Inteligência artificial na educação: potencial transformador, riscos de discriminação e desafios éticos
DOI:
https://doi.org/10.35362/rie9916838Palavras-chave:
inteligência artificial, educação, viés algorítmico, ética digital, personalização do aprendizadoResumo
A inteligência artificial (IA) tem se posicionado como uma ferramenta inovadora em muitas áreas, sendo a educação um dos setores em que seu impacto pode ser mais transformador. Da personalização do aprendizado à automação de processos administrativos, os aplicativos de IA na educação apresentam oportunidades sem precedentes, mas também riscos consideráveis, especialmente relacionados ao viés algorítmico. Este artigo explora, a partir de uma perspectiva crítica, o potencial da IA na educação, as implicações do viés algorítmico e a necessidade urgente de estruturas éticas e regulatórias. São integradas descobertas recentes, experiências práticas e debates contemporâneos, com o objetivo de promover uma implementação responsável, inclusiva e centrada nas pessoas
Downloads
Referências
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. https://go.oei.int/wz8qf1pg
Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. NESTA. https://www.nesta.org.uk/report/education-rebooted/
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, 149-159.
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (technology) is power: A critical survey of “bias” in NLP. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5454-5476. https://doi.org/10.18653/v1/2020.acl-main.485 DOI: https://doi.org/10.18653/v1/2020.acl-main.485
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 77–91.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510 DOI: https://doi.org/10.1109/ACCESS.2020.2988510
Cios, K. J., & Zapala, M. (2021). Ethics of AI and Big Data in Education. In Ethics of Artificial Intelligence and Robotics. Stanford Encyclopedia of Philosophy. https://go.oei.int/iquqdnio
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. DOI: https://doi.org/10.12987/9780300252392
Crawford, R., Kallitsis, M., & McKenna, L. (2021). Algorithmic injustice in education: The UK A-level grading scandal. Data & Society Institute. https://go.oei.int/arhp1l53
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press. https://data-feminism.mitpress.mit.edu/ DOI: https://doi.org/10.7551/mitpress/11805.001.0001
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. Interactions, 24(4), 38-42. https://doi.org/10.1145/3085558 DOI: https://doi.org/10.1145/3085558
Freire, P. (2014). Pedagogía del oprimido (30.ª ed.). Siglo XXI Editores. (Obra original publicada en 1970)
Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://go.oei.int/urx6xpik
Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? In CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3290605.3300830 DOI: https://doi.org/10.1145/3290605.3300830
Latonero, M., & Yeung, K. (2021). Governing artificial intelligence: Upholding human rights & dignity. Data & Society Research Institute. https://datasociety.net/wp-content/uploads/2021/07/Governing-AI.pdf
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Mohamed, S., Png, M. T., & Isaac, W. (2020). Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology, 33(4), 659-684. https://doi.org/10.1007/s13347-020-00405-8 DOI: https://doi.org/10.1007/s13347-020-00405-8
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. ASCD.
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59-68. https://doi.org/10.1145/3287560.3287598 DOI: https://doi.org/10.1145/3287560.3287598
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
UNESCO. (2021a). Artificial intelligence and education: Guidance for policy-makers.
UNESCO. (2021b). AI and gender equality: A global study on the use of artificial intelligence to support women’s empowerment. https://unesdoc.unesco.org/ark:/48223/pf0000377250
van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780190889760.001.0001
Williamson, B. (2022). Education platforms and the platformization of education policy. Learning, Media and Technology, 47(1), 12–25. https://doi.org/10.1080/17439884.2021.1987302
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995 DOI: https://doi.org/10.1080/17439884.2020.1798995
Woolf, B. P., Burleson, W., Arroyo, I., Dragon, T., Cooper, D. G., & Picard, R. W. (2021). Affect-aware tutors: Recognising and responding to student affect. In Advances in Intelligent Tutoring Systems (pp. 157–168). Springer. https://doi.org/10.1007/978-3-030-64452-9_10 DOI: https://doi.org/10.1007/978-3-030-64452-9_10
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0
Como Citar
Publicado
Edição
Seção
Licença
Copyright (c) 2025 Revista Ibero-americana de Educação

Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Os(as) autores(as) que publiquem nesta revista concordam com os seguintes termos:





