IA Generativa no Ensino de Programação: Mapeamento Sistemático, Inversão do Fluxo Cognitivo e Agenda para a Pesquisa Brasileira
Resumo
Este Mapeamento Sistemático da Literatura analisa 101 estudos (2021–2025) sobre Inteligência Artificial Generativa (GenAI) no ensino de programação, recuperados em sete bibliotecas digitais e classificados em quatro categorias temáticas. Avaliação Tecnológica está presente em 79,2% dos estudos e Impactos Psicossociais em apenas 5,9%. O achado central é a inversão do fluxo cognitivo, em que estudantes geram código antes de compreender o problema, com risco documentado de ilusão de competência. A produção brasileira representa 4% do corpus, concentrada em Avaliação Tecnológica e sem foco predominante em Estratégias Pedagógicas e Impactos Psicossociais, quadro do qual derivamos uma agenda nacional em cinco lacunas estruturais de pesquisa.
Palavras-chave:
IA Generativa, Ensino de Programação, Mapeamento Sistemático, Contexto Brasileiro, Informática na Educação
Referências
Amoozadeh, M., Nam, D., Prol, D., Alfageeh, A., Prather, J., Hilton, M., Srinivasa Ragavan, S., e Alipour, A. (2024). Student-AI interaction: A case study of CS1 students. In Proceedings of the 24th Koli Calling International Conference on Computing Education Research. ACM.
Aruleba, K., Oyelere, S. S., Obaido, G. R., e Sanusi, I. T. (2025). A scoping review of student and educator engagement with large language models in introductory programming education. In UKICER 2025. ACM.
Banic, B., Konecki, M., e Konecki, M. (2023). Pair programming education aided by ChatGPT. In MIPRO 2023. IEEE.
Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., e Noroozi, O. (2025). A systematic mapping review at the intersection of artificial intelligence and self-regulated learning. International Journal of Educational Technology in Higher Education, 22(1):50.
Barcaui, A. (2025). ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention. Social Sciences & Humanities Open, 12:102287.
Barros, A., Monteiro, M., Rodrigues, L., Dermeval, D., Isotani, S., e Bittencourt, I. I. (2025). Evaluating large language model quality in resource-constrained environments: An educational stakeholders' survey on accuracy, completeness, and readability in Brazil. In Artificial Intelligence in Education, Lecture Notes in Computer Science, pages 218–232. Springer.
Bedin, H. A. T., Canedo, E. D., e Jaques, P. A. (2026a). Responsible use of generative AI in systematic reviews: A practical methodological guideline. In CSEDU 2026, pages 63–74. SCITEPRESS.
Bedin, H. A. T., Mariotti, M. B., e Jaques, P. A. (2026b). Artefatos suplementares do mapeamento sistemático sobre IA Generativa no ensino de programação. Zenodo. DOI: 10.5281/zenodo.21342736.
Borchers, C. e Shou, T. (2025). Can large language models match tutoring system adaptivity? A benchmarking study. In AIED 2025, volume 15878, pages 407–420. Springer.
Carvalho, S. e Oliveira, I. C. (2024). O uso e o potencial de inteligências artificiais no aprendizado de algoritmos e programação de computadores. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 3049–3058. SBC.
Cheng, G., Wong, W., Luo, L., e Yu, M. (2025). Integrating a scaffolding-based, LLM-driven chatbot into programming education: A university case study. In ISET 2025, pages 1–5. IEEE.
Dawson, M. G., Deer, R., e Boguslawski, S. (2025). Cognitive dissonance in programming education: A qualitative exploration of the impact of generative AI on application-directed learning. Computers in Human Behavior Reports, 19:100724.
de Campos, T. P., Damasceno, E. F., e Valentim, N. M. C. (2023). Evaluating a collaborative IS to support systematic reviews and mapping studies. iSys: Revista Brasileira de Sistemas de Informação, 16(1):8:1–8:33.
Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., e Reeves, B. N. (2024). Prompt problems: A new programming exercise for the generative AI era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, page 296–302. ACM.
