Generative AI Assisting Programming Learning: Functional Roles, Comparative Analysis, and Emerging Challenges
Resumo
Este trabalho examina, por meio de um mapeamento sistemático da literatura, como a Inteligência Artificial (IA) generativa tem apoiado a aprendizagem de programação. A análise de 52 estudos em sete bases de dados revelou três funções principais da IA: participante colaborativo, ferramenta de suporte e mediador pedagógico. O estudo compara abordagens assistidas por IA com métodos tradicionais, identificando transformações nos modelos pedagógicos, comunicação e engajamento cognitivo. A pesquisa mapeia desafios técnicos, pedagógicos, éticos e avaliativos. Propõe-se uma classificação funcional para os papéis da IA e recomendações para integração responsável no ensino de programação.Referências
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Amoozadeh, M., Nam, D., Prol, D., Alfageeh, A., Prather, J., Hilton, M., Srinivasa Ragavan, S., and 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, Koli Calling ’24, New York, NY, USA. Association for Computing Machinery.
Asare, O., Nagappan, M., and Asokan, N. (2023). Is github’s copilot as bad as humans at introducing vulnerabilities in code? Empirical Software Engineering, 28(129).
Banić, B., Konecki, M., and Konecki, M. (2023). Pair programming education aided by chatgpt. In 2023 46th MIPRO ICT and Electronics Convention (MIPRO). IEEE.
Barke, S., James, M. B., and Polikarpova, N. (2023). Grounded copilot: How programmers interact with code-generating models. Proc. ACM Program. Lang., 7(OOP-SLA1).
Bassner, P., Frankford, E., and Krusche, S. (2024). Iris: An ai-driven virtual tutor for computer science education. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, page 394–400, New York, NY, USA. Association for Computing Machinery.
Becker, B. A., Craig, M., Denny, P., Keuning, H., Kiesler, N., Leinonen, J., Luxton-Reilly, A., Malmi, L., Prather, J., and Quille, K. (2023). Generative ai in introductory programming. Computer Science Curricula 2023, Curricular Practices Volume.
Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., and Gazit, I. (2022). Taking flight with copilot: Early insights and opportunities of ai-powered pair-programming tools. ACM Queue, 20(6):35–57.
Carvalho, S. and 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, Porto Alegre, RS, Brasil. SBC.
Chan, W. K., Yu, Y. T., Keung, J. W., and Lee, V. C. (2023). Toward ai-assisted exercise creation for first course in programming through adversarial examples of ai models. In 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T), pages 132–136.
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De Silva, D., Jayasinghe, P., Illesinghe, A., Mallawaarachchi, D., Vithanage, C., and Vidhanaarachchi, S. (2024). Coderookie: Educational java programming environment for beginners. Lecture Notes in Networks and Systems, 1013 LNNS:243 – 253.
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.
Dibia, V., Fourney, A., Bansal, G., Poursabzi-Sangdeh, F., Liu, H., and Amershi, S. (2023). Aligning offline metrics and human judgments of value for code generation models.
Domingo, C. (2023). Recording multimodal pair-programming dialogue for reference resolution by conversational agents. In Proceedings of the 25th International Conference on Multimodal Interaction, ICMI ’23, page 731–735, New York, NY, USA. Association for Computing Machinery.
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Filho, L. P., Souza, T., and Paula, L. (2023). Análise das respostas do chatgpt em relação ao conteúdo de programação para iniciantes. In Anais do XXXIV Simpósio Brasileiro de Informática na Educação, pages 1738–1748, Porto Alegre, RS, Brasil. SBC.
Górecki, J. (2024). Pair programming with chatgpt for sampling and estimation of copulas. Comput. Stat., 39(6):3231–3261.
Groothuijsen, S., van den Beemt, A., Remmers, J. C., and 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.
Hassany, M., Brusilovsky, P., Ke, J., Akhuseyinoglu, K., and Narayanan, A. B. L. (2024). Engaging an llm to explain worked examples for java programming: Prompt engineering and a feasibility study. In CEUR Workshop Proceedings, volume 3840.
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Jiang, L., Bin Ahmadon, M. A., Zhang, D., Fukada, T., and Yamaguchi, S. (2023). Synergyai: An human-ai pair programming design tool based on program net. In 2023 11th International Conference on Information and Education Technology (ICIET), pages 210–214.
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Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., and 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.
