The knowledge of the mathematics teacher in the age of artificial intelligence: a systematic review

Authors

DOI:

https://doi.org/10.5007/1982-5153.2026.e109836

Keywords:

Mathematics Education, Teacher Education, Artificial Intelligence, AI-TPACK, Methodi Ordinatio

Abstract

This article analyzes the relationship between mathematics teachers' knowledge and the integration of Artificial Intelligence (AI) into education. To this end, a systematic literature review was conducted using the Methodi Ordinatio methodology, focusing on studies published between 2018 and 2025 in high-impact journals. The qualitative analysis focused on the top ten articles, out of a total of thirty selected, which discuss topics such as the role of teachers and their professional knowledge; transformations in teaching practice mediated by AI; connections between pedagogical, mathematical, and technological knowledge; the impacts of AI in the classroom; competencies required for the pedagogical use of AI; and gaps in teacher training. The results indicate that Artificial Intelligence is redefining the role of teachers, requiring the integration of traditional and technological knowledge. Despite the benefits, such as personalized teaching, challenges related to algorithmic aspects and privacy risks are also evident, highlighting the need for teacher training that prepares teachers for ethical, critical, and mediating practices.

Author Biographies

Loryane Santos de Oliveira, Universidade Tecnológica Federal do Paraná

Loryane Santos de Oliveira is a doctoral candidate in Science and Technology Education at the Federal University of Technology – Paraná (UTFPR), Ponta Grossa campus, affiliated with the Graduate Program in Science and Technology Education (PPGECT). She holds a Bachelor’s degree in Mathematics from UTFPR – Cornélio Procópio campus (2021) and a Professional Master’s degree in Mathematics Education from UTFPR – Cornélio Procópio and Londrina campuses (2024). Her research focuses on the teaching and learning of Mathematics, with emphasis on mathematical reasoning, exploratory tasks, and teacher education. Her academic trajectory is marked by the investigation of processes that foster the development of mathematical reasoning through formative and reflective practices, contributing to teacher professional growth and the improvement of Mathematics education.

André Luis Trevisan , Federal University of Technology Paraná

André Luis Trevisan is a full professor in the Department of Mathematics at the Federal University of Technology – Paraná (UTFPR) and a permanent faculty member of the Graduate Program in Science and Technology Education (PPGECT), Ponta Grossa campus. He holds a PhD in Science and Mathematics Education from the State University of Londrina (2013), a Master’s degree in Applied Mathematics (Unicamp, 2008), a Bachelor’s and Licentiate degree in Applied and Computational Mathematics (Unicamp, 2005), and a Licentiate degree in Pedagogy (Unifatecie, 2021). He completed a postdoctoral fellowship at the Federal University of ABC (2018–2019). Since 2019, he has been the leader of the research group “Mathematical Reasoning and Teacher Education” and has experience in Mathematics and Mathematics Education, with emphasis on the teaching of Calculus and on the development of mathematical reasoning in teacher education. He participates in continuing education programs in collaboration with school networks and serves as deputy coordinator of SBEM’s Working Group 4 – Mathematics Education in Higher Education. He coordinated the Professional Master’s Program in Mathematics Education (PPGMAT – UTFPR/CP-LD) from 2015 to 2016 and is a productivity fellow of Fundação Araucária (2019–2021; 2024–present).

Eliane Maria de Oliveira Araman , Universidade tecnológica Federal do Paraná

Eliane Maria de Oliveira Araman is a faculty member in the Department of Mathematics at the Federal University of Technology – Paraná (UTFPR), Cornélio Procópio campus. She holds a Bachelor’s degree in Science Teaching with a concentration in Mathematics from the Center for Higher Studies of Londrina (1994), as well as a Master’s degree (2006) and a PhD (2011) in Science and Mathematics Education from the State University of Londrina (UEL). She is a permanent professor in the Graduate Programs in Mathematics Education (PPGMAT) and in Science and Technology Education (PPGECT) at UTFPR. In 2019, she completed a postdoctoral fellowship at the Institute of Education of the University of Lisbon. She leads the research groups “Perspectives on Teaching and Learning in Mathematics” and “Mathematical Reasoning and Teacher Education.” Her research focuses on the History of Mathematics in Mathematics Education, Mathematical Reasoning, and Teacher Education.

S, Universidade tecnológica Federal do Paraná

Sani de Carvalho Rutz da Silva é professora titular da Universidade Tecnológica Federal do Paraná
(UTFPR). Licenciada em Matemática pela UEPG, possui mestrado em Matemática Aplicada e doutorado em
Ciência dos Materiais pela Universidade Federal do Rio Grande do Sul (UFRGS). Atua nas áreas de Ensino
de Matemática, Educação Matemática Inclusiva e uso de Tecnologias no ensino. Participou do programa
internacional PREFALC (França-Colômbia-Brasil) e é professor responsável externo do Mestrado
Interdisciplinar Multicultural em Inovação Educativa, Tecnológica e Gestão do Conhecimento (México
BUAP/INSA de Lyon). Foi orientadora do trabalho vencedor do Prêmio CAPES de Tese (2018) na área de
Ensino e é membro do Comitê de Assessores de Ciências Humanas da Fundação Araucária (2020–2024).
Coordenou o projeto internacional Rethinking Teacher Education, financiado pela Teachers College e
Fundação Lemann. Na UTFPR, integrou o Conselho de Pesquisa e Pós-Graduação (2015–2023) e atua
como professora permanente do PPGECT (mestrado e doutorado). É líder do grupo “O Ensino e a Inclusão
de Pessoas com Deficiência”, editora-chefe da RBECT e membro da ANPEd, SBEM e RedDOLAC.