Dos Santos, O. L. e Cury, D. (2023). Challenging the confirmation bias: Using ChatGPT as a virtual peer for peer instruction in computer programming education. In FIE 2023. IEEE.
Feng, T., Liu, S., e Ghosal, D. (2024). CourseAssist: Pedagogically appropriate AI tutor for computer science education. In VGCE 2024, page 310–311. ACM.
Gandhi, A. e Muldner, K. (2025). "that's not the way I would explain it": A teacher-researcher's autoethnography of learning to program with ChatGPT. In ICER 2025. ACM.
Gong, X., Xu, W., e Qiao, A. (2025). Exploring undergraduates' computational thinking and human-computer interaction patterns in generative progressive prompt-assisted programming learning. International Journal of Educational Technology in Higher Education, 22(51).
Groothuijsen, S., van den Beemt, A., Remmers, J. C., e van Meeuwen, L. W. (2024). AI chatbots in programming education: Students' use in a scientific computing course and consequences for learning. Computers and Education: Artificial Intelligence, 7:100290.
INEP (2013). Censo da educação superior 2011: Resumo técnico. Technical report, Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira.
INEP (2024). Resumo técnico do censo da educação superior 2023. Technical report, Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira.
Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., e Grossman, T. (2023). Studying the effect of AI code generators on supporting novice learners in introductory programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, page 1–23. ACM.
Kitchenham, B. e Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01, Keele University and University of Durham.
Koriat, A. (1997). Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4):349–370.
Kosmyna, N., Hauptmann, E., Yuan, Y., et al. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872.
Kuramitsu, K., Obara, Y., Sato, M., e Obara, M. (2023). KOGI: A seamless integration of ChatGPT into Jupyter environments for programming education. In Proceedings of the 2023 ACM SIGPLAN International Symposium on SPLASH-E, page 50–59. ACM.
Kuttal, S. K., Sedhain, A., e AuBuchon, J. (2021). Designing a gender-inclusive conversational agent for pair programming: An empirical investigation. Lecture Notes in Computer Science, 12797 LNAI:59–75.
Landis, J. R. e Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.
Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems, volume 33, pages 9459–9474.
Logozar, R., Mikac, M., e Hizak, J. (2023). ChatGPT on the Freshman Test in C/C++ Programming. In SISY 2023. IEEE.
MacNeil, S., Denny, P., Tran, A., Leinonen, J., Bernstein, S., Hellas, A., Sarsa, S., e Kim, J. (2024). Decoding logic errors: A comparative study on bug detection by students and large language models. In ACE 2024, pages 11–18. ACM.
Manorat, P., Tuarob, S., e Pongpaichet, S. (2025). Artificial intelligence in computer programming education: A systematic literature review. Computers and Education: Artificial Intelligence, 8:100403.
Margulieux, L. E., Prather, J., Reeves, B. N., et al. (2024). Self-regulation, self-efficacy, and fear of failure interactions with how novices use LLMs to solve programming problems. In ITiCSE 2024, pages 276–282. ACM.
Masetty, S. V. P. e Vallamulla, S. (2025). Enhancing novice programming education through AI-driven mentorship and project-based learning. In 2025 IEEE Integrated STEM Education Conference (ISEC), pages 1–7.
Medeiros, R. P., Falcão, T. P., e Ramalho, G. L. (2020). Ensino e aprendizagem de introdução à programação no ensino superior brasileiro: Revisão sistemática da literatura. In Workshop Sobre Educação em Computação (WEI), pages 186–190. SBC.
Moon, J., Yang, R., Cha, S., e Kim, S. B. (2023). ChatGPT vs mentor: Programming language learning assistance system for beginners. In 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), pages 106–110.
Norton, V., Honda, F., Pessoa, M., e Pires, F. (2024). CodeX: auxiliando estudantes com TEA em programação por meio de um ambiente virtual de aprendizagem (AVA) integrado à um LLM. In Anais Estendidos do XIII Congresso Brasileiro de Informática na Educação, pages 155–158. SBC.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
Paludo, G. e Montresor, A. (2024). Fostering metacognitive skills in programming: Leveraging AI to reflect on code. In AIxEDU 2024, volume 3879 of CEUR Workshop Proceedings. CEUR-WS.org.
