Generative Artificial Intelligence and Mathematics Education: Tensions Between Instrumental Use and Complex Learning
DOI:
https://doi.org/10.70577/asce.v5i2.844Keywords:
generative artificial intelligence, mathematics education, scoping review, mathematical modeling, self-regulated learning, critical thinking.Abstract
The accelerated incorporation of generative artificial intelligence (GenAI) into mathematics education raises questions about its capacity to foster cognitively complex learning. The literature has grown exponentially since the release of conversational models, yet a large portion of the field remains oriented toward instructional efficiency, technology adoption, and personalization, at the expense of high cognitive demand processes. The objective was to map and analyze the presence of three complex cognitive dimensions, mathematical modeling, self-regulated learning, and critical thinking, in the literature on GenAI and mathematics education published between 2020 and 2025. A qualitative approach with an exploratory and descriptive design was adopted, through a scoping review following the PRISMA-ScR protocol; 219 articles were retrieved from six databases (Web of Science, Scopus, ERIC, SciELO, IEEE Xplore, and ACM Digital Library) and 34 variables were coded per study. The results show an exponential acceleration in production (51.6 % in 2025), dominated by systematic reviews (80.4 %) and by ChatGPT/GPT (63.9 %), with problem solving accounting for 77.2 % of the mathematical processes addressed. Self-regulated learning appears in 31.5 % of the corpus, mathematical modeling in 13.2 %, and critical thinking in 8.2 %; 58.4 % address none and only 2.3 % integrate them simultaneously. It is concluded that the corpus reveals five structural tensions between instrumental use and complex learning, and four research gaps that shape a priority agenda for longitudinal empirical research situated in the Ibero-American context.
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Acuña, C. R. Y., y Marrero, C. M. (2025). Integración de metodologías activas e inteligencia artificial en la enseñanza del tema de sucesiones y series: una experiencia de clase en un curso semipresencial del Instituto Tecnológico de Costa Rica. Cuadernos de Investigación y Formación en Educación Matemática, 18(2), 135-160. https://doi.org/10.15517/eqxwfy21 DOI: https://doi.org/10.15517/eqxwfy21
Borromeo Ferri, R. (2021). Learning how to teach mathematical modeling in school and teacher education. Springer. https://doi.org/10.1007/978-3-319-68072-9 DOI: https://doi.org/10.1007/978-3-319-68072-9
Campos, M. J. E. (2024). Inteligencia artificial en la educación superior de Costa Rica: desafíos y oportunidades desde una perspectiva ética [Tesis de Maestría, Universidad Nacional de Costa Rica]. Repositorio Institucional UNA. https://hdl.handle.net/11056/29614
Chacón-Rivadeneira, K., Morales-Maure, L., García-Marimón, O., Sáez-Delgado, F., Gutiérrez González, J., y Alfaro Ponce, B. (2024). Artificial intelligence adoption in Latin American mathematics education: challenges and opportunities. Journal of Posthumanism, 4(3), 1141-1161. https://doi.org/10.63332/joph.v4i3.3195 DOI: https://doi.org/10.63332/joph.v4i3.3195
Contel, F., & Cusi, A. (2025). Investigating the role of ChatGPT in supporting metacognitive processes during problem-solving activities. Digital Experiences in Mathematics Education, 11, 167–191. https://doi.org/10.1007/s40751-024-00164-7 DOI: https://doi.org/10.1007/s40751-024-00164-7
Greefrath, G., y Vorhölter, K. (2022). Teaching and learning mathematical modelling: approaches and developments from recent research. ZDM Mathematics Education, 54, 1-15. https://doi.org/10.1007/s11858-021-01330-6 DOI: https://doi.org/10.1007/s11858-022-01339-5