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Kuttal, S. K., Sedhain, A., and AuBuchon, J. (2021). Designing a gender-inclusive conversational agent for pair programming: An empirical investigation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12797 LNAI:59 – 75.
Liu, J. and Li, S. (2024). Toward artificial intelligence-human paired programming: A review of the educational applications and research on artificial intelligence code-generation tools. Journal of Educational Computing Research, 62(5):1385 – 1415.
Ma, Q., Shen, H., Koedinger, K., and Wu, S. T. (2024). How to teach programming in the ai era? using llms as a teachable agent for debugging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14829 LNAI:265 – 279.
Ma, Q., Wu, T., and Koedinger, K. (2023). Is ai the better programming partner? human-human pair programming vs. human-ai pair programming.
Manfredi, G., Erra, U., and Gilio, G. (2023). A mixed reality approach for innovative pair programming education with a conversational ai virtual avatar. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, EASE ’23, page 450–454, New York, NY, USA. Association for Computing Machinery.
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Moradi Dakhel, A., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., and Jiang, Z. M. J. (2023). Github copilot ai pair programmer: Asset or liability? Journal of Systems and Software, 203:111734.
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Banić, B., Konecki, M., and Konecki, M. (2023). Pair programming education aided by chatgpt. In 2023 46th MIPRO ICT and Electronics Convention (MIPRO). IEEE.
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Bassner, P., Frankford, E., and Krusche, S. (2024). Iris: An ai-driven virtual tutor for computer science education. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, page 394–400, New York, NY, USA. Association for Computing Machinery.
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Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., and Gazit, I. (2022). Taking flight with copilot: Early insights and opportunities of ai-powered pair-programming tools. ACM Queue, 20(6):35–57.
Carvalho, S. and 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, Porto Alegre, RS, Brasil. SBC.
Chan, W. K., Yu, Y. T., Keung, J. W., and Lee, V. C. (2023). Toward ai-assisted exercise creation for first course in programming through adversarial examples of ai models. In 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T), pages 132–136.
Chen, T. (2024). The impact of ai-pair programmers on code quality and developer satisfaction: Evidence from timi studio. In Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security, GAIIS ’24, page 201–205, New York, NY, USA. Association for Computing Machinery.
De Silva, D., Jayasinghe, P., Illesinghe, A., Mallawaarachchi, D., Vithanage, C., and Vidhanaarachchi, S. (2024). Coderookie: Educational java programming environment for beginners. Lecture Notes in Networks and Systems, 1013 LNNS:243 – 253.
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.
Dibia, V., Fourney, A., Bansal, G., Poursabzi-Sangdeh, F., Liu, H., and Amershi, S. (2023). Aligning offline metrics and human judgments of value for code generation models.
Domingo, C. (2023). Recording multimodal pair-programming dialogue for reference resolution by conversational agents. In Proceedings of the 25th International Conference on Multimodal Interaction, ICMI ’23, page 731–735, New York, NY, USA. Association for Computing Machinery.
Dos Santos, O. L. and Cury, D. (2023). Challenging the confirmation bias: Using chatgpt as a virtual peer for peer instruction in computer programming education. In Proceedings - Frontiers in Education Conference, FIE.
Felizardo, K. R., Nakagawa, E. Y., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão sistemática da literatura em engenharia de software: teoria e prática. Elsevier, Rio de Janeiro, Brasil, 1 edition.
Feng, L., Yen, R., You, Y., Fan, M., Zhao, J., and Lu, Z. (2024a). Coprompt: Supporting prompt sharing and referring in collaborative natural language programming. In Proceedings of the CHI Conference on Human Factors in Computing Systems, page 1–21. ACM.
Feng, T., Liu, S., and Ghosal, D. (2024b). Courseassist: Pedagogically appropriate ai tutor for computer science education. In Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 2, SIGCSE Virtual 2024, page 310–311, New York, NY, USA. Association for Computing Machinery.
Filho, L. P., Souza, T., and Paula, L. (2023). Análise das respostas do chatgpt em relação ao conteúdo de programação para iniciantes. In Anais do XXXIV Simpósio Brasileiro de Informática na Educação, pages 1738–1748, Porto Alegre, RS, Brasil. SBC.
Górecki, J. (2024). Pair programming with chatgpt for sampling and estimation of copulas. Comput. Stat., 39(6):3231–3261.
Groothuijsen, S., van den Beemt, A., Remmers, J. C., and 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.