Park, S. M., Ho, M., Lin, M. P.-C., e Ryoo, J. (2025). Evaluating the impact of assistive AI tools on learning outcomes and ethical considerations in programming education. In 2025 IEEE Global Engineering Education Conference (EDUCON), pages 1–10.
Pesovski, I., Vorkel, D., e Trajkovik, V. (2024). AI-powered education: Rethinking the way programming is taught using AI tools and reversed Bloom's taxonomy. In 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), pages 1–6.
Petersen, K., Vakkalanka, S., e Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.
Phung, T., Padurean, V.-A., Cambronero, J., Gulwani, S., Kohn, T., Majumdar, R., Singla, A., e Soares, G. (2023). Generative AI for programming education: Benchmarking ChatGPT, GPT-4, and human tutors. In ICER '23 V. 2, pages 41–42. ACM.
Portela, C., Palomino, P., Challco, G., Sobrinho, Á., Cordeiro, T., Mello, R., Dermeval, D., Bittencourt, I., e Isotani, S. (2024). AI in education unplugged support equity between rural and urban areas in Brazil. In ICTD 2024, pages 143–154.
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., et al. (2023). The robots are here: Navigating the generative AI revolution in computing education. In ITiCSE-WGR 2023, page 108–159. ACM.
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., e Briggs, B. (2024). The widening gap: The benefits and harms of generative AI for novice programmers. In ICER 2024, pages 469–486. ACM.
Ramabu, T. e Malebane, T. (2024). Guidelines for effective use of ChatGPT in introductory programming education. In IST-Africa 2024, pages 1–8.
Robins, A., Rountree, J., e Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2):137–172.
Silva, E. P. d., Caceffo, R., e Azevedo, R. (2023). Uma Análise Curricular de Disciplinas de CS1 das Universidades Públicas Brasileiras. RBIE, 31:407–436.
Silva, T. L. d., Vidotto, K. N. S., Tarouco, L. M. R., e Silva, P. F. d. (2024). Potencialidades do uso de Inteligência Artificial Generativa como apoio ao Ensino de Programação. In SBIE 2024, pages 1942–1956.
Simaremare, M., Pardede, C., Tampubolon, I., Manurung, P., e Simangunsong, D. (2024). Pair programming in programming courses in the era of generative AI: Students' perspective. In APSEC 2024, pages 507–511.
Slimi, Z. (2026). A systematic critical review of generative AI's impact on authorship, pedagogy, and integrity (2023–2025). Frontiers in Education, 11:1769680.
Teodoro, P. V. e Freire, V. (2025). Artificial intelligence and qualitative reasoning in science education: A systematic review of lusophone literature (2000–2024). In Artificial Intelligence in Education, Lecture Notes in Computer Science, pages 449–456. Springer.
Uema, M. A., Souza, T. d. P. C. d., Dermeval, D., Bittencourt, I. I., e Isotani, S. (2025). Designing for meaningful access: Towards a framework for AI in education unplugged. In Cristea, A. I. et al., editors, Artificial Intelligence in Education, volume 15880 of Lecture Notes in Artificial Intelligence (LNAI), pages 307–320. Springer Nature Switzerland.
Vadaparty, A., Geng, F., Smith, D. H., Benario, J. G., Zingaro, D., e Porter, L. (2025). Achievement goals in CS1-LLM. In ACE 2025, page 144–153. ACM.
Valový, M. e Buchalcevova, A. (2023). The psychological effects of AI-assisted programming on students and professionals. In ICSME 2023, pages 385–390.
Yilmaz, R. e Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4:100147.
Zhang, L., Jiang, Q., Xiong, W., e Zhao, W. (2025). Effects of ChatGPT-based human-computer dialogic interaction programming activities on student engagement. Journal of Educational Computing Research, 63(4).
Zhao, Y., Ling, X., Zhu, H., Zhou, F., e Deng, Z. (2025). Exploration of computer programming teaching reform based on large language models. In ICCAI 2025, pages 359–363.
