Inteligência Artificial Generativa no Ensino de Computação: Limitações Estruturais e o Novo Paradigma Educacional
Resumo
O uso de ferramentas de inteligência artificial (IA) na academia intensifica debates sobre ética e inovação pedagógica. Este artigo investiga se tal fenômeno reflete lacunas estruturais no ensino. Por meio de abordagem qualitativa teórico-reflexiva, discute-se o papel do Estado, universidades e docentes na promoção da inovação. O estudo foca nos desafios dos cursos da área da Computação, onde ferramentas de automação de código desafiam avaliações tradicionais baseadas em sintaxe. Argumenta-se que a tecnologia, sem políticas públicas e formação docente, é insuficiente para mudanças estruturais. O trabalho evidencia desafios e vias para um modelo educacional alinhado à Educação em Computação.Referências
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Bommasani, R. et al. (2021) “On the Opportunities and Risks of Foundation Models”, arXiv preprint arXiv:2108.07258.
Castells, M. (1999), A sociedade em rede, São Paulo, Paz e Terra.
Cuban, L. (2001), Oversold and Underused: Computers in the Classroom, Cambridge, Harvard University Press.
Denny, P. et al. (2023) “Computing Education in the Era of Generative AI”, Communications of the ACM, v. 67, n. 2, p. 56–67.
Finnie-Ansley, J. et al. (2022) “The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming”, In: Proceedings of the 24th Australasian Computing Education Conference (ACE 2022), p. 10–19.
Hassan, R. (2008), The Information Society: Cyber Dreams and Digital Nightmares, Cambridge, Polity Press.
Henderson, C., Beach, A. and Finkelstein, N. (2011) “Facilitating Change in Undergraduate STEM Instructional Practices: An Analytic Review of the Literature”, Journal of Research in Science Teaching, v. 48, n. 8, p. 952–984.
Kasneci, E. et al. (2023) “ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education”, Learning and Individual Differences, v. 103, Article 102274.
Kenski, V. M. (2012), Tecnologias e ensino presencial e a distância, 9th edition, Campinas, Papirus.
Lau, S. and Nekoto, W. (2023) “Towards Responsible Generative AI in Data Science Education: Opportunities, Challenges and Implications for Teaching and Learning”, In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education (ITiCSE 2023), New York, ACM.
Lévy, P. (1999), Cibercultura, São Paulo, Editora 34.
Lister, R. (2011) “Concrete Thinking and Program Design”, In: Proceedings of the 13th Australasian Computing Education Conference (ACE 2011), p. 153–162.
Luxton-Reilly, A. et al. (2018) “Review of Introductory Programming Literature”, In: Proceedings of the 2018 ACM Conference on International Computing Education Research (ICER 2018), p. 219–228.
Moran, J. M. (2013), A educação que desejamos: novos desafios e como chegar lá, 5th edition, Campinas, Papirus.
Papert, S. (1980), Mindstorms: Children, Computers, and Powerful Ideas, New York, Basic Books.
Qureshi, B. (2023) “Exploring the Use of ChatGPT as a Tool for Learning and Assessment in Undergraduate Computer Science Curriculum”, In: Proceedings of the 2023 IEEE Frontiers in Education Conference (FIE 2023).
Robins, A. V. (2010) “Learning Edge Momentum: A New Explanation of Outcomes in Introductory Programming”, Computer Science Education, v. 20, n. 1, p. 37–71.
Selwyn, N. (2016), Education and Technology: Key Issues and Debates, 2nd edition, London, Bloomsbury Academic.
Tardif, M. (2002), Saberes docentes e formação profissional, Petrópolis, Vozes.
Toffler, A. (2007), A terceira onda, 28th edition, Rio de Janeiro, Record.
UNESCO (2023), Guidance for Generative AI in Education and Research, Paris, UNESCO.
Valente, J. A. (1999), O computador na sociedade do conhecimento, Campinas, UNICAMP/NIED.
