud+laboratories\">GS Search<\/a>].","DOI":"10.1109\/ISCAS51556.2021.9401151"},{"key":"6","doi-asserted-by":"crossref","unstructured":"Chen, B., Zhang, Z., Langren\u00e9, N., & Zhu, S. (2023). Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review. arXiv preprint arXiv:2310.14735. <a href=\"https:\/\/doi.org\/10.1016\/j.patter.2025.101260\">https:\/\/doi.org\/10.1016\/j.patter.2025.101260<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Unleashing+the+potential+of+prompt+engineering\">GS Search<\/a>].","DOI":"10.1016\/j.patter.2025.101260"},{"key":"7","doi-asserted-by":"crossref","unstructured":"Chen, C., Sonnert, G., Sadler, P. M., & Malan, D. J. (2020). Computational thinking and assignment resubmission predict persistence in a computer science MOOC. 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LLM Agents for Education: Advances and Applications. arXiv preprint arXiv:2503.11733. [<a href=\"https:\/\/scholar.google.com\/scholar?q=LLM+Agents+for+Education+Advances+and+Applications\">GS Search<\/a>].","DOI":"10.18653\/v1\/2025.findings-emnlp.743"},{"key":"10","doi-asserted-by":"crossref","unstructured":"Chu, Z., Wang, S., Xie, J., Zhu, T., Yan, Y., Ye, J., Zhong, A., Hu, X., Liang, J., Yu, P. S., & Wen, Q. (2025b). LLM agents for education: Advances and applications. arXiv preprint arXiv:2503.11733. <a href=\"https:\/\/doi.org\/10.18653\/v1\/2025.findings-emnlp.743\">https:\/\/doi.org\/10.18653\/v1\/2025.findings-emnlp.743<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=LLM+agents+for+education+Advances+and+applications\">GS Search<\/a>].","DOI":"10.18653\/v1\/2025.findings-emnlp.743"},{"key":"11","doi-asserted-by":"crossref","unstructured":"Coura, P., Freitas, I., Costa, H., Nacif, J., & Ferreira, R. (2025). Desmistificando o Ensino de Intelig\u00eancia Artificial e Aprendizado de M\u00e1quina. Simp\u00f3sio Brasileiro de Educa\u00e7\u00e3o em Computa\u00e7\u00e3o (EDUCOMP), 25\u201327. <a href=\"https:\/\/doi.org\/10.5753\/educomp_estendido.2025.6578\">https:\/\/doi.org\/10.5753\/educomp_estendido.2025.6578<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Desmistificando+o+Ensino+de+Intelig\u00eancia+Artificial\">GS Search<\/a>].","DOI":"10.5753\/educomp_estendido.2025.6578"},{"key":"12","doi-asserted-by":"crossref","unstructured":"Del, M., & Fishel, M. (2022). True detective: a deep abductive reasoning benchmark undoable for GPT-3 and challenging for GPT-4. arXiv preprint arXiv:2212.10114. <a href=\"https:\/\/doi.org\/10.18653\/v1\/2023.starsem-1.28\">https:\/\/doi.org\/10.18653\/v1\/2023.starsem-1.28<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=True+detective+deep+abductive+reasoning+benchmark\">GS Search<\/a>].","DOI":"10.18653\/v1\/2023.starsem-1.28"},{"key":"13","unstructured":"Elon University. (2025, mar\u00e7o). Survey: 52% of U.S. adults now use AI large language models like ChatGPT. Dispon\u00edvel em: [<a href=\"https:\/\/imaginingthedigitalfuture.org\/reports-and-publications\/close-encounters-of-the-ai-kind\/\">Link<\/a>]. Acessado em: 24 de mar\u00e7o de 2026."},{"key":"14","doi-asserted-by":"crossref","unstructured":"Ferreira, R., Canesche, M., Jamieson, P., Vilela Neto, O. P., & Nacif, J. A. (2024). Examples and tutorials on using Google Colab and Gradio to create online interactive student-learning modules. Computer Applications in Engineering Education, e22729. <a href=\"https:\/\/doi.org\/10.1002\/cae.22729\">https:\/\/doi.org\/10.1002\/cae.22729<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Examples+and+tutorials+on+using+Google+Colab+and+Gradio\">GS Search<\/a>].","DOI":"10.1002\/cae.22729"},{"key":"15","doi-asserted-by":"crossref","unstructured":"Ferreira, R., Sabino, C., Canesche, M., Vilela Neto, O. P., & Nacif, J. A. (2024). AIoT tool integration for enriching teaching resources and monitoring student engagement. Internet of Things, 26, 101045. <a href=\"https:\/\/doi.org\/10.1016\/j.iot.2023.101045\">https:\/\/doi.org\/10.1016\/j.iot.2023.101045<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=AIoT+tool+integration+for+enriching+teaching+resources\">GS