Zhou, X., Liang, P., Zhang, B., Li, Z., Ahmad, A., Shahin, M., e Waseem, M. (2025). Exploring the problems, their causes and solutions of AI pair programming: A study on GitHub and stack overflow. Journal of Systems and Software, 219:112204.
Zhuang, Y., Ji, Y., Huang, Z., Shang, Y., Li, X., e Su, X. (2025). Exploring the integration of artificial intelligence and programming education: A practice-oriented study on the development of computational thinking based on human-AI co-creation. In SEAI 2025, pages 325–329.
Aruleba, K., Oyelere, S. S., Obaido, G. R., e Sanusi, I. T. (2025). A scoping review of student and educator engagement with large language models in introductory programming education. In UKICER 2025. ACM.
Banic, B., Konecki, M., e Konecki, M. (2023). Pair programming education aided by ChatGPT. In MIPRO 2023. IEEE.
Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., e Noroozi, O. (2025). A systematic mapping review at the intersection of artificial intelligence and self-regulated learning. International Journal of Educational Technology in Higher Education, 22(1):50.
Barcaui, A. (2025). ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention. Social Sciences & Humanities Open, 12:102287.
Barros, A., Monteiro, M., Rodrigues, L., Dermeval, D., Isotani, S., e Bittencourt, I. I. (2025). Evaluating large language model quality in resource-constrained environments: An educational stakeholders' survey on accuracy, completeness, and readability in Brazil. In Artificial Intelligence in Education, Lecture Notes in Computer Science, pages 218–232. Springer.
Bedin, H. A. T., Canedo, E. D., e Jaques, P. A. (2026a). Responsible use of generative AI in systematic reviews: A practical methodological guideline. In CSEDU 2026, pages 63–74. SCITEPRESS.
Bedin, H. A. T., Mariotti, M. B., e Jaques, P. A. (2026b). Artefatos suplementares do mapeamento sistemático sobre IA Generativa no ensino de programação. Zenodo. DOI: 10.5281/zenodo.21342736.
Borchers, C. e Shou, T. (2025). Can large language models match tutoring system adaptivity? A benchmarking study. In AIED 2025, volume 15878, pages 407–420. Springer.
Carvalho, S. e Oliveira, I. C. (2024). O uso e o potencial de inteligências artificiais no aprendizado de algoritmos e programação de computadores. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 3049–3058. SBC.
Cheng, G., Wong, W., Luo, L., e Yu, M. (2025). Integrating a scaffolding-based, LLM-driven chatbot into programming education: A university case study. In ISET 2025, pages 1–5. IEEE.
Dawson, M. G., Deer, R., e Boguslawski, S. (2025). Cognitive dissonance in programming education: A qualitative exploration of the impact of generative AI on application-directed learning. Computers in Human Behavior Reports, 19:100724.
de Campos, T. P., Damasceno, E. F., e Valentim, N. M. C. (2023). Evaluating a collaborative IS to support systematic reviews and mapping studies. iSys: Revista Brasileira de Sistemas de Informação, 16(1):8:1–8:33.
Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., e Reeves, B. N. (2024). Prompt problems: A new programming exercise for the generative AI era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, page 296–302. ACM.
Dos Santos, O. L. e Cury, D. (2023). Challenging the confirmation bias: Using ChatGPT as a virtual peer for peer instruction in computer programming education. In FIE 2023. IEEE.
Feng, T., Liu, S., e Ghosal, D. (2024). CourseAssist: Pedagogically appropriate AI tutor for computer science education. In VGCE 2024, page 310–311. ACM.
Gandhi, A. e Muldner, K. (2025). "that's not the way I would explain it": A teacher-researcher's autoethnography of learning to program with ChatGPT. In ICER 2025. ACM.
Gong, X., Xu, W., e Qiao, A. (2025). Exploring undergraduates' computational thinking and human-computer interaction patterns in generative progressive prompt-assisted programming learning. International Journal of Educational Technology in Higher Education, 22(51).