Alessandra Dutra , Universidade tecnológica Federal do Paraná

Alessandra Dutra é professora titular da Universidade Tecnológica Federal do Paraná (UTFPR). Possui
graduação em Letras – Anglo e Literaturas (1997), mestrado em Estudos da Linguagem (2003) pela
Universidade Estadual de Londrina (UEL), além de doutorado em Linguística e Língua Portuguesa pela
Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP, 2008). Atuou como coordenadora do
Programa de Pós-Graduação em Ensino de Ciências Humanas, Sociais e da Natureza (PPGEN) da UTFPR
entre 2013 e 2017, e como coordenadora adjunta de 2017 a 2019. Atualmente, é professora permanente do
Programa de Pós-Graduação em Ensino de Ciência e Tecnologia (PPGECT), em Ponta Grossa (PR). Sua
pesquisa concentra-se nas áreas de Multiletramentos e Letramentos Digitais, com ênfase em práticas
pedagógicas que promovem o desenvolvimento do Pensamento Computacional. Foi bolsista de
produtividade em pesquisa pela Fundação Araucária entre 2019 e 2021.

References

Almuhanna, M. A. (2025). Teachers’ perspectives of integrating AI-powered technologies in K-12 education for creating customized learning materials and resources. Education and Information Technologies, 30, 10343–10371.

Anderson, T. (2020). The role of AI in educational practices. Journal of Educational Technology & Society, 23(1), 56–70.

Bai, X., Wang, Y., Zhang, X., & Li, Z. (2024). Math teachers and AI: Potential and limitations. Frontiers in Education.

Ball, D. L., Thames, M. H., & Phelps, G. (2008). Content knowledge for teaching: What makes it special? Journal of Teacher Education, 59(5), 389–407.

Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.

Elias, H. R. (2017). Fundamentos teórico-metodológicos para o ensino do corpo dos números racionais na formação de professores de matemática [Tese de Doutorado, Universidade Estadual de Londrina]. Universidade Estadual de Londrina.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Imran, A., & Almusharraf, N. (2024). Teacher readiness and ethical concerns in AI literacy. Education Sciences.

Kim, H., Park, S., Lee, J., & Choi, Y. (2024). AI literacy and mathematics teacher preparation. Journal of Technology and Teacher Education.

Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University Technical Report TR/SE-0401. Keele University.

Koehler, M. J., Mishra, P., Kereluik, K., Shin, T. S., & Graham, C. (2013). The technological pedagogical content knowledge framework. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (pp. 101–111). Springer.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Meylani, R. (2024). Artificial Intelligence in Mathematics Teacher Education: A Systematic Review and Qualitative Synthesis of Contemporary Research Literature. International journal of Technology in Education Science, 1(1), 63-91.

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

Ng, D. T. K., Chan, K., & Lo, B. (2025). Opportunities, challenges and strategies for integrating generative AI in education. Computers and Education: Artificial Intelligence.

Niess, M. L. (2005). Preparing teachers to teach science and mathematics with technology: Developing technology pedagogical content knowledge. Teaching and Teacher Education, 21(5), 509–523.

Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16, 1–17.

Okoli, C. (2015). A guide to conducting a systematic literature review of information systems research. Sprouts: Working Papers on Information Systems, 10(26), 1–49.

OpenAI. (2022). Introducing ChatGPT. OpenAI.

Pagani, R. N., Kovaleski, J. L., & Resende, L. M. (2015). Methodi Ordinatio: A proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, 105(3), 2109–2135.

Saragih, I. (2024). Integrating AI in mathematics teaching: Teachers’ attitudes. International Journal of STEM Education.

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.

Sperling, K., Stenberg, C. J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169.

Stokel-Walker, C. (2023). ChatGPT listed as author on research papers: Many scientists disapprove. Nature, 613(7945), 620–621.

Sun, J., Ma, H., Zeng, Y., Han, D., & Jin, Y. (2023). Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach. Education and Information Technologies, 28(2), 1509–1533.

Tardif, M. (2014). conhecimentos profissionais e formação profissional (14a ed.). Vozes.

UNESCO. (2024). AI competency framework for teachers. UNESCO.

Uygun, D. (2024). Teachers’ perspectives on artificial intelligence in education. Advances in Mobile Learning Educational Research, 4(1), 931–941.

Yan, L., Sha, L., Zhao, L., Chai, C. S., Jong, M. S. Y., Istenič, A., Spector, J. M., Liu, J. B., Yuan, J., & Li, Y. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review.

Published

2026-08-13

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Section

Articles