Meza-Cascante, L. G., Ramírez-Bogantes, M., y Meza-Chavarría, L. A. (2026). Actitudes del estudiantado de ingeniería ante la inteligencia artificial generativa: estudio en el Instituto Tecnológico de Costa Rica. Revista Tecnología en Marcha, 39(5), 137-146. https://doi.org/10.18845/tm.v39i5.8502 DOI: https://doi.org/10.18845/tm.v39i5.8502
Nguyen, D. T., y Pham, Q. V. (2025). The evolving landscape of AI integration in mathematics education: a systematic review of trends (2015-2025). Eurasia Journal of Mathematics, Science and Technology Education, 21(10), em2714. https://doi.org/10.29333/ejmste/17078 DOI: https://doi.org/10.29333/ejmste/17078
Opesemowo, O. A. G., y Adewuyi, H. O. (2024). A systematic review of artificial intelligence in mathematics education: the emergence of 4IR. Eurasia Journal of Mathematics, Science and Technology Education, 20(7), em2478. https://doi.org/10.29333/ejmste/14762 DOI: https://doi.org/10.29333/ejmste/14762
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.1136/bmj.n71
Panadero, E., y Lipnevich, A. A. (2022). A review of feedback models and typologies: towards an integrative model of feedback elements. Educational Research Review, 35, 100416. https://doi.org/10.1016/j.edurev.2021.100416 DOI: https://doi.org/10.1016/j.edurev.2021.100416
Panqueban, J., y Huincahue, J. (2024). Artificial intelligence in mathematics education: a systematic review. Uniciencia, 38(1). https://doi.org/10.15359/ru.38-1.20 DOI: https://doi.org/10.15359/ru.38-1.20
Pepin, B., Buchholtz, N., y Salinas-Hernández, U. (2025). A scoping survey of ChatGPT in mathematics education. Digital Experiences in Mathematics Education, 11(1), 9-41. https://doi.org/10.1007/s40751-025-00172-1 DOI: https://doi.org/10.1007/s40751-025-00172-1
Pochulu, M. D., y Font, V. (2025). Idoneidad didáctica de tareas de matemáticas reformuladas con inteligencia artificial. Paradigma, 46(1), e2025027. https://doi.org/10.37618/PARADIGMA.1011-2251.2025.e2025027.id1621 DOI: https://doi.org/10.37618/PARADIGMA.1011-2251.2025.e2025027.id1621
Ramírez-Bogantes, M., Monge-Fallas, J., Borbón-Alpízar, A., Gutiérrez-Montenegro, M. V., Acuña-Chacón, R., y Meza-Cascante, L. G. (2026). Innovación didáctica con IA generativa en matemática universitaria: experiencias en el TEC. Revista Tecnología en Marcha, 39(5), 147-156. https://doi.org/10.18845/tm.v39i5.8503 DOI: https://doi.org/10.18845/tm.v39i5.8503
Sapkota, B., & Bondurant, L. (2024). Assessing concepts, procedures, and cognitive demand of ChatGPT-generated mathematical tasks. International Journal of Technology in Education, 7(2), 218–238. https://doi.org/10.46328/ijte.677 DOI: https://doi.org/10.46328/ijte.677
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., . . . Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of Internal Medicine, 169(7), 467-473. https://doi.org/10.7326/M18-0850 DOI: https://doi.org/10.7326/M18-0850
Urhan, S., Gençaslan, O., y Dost, Ş. (2024). An argumentation experience regarding concepts of calculus with ChatGPT. Interactive Learning Environments, 1-26. https://doi.org/10.1080/10494820.2024.2308093 DOI: https://doi.org/10.1080/10494820.2024.2308093
Yi, L., Liu, D., Jiang, T., & Xian, Y. (2025). The effectiveness of AI on K-12 students’ mathematics learning: A systematic review and meta-analysis. International Journal of Science and Mathematics Education, 23(4), 1105–1126. https://doi.org/10.1007/s10763-024-10499-7 DOI: https://doi.org/10.1007/s10763-024-10499-7
Yoon, H., Hwang, J., Lee, K., Roh, K. H., y Kwon, O. N. (2024). Students’ use of generative artificial intelligence for proving mathematical statements. ZDM Mathematics Education, 56(7), 1531-1551. https://doi.org/10.1007/s11858-024-01629-0 DOI: https://doi.org/10.1007/s11858-024-01629-0
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