Groothuijsen, S., van den Beemt, A., Remmers, J. C., e van Meeuwen, L. W. (2024). AI chatbots in programming education: Students' use in a scientific computing course and consequences for learning. Computers and Education: Artificial Intelligence, 7:100290.
INEP (2013). Censo da educação superior 2011: Resumo técnico. Technical report, Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira.
INEP (2024). Resumo técnico do censo da educação superior 2023. Technical report, Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira.
Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., e Grossman, T. (2023). Studying the effect of AI code generators on supporting novice learners in introductory programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, page 1–23. ACM.
Kitchenham, B. e Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01, Keele University and University of Durham.
Koriat, A. (1997). Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4):349–370.
Kosmyna, N., Hauptmann, E., Yuan, Y., et al. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872.
Kuramitsu, K., Obara, Y., Sato, M., e Obara, M. (2023). KOGI: A seamless integration of ChatGPT into Jupyter environments for programming education. In Proceedings of the 2023 ACM SIGPLAN International Symposium on SPLASH-E, page 50–59. ACM.
Kuttal, S. K., Sedhain, A., e AuBuchon, J. (2021). Designing a gender-inclusive conversational agent for pair programming: An empirical investigation. Lecture Notes in Computer Science, 12797 LNAI:59–75.
Landis, J. R. e Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.
Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems, volume 33, pages 9459–9474.
Logozar, R., Mikac, M., e Hizak, J. (2023). ChatGPT on the Freshman Test in C/C++ Programming. In SISY 2023. IEEE.
MacNeil, S., Denny, P., Tran, A., Leinonen, J., Bernstein, S., Hellas, A., Sarsa, S., e Kim, J. (2024). Decoding logic errors: A comparative study on bug detection by students and large language models. In ACE 2024, pages 11–18. ACM.
Manorat, P., Tuarob, S., e Pongpaichet, S. (2025). Artificial intelligence in computer programming education: A systematic literature review. Computers and Education: Artificial Intelligence, 8:100403.
Margulieux, L. E., Prather, J., Reeves, B. N., et al. (2024). Self-regulation, self-efficacy, and fear of failure interactions with how novices use LLMs to solve programming problems. In ITiCSE 2024, pages 276–282. ACM.
Masetty, S. V. P. e Vallamulla, S. (2025). Enhancing novice programming education through AI-driven mentorship and project-based learning. In 2025 IEEE Integrated STEM Education Conference (ISEC), pages 1–7.
Medeiros, R. P., Falcão, T. P., e Ramalho, G. L. (2020). Ensino e aprendizagem de introdução à programação no ensino superior brasileiro: Revisão sistemática da literatura. In Workshop Sobre Educação em Computação (WEI), pages 186–190. SBC.
Moon, J., Yang, R., Cha, S., e Kim, S. B. (2023). ChatGPT vs mentor: Programming language learning assistance system for beginners. In 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), pages 106–110.
Norton, V., Honda, F., Pessoa, M., e Pires, F. (2024). CodeX: auxiliando estudantes com TEA em programação por meio de um ambiente virtual de aprendizagem (AVA) integrado à um LLM. In Anais Estendidos do XIII Congresso Brasileiro de Informática na Educação, pages 155–158. SBC.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
Paludo, G. e Montresor, A. (2024). Fostering metacognitive skills in programming: Leveraging AI to reflect on code. In AIxEDU 2024, volume 3879 of CEUR Workshop Proceedings. CEUR-WS.org.
Park, S. M., Ho, M., Lin, M. P.-C., e Ryoo, J. (2025). Evaluating the impact of assistive AI tools on learning outcomes and ethical considerations in programming education. In 2025 IEEE Global Engineering Education Conference (EDUCON), pages 1–10.
Pesovski, I., Vorkel, D., e Trajkovik, V. (2024). AI-powered education: Rethinking the way programming is taught using AI tools and reversed Bloom's taxonomy. In 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), pages 1–6.
Petersen, K., Vakkalanka, S., e Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.
