Cabeçalho da página

Tecnologias de IA no ensino e na aprendizagem de Matemática: um mapeamento sistemático da literatura

Deusarino Oliveira Almeida Junior, José Messildo Nunes

Resumo

Este artigo apresenta um mapeamento sistemático sobre o uso de tecnologias de Inteligência Artificial (IA) no ensino e na aprendizagem de Matemática, estruturado pela estratégia POT e orientado pelo protocolo PRISMA 2020. A busca nas bases ERIC, SciELO e Portal CAPES resultou em 20 estudos selecionados. A análise evidencia que tutores inteligentes, agentes ensináveis, algoritmos de machine learning, robôs educacionais e modelos de linguagem podem favorecer personalização da aprendizagem, feedback imediato, suporte investigativo e desenvolvimento de competências matemáticas. Também foram identificados desafios éticos, limitações metodológicas e lacunas na formação docente para o uso crítico da IA. Os resultados reforçam potencialidades, indicam oportunidades para novas práticas na Educação Matemática e apontam direções para pesquisas futuras.


Palavras-chave

ensino de matemática; inteligência artificial; revisão sistemática; prisma 2020


Texto completo:

PDF

Referências


ABOUELENEIN, Y.; ELMAADAWAY, M. Artificial intelligence and computational thinking in mathematics education: an integrated approach. Education and Information Technologies, 2023.

ARROYO, I.; ROYER, J.; WOOLF, B. Improving math learning through intelligent tutoring and math fluency training. International Journal of Artificial Intelligence in Education, v. 21, n. 1–2, p. 135-152, 2011.

BAHADIR, E. Using artificial neural networks for prediction of mathematics achievement: input from cognitive and affective variables. Education and Science, v. 41, n. 183, p. 41-57, 2016.

BONADIMAN BERTOL, Daniel et al. Uso de tecnologias digitais no ensino de matemática para alunos com transtorno do espectro autista (TEA): uma revisão sistemática. Amazônia: Revista de Educação em Ciências e Matemáticas, Belém, v. 21, n. 47, p. 19-39, out. 2025.

CAMPOS, M. M.; LASTÓRIA, R. C. R. Tecnologias digitais, educação e semiformação: problematizações a partir de Adorno. Educação e Pesquisa, v. 46, e223517, 2020.

CAPINDING, A. T. Enhancing students’ mathematical dispositions through AI-supported inquiry. International Journal of Learning, Teaching and Educational Research, v. 22, n. 2, p. 18-35, 2023.

CHEVALLARD, Y. L’analyse des pratiques enseignantes en théorie anthropologique du didactique. In: Recherches en Didactique des Mathématiques. Grenoble: La Pensée Sauvage, 1999.

DEL ZOZZO, A.; SANTI, G. R. Learning geometry with GGBot: perceptual reorganizations in a robotic environment. Digital Experiences in Mathematics Education, v. 9, p. 67-89, 2023.

GAO, J. Exploring the integration of big data technologies in college mathematics teaching. Journal of Applied Mathematics and Physics, v. 8, p. 1-10, 2020.

GARCÍA, J.; CATARREIRA, Sofía María Veríssimo; GONZÁLEZ, Ricardo Luengo. Contraste en la percepción sobre el uso de una plataforma virtual para la mejora de la enseñanza y aprendizaje de las matemáticas. Revista Ibérica de Sistemas e Tecnologias de Informação, n. 38, p. 33-47, 2020.

GOVENDER, R. The impact of artificial intelligence and the future of ChatGPT for mathematics teaching and learning in schools and higher education. African Journal of Research in Mathematics, Science and Technology Education, v. 27, n. 1, p. 1-15, 2023.

GÜNEL, A.; AŞLIYAN, R. A dynamic difficulty adjustment model using differential equations in intelligent tutoring systems. Procedia - Social and Behavioral Sciences, v. 1, p. 729-733, 2009.

HAN, X. et al. Personalized mathematics micro-learning using AI-driven analytics. Computers & Education: Artificial Intelligence, v. 3, 2022.

Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230

HOLMES, W. et al. Artificial Intelligence in education: promises and implications for teaching and learning. Paris: OECD Publishing, 2022.

HOLMES, W.; TUOMI, I. State of the art and practice in artificial intelligence in education. Luxembourg: Publications Office of the European Union, 2022. Disponível em: https://discovery.ucl.ac.uk/id/eprint/10158374/1/Holmes%20and%20Tuomi%20-%202022%20-%20State%20of%20the%20art%20and%20practice%20in%20AI%20in%20education.pdf. Acesso em: 09 jan. 2026.

KITCHENHAM, B.; CHARTERS, S. Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report, 2007.

MORENO-ESTEVA, E. et al. Eye-tracking and machine learning for analyzing students’ interpretation of statistical graphs. International Journal of Science and Mathematics Education, v. 16, p. 131-150, 2018.

PAGE, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, v. 372, n. 71, p. 1-9, 2021.

PARREIRA, J. S. et al. Artificial intelligence in education: teacher knowledge, perceptions, and concerns. Revista Brasileira de Informática na Educação, v. 29, p. 67-90, 2021.

QIAO, Yan et al. Study on obe teaching concept in the context of deep learning for the construction of university mathematics microcourses. Computational intelligence and neuroscience, v. 2022, 2022.

RABARDEL, P. Les hommes et les technologies: approche cognitive des instruments contemporains. Paris: Armand Colin, 1995.

SANTOS, C. M. C.; PIMENTA, C. A. M.; NOBRE, M. R. C. A estratégia PICOT para a construção da pergunta de pesquisa e busca de evidências. Revista Latino-Americana de Enfermagem, v. 15, n. 3, p. 508-511, 2007.

SKLAVAKIS, D.; REFANIDIS, I. MATHESIS: an AI-driven tutor for personalized mathematics learning. Educational Technology & Society, v. 16, n. 2, p. 296-308, 2013.

SHIN, D. Pedagogical perspectives for integrating artificial intelligence into mathematics classrooms. Educational Studies in Mathematics, v. 110, p. 1-20, 2022.

SOESANTO, R.; DIRGANTORO, S.; PRIYANTI, I. Indonesian students’ perceptions towards AI-based learning in mathematics. International Journal of Emerging Technologies in Learning, v. 17, n. 13, p. 153-167, 2022.

SOMMER, A.; NUCKOLS, G. Automated formal reasoning systems for teaching proof strategies. Journal of Automated Reasoning, v. 33, p. 1-25, 2004.

SONG, Y. Learning by teaching with AI-based teachable agents: effects on mathematical reasoning. Computers & Education, v. 108, p. 73-89, 2017.

WARDAT, Y. et al. Exploring mathematics teachers’ perceptions of ChatGPT as an instructional assistant. Journal of Mathematics Teacher Education, 2023.




DOI: http://dx.doi.org/10.18542/amazrecm.v22i48.19945

Direitos autorais 2026 Deusarino Oliveira Almeida Junior, José Messildo Nunes

Creative Commons License