Da Intuição em IA à Literacia em IA: Um Quadro Dual para a Ensino Básico e Secundário
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Resumo
A literacia em inteligência artificial tem-se afirmado como um quadro central para orientar a integração da IA na educação básica e secundária, enfatizando a compreensão conceptual, a aplicação prática, a avaliação crítica e o julgamento ético. Estas competências são essenciais para promover uma utilização informada e responsável dos sistemas de IA. No entanto, a prática em sala de aula sugere que os estudantes frequentemente aprendem a interagir com a IA generativa através de interações exploratórias e experienciais: experimentam instruções, observam o comportamento do sistema, ajustam estratégias e refletem sobre os resultados, antes de conseguirem formular plenamente conceitos ou regras formais. Para dar conta desta dimensão experiencial da aprendizagem, este artigo introduz a noção de intuição em IA como um constructo educativo complementar. A intuição em IA é definida como uma forma de compreensão experiencial e indutiva que se desenvolve através da interação iterativa com sistemas de IA e sustenta um julgamento contextual em situações de incerteza. Propomos um modelo dual no qual a alfabetização em IA fornece conhecimentos explícitos, critérios críticos e orientação ética, enquanto a intuição da IA se desenvolve por meio da experiência iterativa e da reflexão. Essas duas dimensões interagem de forma dinâmica e se reforçam mutuamente. Discutimos implicações pedagógicas para a educação básica e secundária e delineamos direções para investigação futura sobre aprendizagem experiencial com IA..
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Referências
Abdi, A., Hassani, P., Jalali, R., & Salari, N. (2016). Use of intuition by critical care nurses: A phenomenological study. Advances in Medical Education and Practice, 7, 65–71. https://doi.org/10.2147/amep.s100324 DOI: https://doi.org/10.2147/AMEP.S100324
AI4K12. (n.d.). AI for K-12. https://ai4k12.org Accessed August 14, 2025.
Barana, A., Marchisio, M., & Sacchet, M. (2023, October 21-23). Fostering problem solving and critical thinking in mathematics through generative artificial intelligence [Paper presentation]. IADIS International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA), Madeira Island, Portugal. https://eric.ed.gov/?id=ED636445
Beau, M., & Lazar, M. (2025). AI in K-12 education: Teaching in practice [White paper]. International School of Boston. https://resources.finalsite.net/images/v1760105856/isboston/njqucrdzeyxvmtjr4cla/WhitePaper_AI_October2025.pdf
Berliner, D. C. (2001). Learning about and learning from expert teachers. International Journal of Educational Research, 35(5), 463–482. DOI: https://doi.org/10.1016/S0883-0355(02)00004-6
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, Article 100171. https://doi.org/10.1016/j.caeo.2024.100171 DOI: https://doi.org/10.1016/j.caeo.2024.100171
Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. The Academy of Management Review, 24(3), 522–537. https://doi.org/10.2307/259140 DOI: https://doi.org/10.2307/259140
Dao, X. Q., & Le, N. B. (2023). Investigating the effectiveness of ChatGPT in mathematical reasoning and problem solving: Evidence from the Vietnamese national high school graduation examination. arXiv. https://arxiv.org/abs/2306.06331
Descartes, R. (1985). The philosophical writings of Descartes (J. Cottingham, R. Stoothoff, & D. Murdoch, Trans.). Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511805042
Dewey, J. (1933). How we think. D. C. Heath.
Eraut, M. (2000). Non‐formal learning and tacit knowledge in professional work. British Journal of Educational Psychology, 70(1), 113–136. DOI: https://doi.org/10.1348/000709900158001
Flechtner, R., & Kilian, J. (2024). Making (non-)sense—A playful and explorative approach to teaching AI intuition for the design of sensor-based interactions. In Proceedings of the 6th Annual Symposium on HCI Education (EduCHI ’24). ACM. https://doi.org/10.1145/3658619.3658643 DOI: https://doi.org/10.1145/3658619.3658643
Flechtner, R., & Stankowski, A. (2023). AI is not a wildcard: Challenges for integrating AI into the design curriculum. In Proceedings of the 5th Conference on Creativity and Cognition (pp. 1–8). ACM. https://doi.org/10.1145/3587399.3587410 DOI: https://doi.org/10.1145/3587399.3587410
Foucault, M. (1994). The order of things: An archaeology of the human sciences. Vintage Books. (Original work published 1966)
Gardner-McTaggart, A., Blyth, C. Ontological capture: AI, childhood, and the algorithmic governance of becoming. AI & Soc (2025). https://doi.org/10.1007/s00146-025-02807-8 DOI: https://doi.org/10.1007/s00146-025-02807-8
Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. Viking.