Yilmaz, R. and Yilmaz, F. G. K. (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, v. 5, Article 100147.
Bauman, Z. (2007), Tempos líquidos, Rio de Janeiro, Zahar.
Bender, E. M., Gebru, T., McMillan-Major, A. and Shmitchell, S. (2021) “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”, In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), p. 610–623.
Bommasani, R. et al. (2021) “On the Opportunities and Risks of Foundation Models”, arXiv preprint arXiv:2108.07258.
Castells, M. (1999), A sociedade em rede, São Paulo, Paz e Terra.
Cuban, L. (2001), Oversold and Underused: Computers in the Classroom, Cambridge, Harvard University Press.
Denny, P. et al. (2023) “Computing Education in the Era of Generative AI”, Communications of the ACM, v. 67, n. 2, p. 56–67.
Finnie-Ansley, J. et al. (2022) “The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming”, In: Proceedings of the 24th Australasian Computing Education Conference (ACE 2022), p. 10–19.
Hassan, R. (2008), The Information Society: Cyber Dreams and Digital Nightmares, Cambridge, Polity Press.
Henderson, C., Beach, A. and Finkelstein, N. (2011) “Facilitating Change in Undergraduate STEM Instructional Practices: An Analytic Review of the Literature”, Journal of Research in Science Teaching, v. 48, n. 8, p. 952–984.
Kasneci, E. et al. (2023) “ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education”, Learning and Individual Differences, v. 103, Article 102274.
Kenski, V. M. (2012), Tecnologias e ensino presencial e a distância, 9th edition, Campinas, Papirus.
Lau, S. and Nekoto, W. (2023) “Towards Responsible Generative AI in Data Science Education: Opportunities, Challenges and Implications for Teaching and Learning”, In: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education (ITiCSE 2023), New York, ACM.
Lévy, P. (1999), Cibercultura, São Paulo, Editora 34.
Lister, R. (2011) “Concrete Thinking and Program Design”, In: Proceedings of the 13th Australasian Computing Education Conference (ACE 2011), p. 153–162.
Luxton-Reilly, A. et al. (2018) “Review of Introductory Programming Literature”, In: Proceedings of the 2018 ACM Conference on International Computing Education Research (ICER 2018), p. 219–228.
Moran, J. M. (2013), A educação que desejamos: novos desafios e como chegar lá, 5th edition, Campinas, Papirus.
Papert, S. (1980), Mindstorms: Children, Computers, and Powerful Ideas, New York, Basic Books.
Qureshi, B. (2023) “Exploring the Use of ChatGPT as a Tool for Learning and Assessment in Undergraduate Computer Science Curriculum”, In: Proceedings of the 2023 IEEE Frontiers in Education Conference (FIE 2023).
Robins, A. V. (2010) “Learning Edge Momentum: A New Explanation of Outcomes in Introductory Programming”, Computer Science Education, v. 20, n. 1, p. 37–71.
Selwyn, N. (2016), Education and Technology: Key Issues and Debates, 2nd edition, London, Bloomsbury Academic.
Tardif, M. (2002), Saberes docentes e formação profissional, Petrópolis, Vozes.
Toffler, A. (2007), A terceira onda, 28th edition, Rio de Janeiro, Record.
UNESCO (2023), Guidance for Generative AI in Education and Research, Paris, UNESCO.
Valente, J. A. (1999), O computador na sociedade do conhecimento, Campinas, UNICAMP/NIED.
Yilmaz, R. and Yilmaz, F. G. K. (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, v. 5, Article 100147.
Publicado
23/09/2026
Como Citar
JESUS, Maria Clara Simoes de; SILVA, Emyle Santana; AMBRÓSIO, Paulo Eduardo; ALMEIDA, Amanda Morais.
Inteligência Artificial Generativa no Ensino de Computação: Limitações Estruturais e o Novo Paradigma Educacional. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 26. , 2026, Maragogi/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 725-734.
DOI: https://doi.org/10.5753/erbase.2026.30411.