Phung, T., Padurean, V.-A., Cambronero, J., Gulwani, S., Kohn, T., Majumdar, R., Singla, A., e Soares, G. (2023). Generative AI for programming education: Benchmarking ChatGPT, GPT-4, and human tutors. In ICER '23 V. 2, pages 41–42. ACM.
Portela, C., Palomino, P., Challco, G., Sobrinho, Á., Cordeiro, T., Mello, R., Dermeval, D., Bittencourt, I., e Isotani, S. (2024). AI in education unplugged support equity between rural and urban areas in Brazil. In ICTD 2024, pages 143–154.
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., et al. (2023). The robots are here: Navigating the generative AI revolution in computing education. In ITiCSE-WGR 2023, page 108–159. ACM.
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., e Briggs, B. (2024). The widening gap: The benefits and harms of generative AI for novice programmers. In ICER 2024, pages 469–486. ACM.
Ramabu, T. e Malebane, T. (2024). Guidelines for effective use of ChatGPT in introductory programming education. In IST-Africa 2024, pages 1–8.
Robins, A., Rountree, J., e Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2):137–172.
Silva, E. P. d., Caceffo, R., e Azevedo, R. (2023). Uma Análise Curricular de Disciplinas de CS1 das Universidades Públicas Brasileiras. RBIE, 31:407–436.
Silva, T. L. d., Vidotto, K. N. S., Tarouco, L. M. R., e Silva, P. F. d. (2024). Potencialidades do uso de Inteligência Artificial Generativa como apoio ao Ensino de Programação. In SBIE 2024, pages 1942–1956.
Simaremare, M., Pardede, C., Tampubolon, I., Manurung, P., e Simangunsong, D. (2024). Pair programming in programming courses in the era of generative AI: Students' perspective. In APSEC 2024, pages 507–511.
Slimi, Z. (2026). A systematic critical review of generative AI's impact on authorship, pedagogy, and integrity (2023–2025). Frontiers in Education, 11:1769680.
Teodoro, P. V. e Freire, V. (2025). Artificial intelligence and qualitative reasoning in science education: A systematic review of lusophone literature (2000–2024). In Artificial Intelligence in Education, Lecture Notes in Computer Science, pages 449–456. Springer.
Uema, M. A., Souza, T. d. P. C. d., Dermeval, D., Bittencourt, I. I., e Isotani, S. (2025). Designing for meaningful access: Towards a framework for AI in education unplugged. In Cristea, A. I. et al., editors, Artificial Intelligence in Education, volume 15880 of Lecture Notes in Artificial Intelligence (LNAI), pages 307–320. Springer Nature Switzerland.
Vadaparty, A., Geng, F., Smith, D. H., Benario, J. G., Zingaro, D., e Porter, L. (2025). Achievement goals in CS1-LLM. In ACE 2025, page 144–153. ACM.
Valový, M. e Buchalcevova, A. (2023). The psychological effects of AI-assisted programming on students and professionals. In ICSME 2023, pages 385–390.
Yilmaz, R. e Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4:100147.
Zhang, L., Jiang, Q., Xiong, W., e Zhao, W. (2025). Effects of ChatGPT-based human-computer dialogic interaction programming activities on student engagement. Journal of Educational Computing Research, 63(4).
Zhao, Y., Ling, X., Zhu, H., Zhou, F., e Deng, Z. (2025). Exploration of computer programming teaching reform based on large language models. In ICCAI 2025, pages 359–363.
Zhou, X., Liang, P., Zhang, B., Li, Z., Ahmad, A., Shahin, M., e Waseem, M. (2025). Exploring the problems, their causes and solutions of AI pair programming: A study on GitHub and stack overflow. Journal of Systems and Software, 219:112204.
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Publicado
05/10/2026
Como Citar
BEDIN, Heloise Acco T.; MARIOTTI, Madianita Bogo; JAQUES, Patricia A..
IA Generativa no Ensino de Programação: Mapeamento Sistemático, Inversão do Fluxo Cognitivo e Agenda para a Pesquisa Brasileira. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 815-831.
DOI: https://doi.org/10.5753/sbie.2026.27338.