Hassany, M., Brusilovsky, P., Ke, J., Akhuseyinoglu, K., and Narayanan, A. B. L. (2024). Engaging an llm to explain worked examples for java programming: Prompt engineering and a feasibility study. In CEUR Workshop Proceedings, volume 3840.
Imai, S. (2022). Is github copilot a substitute for human pair-programming? an empirical study. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, ICSE ’22, page 319–321, New York, NY, USA. Association for Computing Machinery.
Jiang, L., Bin Ahmadon, M. A., Zhang, D., Fukada, T., and Yamaguchi, S. (2023). Synergyai: An human-ai pair programming design tool based on program net. In 2023 11th International Conference on Information and Education Technology (ICIET), pages 210–214.
Jiang, L., Yamaguchi, S., and Bin Ahmadon, M. A. (2025). Synergyai: A human–ai pair programming tool based on dataflow †. Information (Switzerland), 16(3).
Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., and 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. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, ver. 2.3 ebse technical report. ebse.
Kuttal, S. K., Sedhain, A., and AuBuchon, J. (2021). Designing a gender-inclusive conversational agent for pair programming: An empirical investigation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12797 LNAI:59 – 75.
Liu, J. and Li, S. (2024). Toward artificial intelligence-human paired programming: A review of the educational applications and research on artificial intelligence code-generation tools. Journal of Educational Computing Research, 62(5):1385 – 1415.
Ma, Q., Shen, H., Koedinger, K., and Wu, S. T. (2024). How to teach programming in the ai era? using llms as a teachable agent for debugging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14829 LNAI:265 – 279.
Ma, Q., Wu, T., and Koedinger, K. (2023). Is ai the better programming partner? human-human pair programming vs. human-ai pair programming.
Manfredi, G., Erra, U., and Gilio, G. (2023). A mixed reality approach for innovative pair programming education with a conversational ai virtual avatar. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, EASE ’23, page 450–454, New York, NY, USA. Association for Computing Machinery.
Manorat, P., Tuarob, S., and Pongpaichet, S. (2025). Artificial intelligence in computer programming education: A systematic literature review. Computers and Education: Artificial Intelligence, 8:100403.
Moradi Dakhel, A., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., and Jiang, Z. M. J. (2023). Github copilot ai pair programmer: Asset or liability? Journal of Systems and Software, 203:111734.
Nguyen, N. and Nadi, S. (2022). An empirical evaluation of github copilot’s code suggestions. In 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), pages 1–5.
Norton, V., Honda, F., Pessoa, M., and 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, Porto Alegre, RS, Brasil. SBC.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372.
Paludo, G. and Montresor, A. (2024). Fostering metacognitive skills in programming: Leveraging ai to reflect on code. In CEUR Workshop Proceedings, volume 3879.
Phung, T., Pădurean, V.-A., Cambronero, J., Gulwani, S., Kohn, T., Majumdar, R., Singla, A., and Soares, G. (2023). Generative ai for programming education: Benchmarking chatgpt, gpt-4, and human tutors. In ICER ’23: Proceedings of the 2023 ACM Conference on International Computing Education Research, ICER ’23, page 41–42, New York, NY, USA. Association for Computing Machinery.
Piccolo, S. R., Denny, P., Luxton-Reilly, A., Payne, S. H., and Ridge, P. G. (2023). Evaluating a large language model’s ability to solve programming exercises from an introductory bioinformatics course. PLoS Computational Biology, 19(9):e1011511.
Puryear, B. and Sprint, G. (2022). Github copilot in the classroom: learning to code with ai assistance. J. Comput. Sci. Coll., 38(1):37–47.
Rao, L., Liu, S., and Liu, A. (2024). Program segment testing for human-machine pair programming. Int. J. Softw. Eng. Knowl. Eng., 34(10):1565–1591.
Rasnayaka, S., Wang, G., Shariffdeen, R., and Iyer, G. N. (2024). An empirical study on usage and perceptions of llms in a software engineering project. In Proceedings of the 1st International Workshop on Large Language Models for Code, LLM4Code ’24, page 111–118, New York, NY, USA. Association for Computing Machinery.
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Publicado
24/11/2025
Como Citar
BEDIN, Heloise Acco Tives; MARIOTI, Madianita Bogo; JAQUES, Patricia.
Generative AI Assisting Programming Learning: Functional Roles, Comparative Analysis, and Emerging Challenges. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 408-425.
DOI: https://doi.org/10.5753/sbie.2025.12361.