Search<\/a>].","DOI":"10.1016\/j.iot.2023.101045"},{"key":"16","doi-asserted-by":"crossref","unstructured":"Figueiredo, G. A. R., Souza, E. S., Rodrigues, J. H. F., Nacif, J. A., & Ferreira, R. (2024). Desenvolvendo Ferramentas para Ensino de RISC-V com Python, Verilog, Matplotlib, SVG e ChatGPT. International Journal of Computer Architecture Education, 13(1), 43\u201352. <a href=\"https:\/\/doi.org\/10.5753\/ijcae.2024.5343\">https:\/\/doi.org\/10.5753\/ijcae.2024.5343<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Desenvolvendo+Ferramentas+para+Ensino+de+RISC-V\">GS Search<\/a>].","DOI":"10.5753\/ijcae.2024.5343"},{"key":"17","doi-asserted-by":"crossref","unstructured":"Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. 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Exploring the potential of LLM to enhance teaching plans through teaching simulation. npj Science of Learning, 12(1), 1\u201312. <a href=\"https:\/\/doi.org\/10.1038\/s41539-025-00300-x\">https:\/\/doi.org\/10.1038\/s41539-025-00300-x<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Exploring+the+potential+of+LLM+to+enhance+teaching+plans\">GS Search<\/a>].","DOI":"10.1038\/s41539-025-00300-x"},{"key":"20","unstructured":"Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023). Swebench: Can language models resolve real-world GitHub issues? arXiv preprint arXiv:2310.06770. <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2310.06770\">https:\/\/doi.org\/10.48550\/arXiv.2310.06770<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Swebench+Can+language+models+resolve+real-world+GitHub+issues\">GS Search<\/a>]."},{"key":"21","doi-asserted-by":"crossref","unstructured":"Joel, S., Wu, J. J., & Fard, F. H. (2024). A survey on LLM-based code generation for low-resource and domain-specific programming languages. arXiv preprint arXiv:2410.03981. <a href=\"https:\/\/doi.org\/10.1145\/3770084\">https:\/\/doi.org\/10.1145\/3770084<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=A+survey+on+LLM-based+code+generation\">GS Search<\/a>].","DOI":"10.1145\/3770084"},{"key":"22","doi-asserted-by":"crossref","unstructured":"Joshi, S. (2025). Open-Source vs. Commercial Coding Assistants: A 2025 Comparison of DeepSeek R1, Qwen 2.5 and Claude 3.7. International Journal of Computer Applications Technology and Research, 14(09), 6\u201318. [<a href=\"https:\/\/scholar.google.com\/scholar?q=Open-Source+vs+Commercial+Coding+Assistants\">GS Search<\/a>].","DOI":"10.20944\/preprints202508.1904.v1"},{"key":"23","doi-asserted-by":"crossref","unstructured":"Julio, J. P. F., Campano Junior, M. M., Aylon, L. B. R., Fonseca, K. O., & Emmend\u00f6rfer, L. R. (2024). Jogos educativos para Estruturas de Dados: Um Mapeamento Sistem\u00e1tico. Simp\u00f3sio Brasileiro de Jogos e Entretenimento Digital (SBGames), 1186\u20131199. <a href=\"https:\/\/doi.org\/10.5753\/sbgames.2024.240933\">https:\/\/doi.org\/10.5753\/sbgames.2024.240933<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Jogos+educativos+para+Estruturas+de+Dados\">GS Search<\/a>].","DOI":"10.5753\/sbgames.2024.240933"},{"key":"24","doi-asserted-by":"crossref","unstructured":"Kazemitabaar, M., Hou, X., Henley, A., Ericson, B. J., Weintrop, D., & Grossman, T. (2023). How novices use LLM-based code generators to solve CS1 coding tasks in a self-paced learning environment. 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International Journal of Research and Innovation in Applied Science, 10(4), 266\u2013275. <a href=\"https:\/\/doi.org\/10.51584\/IJRIAS.2025.10040020\">https:\/\/doi.org\/10.51584\/IJRIAS.2025.10040020<\/a> [<a href=\"https:\/\/scholar.google.com\/scholar?q=Automated+Question+Paper+Generator+Using+LLM\">GS Search<\/a>].","DOI":"10.51584\/IJRIAS.2025.10040020"},{"key":"28","doi-asserted-by":"crossref","unstructured":"Lisboa, M. O., Costa, H., Coura, P., Freitas, I., Villela, M. L. B., & Ferreira, R. (2025). Modelos Generativos de Linguagem na Constru\u00e7\u00e3o de Ferramentas de Ensino de Computa\u00e7\u00e3o com Interface Gr\u00e1fica. 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