Heidegger, M. (1986). Being and Time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927)
Hogarth, R. M. (2010). Educating intuition. University of Chicago Press.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526. https://doi.org/10.1037/a0016755 DOI: https://doi.org/10.1037/a0016755
Kant, I. (1998). Critique of pure reason (P. Guyer & A. Wood, Trans.). Cambridge University Press. (Original work published 1781). DOI: https://doi.org/10.1017/CBO9780511804649
Klopfer, E., Reich, J., Abelson, H., & Breazeal, C. (2024). Generative AI and K-12 education: An MIT perspective. An MIT Exploration of Generative AI. https://doi.org/10.21428/e4baedd9.81164b06 DOI: https://doi.org/10.21428/e4baedd9.81164b06
Köhler, W. (1929). Gestalt psychology. Liveright.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539
Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12783–12791. https://doi.org/10.1609/aaai.v36i11.21557 DOI: https://doi.org/10.1609/aaai.v36i11.21557
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410 DOI: https://doi.org/10.3390/educsci13040410
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727 DOI: https://doi.org/10.1145/3313831.3376727
Merleau-Ponty, M. (2016). Phenomenology of perception (D. Landes, Trans.). Routledge. (Original work published 1945)
National Council of Teachers of Mathematics. (2024). Artificial intelligence and mathematics teaching—A position of the National Council of Teachers of Mathematics. https://www.nctm.org/Standards-and-Positions/Position-Statements/Artificial-Intelligence-and-Mathematics-Teaching/
Nisbett, R. E., & Miyamoto, Y. (2005). The influence of culture: Holistic vs. analytic perception. Trends in Cognitive Sciences, 9(10), 467–473. https://doi.org/10.1016/j.tics.2005.08.004 DOI: https://doi.org/10.1016/j.tics.2005.08.004
Noë, A. (2004). Action in perception. MIT Press.
OECD & European Commission. (2025). AI Literacy Framework for Primary and Secondary Education. https://ailiteracyframework.org/
Pascal, B. (1963). Œuvres complètes (L. Lafuma, éd.). Éditions du Seuil.
Plato. (1992). Republic (G. M. A. Grube, Trans.; C. D. C. Reeve, Rev.). Hackett.
Price, A., Zulkosky, K., White, K., & Pretz, J. (2016). Accuracy of intuition in clinical decision‐making among novice clinicians. Journal of Advanced Nursing, 73(5), 1147–1157. https://doi.org/10.1111/jan.13202 DOI: https://doi.org/10.1111/jan.13202
Rane, N. (2023). Enhancing mathematical capabilities through ChatGPT and similar generative artificial intelligence: Roles and challenges in solving mathematical problems. SSRN. https://doi.org/10.2139/ssrn.4603237 DOI: https://doi.org/10.2139/ssrn.4603237
Reimers, F., Azim, Z., Palomo, MR., Thony, C. (2026). Education and Artificial Intelligence: A Systems Approach. In: Artificial Intelligence and Education in the Global South. Springer, Cham. https://doi.org/10.1007/978-3-032-11449-5_1 DOI: https://doi.org/10.1007/978-3-032-11449-5
Relmasira, S. C., Lai, Y. C., & Donaldson, J. P. (2023). Fostering AI literacy in elementary science, technology, engineering, art, and mathematics (STEAM) education in the age of generative AI. Sustainability, 15(18), 13595. https://doi.org/10.3390/su151813595 DOI: https://doi.org/10.3390/su151813595
Sánchez-Ruiz, L. M., Moll-López, S., Nuñez-Pérez, A., Moraño-Fernández, J. A., & Vega-Fleitas, E. (2023). ChatGPT challenges blended learning methodologies in engineering education: A case study in mathematics. Applied Sciences, 13(10), 6039. https://doi.org/10.3390/app13106039 DOI: https://doi.org/10.3390/app13106039
Sipman, G., Thölke, J., Martens, R., & McKenney, S. (2019). The role of intuition in pedagogical tact: Educator views. British Educational Research Journal, 45(6), 1186–1202. https://doi.org/10.1002/berj.3557 DOI: https://doi.org/10.1002/berj.3557
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795 DOI: https://doi.org/10.1609/aaai.v33i01.33019795
Touretzky, D.S., & Gardner-McCune, C. (2022). Artificial intelligence thinking in k-12. Computational Thinking Education in K-12. In S.-C. Kong & H. Abelson, Artificial Intelligence Literacy and Physical Computing pp. 153–180. MIT Press. https://doi.org/10.7551/mitpress/13375.003.0013 DOI: https://doi.org/10.7551/mitpress/13375.003.0013
UNESCO. (2023a). Guidance for generative AI in education and research. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
UNESCO. (2023b). AI competency framework for students. https://unesdoc.unesco.org/ark:/48223/pf0000391105
UNESCO. (2023c). AI competency framework for teachers. https://unesdoc.unesco.org/ark:/48223/pf0000391104
Vincent, V. (2021). Integrating intuition and artificial intelligence in organizational decision-making. Business Horizons. 64. https://doi.org/10.1016/j.bushor.2021.02.008 DOI: https://doi.org/10.1016/j.bushor.2021.02.008
Walsh, C., Collins, J. & Knott, P. (2021). The four types of intuition managers need to know. Business Horizons. 65. 10.1016/j.bushor.2021.12.003. DOI: https://doi.org/10.1016/j.bushor.2021.12.003
Wertheimer, M. (1923). Laws of organization in perceptual forms. In W. D. Ellis (Ed.), A sourcebook of Gestalt psychology. Routledge.
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9, 59. https://doi.org/10.1186/s40594-022-00377-5 DOI: https://doi.org/10.1186/s40594-022-00377-5
Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2023). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 33, 290–324. https://doi.org/10.1007/s40593-022-00293-3 DOI: https://doi.org/10.1007/s40593-022-00293-3