Bridging pedagogy and AI: A systematic review of deep learning in mathematics education
DOI:
https://doi.org/10.33474/jpm.v11i2.24319Keywords:
Deep learning, mathematics education, Technology Developer, Artificial IntelligenceAbstract
Deep learning is interpreted differently in educational and computational contexts. The purpose of this study is to systematically examine how deep learning is conceptualized and applied in mathematics education research, and to evaluate the extent to which these applications align with the Indonesian Ministry of Education and Culture’s (Kemendikbud) definition of deep learning. A systematic literature review (SLR) was conducted using PRISMA guidelines. Searches in ScienceDirect, Scopus, Springer Link, and ProQuest produced 1,881 records containing the term “deep learning” published between 2015 and 2025. After duplicate removal and screening, 16 peer-reviewed journal articles explicitly addressing deep learning in mathematics education were included. Two main interpretations were found. Eleven studies framed deep learning pedagogically, focusing on conceptual understanding, problem-solving, collaborative learning, and real-world application. Five studies adopted a computational framing, using deep neural networks and other machine learning techniques for prediction, error analysis, adaptive instruction, and automated feedback. While the majority aligned with Kemendikbud’s pedagogical emphasis, some studies treated deep learning purely as a technical method, without explicit links to student-centered outcomes. The review highlights a conceptual gap between pedagogical and computational uses of deep learning in mathematics education. Bridging this gap requires interdisciplinary collaboration between educators and technology developers to ensure technological applications support meaningful learning. The findings provide a reference for aligning global research on deep learning with national education policy, ensuring relevance for curriculum design and classroom practice.
References
Alnasyan, B., Basheri, M., Alassafi, M., & Alnasyan, K. (2025). Kanformer: An attention-enhanced deep learning model for predicting student performance in virtual learning environments. Social Network Analysis and Mining, 15, 25. https://doi.org/10.1007/s13278-025-01446-7
Badan Standar, Kurikulum, dan Asesmen Pendidikan [Indonesian Education Standar, Curriculum, and Assessment Agency]. (2024). Kajian akademik Kurikulum Merdeka [Academic study of the Independent Curriculum].
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y
Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2019). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791
Demir, I., & Karaboğa, H. A. (2021). Modeling mathematics achievement with deep learning methods. Sigma Journal of Engineering and Natural Sciences, 39, 33–40. https://doi.org/10.14744/sigma.2021.00039
Organisation for Economic Co-operation and Development. (2021). 21st-century readers: Developing literacy skills in a digital world. OECD Publishing. https://doi.org/10.1787/a83d84cb-en
Fauskanger, J., & Bjuland, R. (2018). Deep learning as constructed in mathematics teachers’ written discourses. International Electronic Journal of Mathematics Education, 13(3), 149–160. https://doi.org/10.12973/iejme/2705
Fullan, M., Quinn, J., & McEachen, J. (2017). Deep learning: Engage the world, change the world. Corwin.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Goos, M., Carreira, S., & Namukasa, I. K. (2023). Mathematics and interdisciplinary STEM education: Recent developments and future directions. ZDM–Mathematics Education, 55, 1199–1217. https://doi.org/10.1007/s11858-023-01533-z
Jin, H., & Suh, B. (2024). Research on a statistics education program utilizing deep learning predictions in high school mathematics. The Mathematical Education, 63(2), 209–231. https://doi.org/10.7468/mathedu.2024.63.2.209
Liu, R. (2022). Optimization model of mathematics instructional mode based on deep learning algorithm. Computational Intelligence and Neuroscience, 2022, 1817990. https://doi.org/10.1155/2022/1817990
Mohamed, M. Z. B., Hidayat, R., Suhaizi, N. N. B., Sabri, N. B. M., Mahmud, M. K. H. B., & Baharuddin, S. N. B. (2022). Artificial intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education, 17(3), em0694. https://doi.org/10.29333/iejme/12132
Nurdiana, A., Zulianti, H., Ciciria, D., Fitria, N., & Kirana, A. R. (2024). Practical applications of deep learning in mathematics to enhance student engagement and conceptual mastery. JTAM (Jurnal Teori dan Aplikasi Matematika, 8(4), 1361–1373. https://doi.org/10.31764/jtam.v8i4.33102
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., McGuinness, L. G., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pujawati, F., Azkia, M. N., & Susilawati, W. (2025). Exploration of the implementation of deep learning approach in teaching mathematics in secondary schools. Unnes Journal of Mathematics Education, 14(2), 98–105. https://doi.org/10.15294/ujme.v14i2.27374
Putri, D. K., Zahro, N., Widodo, W., & Suprapto, N. (2025). Primary teachers’ perceptions of deep learning pedagogy in culture-integrated STEM education: A quantitative survey. Journal on Mathematics Education, 16(4), 1331–1346. https://doi.org/10.22342/jme.v16i4.pp1331-1346
Qiu, Y., & Ishak, N. A. (2025). AI-assisting technology and social support in enhancing deep learning and self-efficacy among primary school students in mathematics in China. International Journal of Learning, Teaching and Educational Research, 24(2), 21–37. https://doi.org/10.26803/ijlter.24.2.2
Slamet, D., Hendriana, B., & Supiat. (2025). Mengungkap perspektif siswa: Peran deep learning dalam visualisasi konsep dan pemecahan masalah matematika. International Journal of Progressive Mathematics Education, 5(1), 225–237. https://doi.org/10.22236/ijopme.v5i1.19310
Strock, A., Mistry, P. K., & Menon, V. (2025). Personalized deep neural networks reveal mechanisms of math learning disabilities in children. Science Advances, 11(23), eadq9990. https://doi.org/10.1126/sciadv.adq9990
Tian, X., Zhao, J., & Nguyen, K. T. (2022). Practical research on primary mathematics teaching based on deep learning. Science Programming, 2022, 7899180. https://doi.org/10.1155/2022/7899180
Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2020). Transfer learning from deep neural networks for predicting student performance. Applied Sciences, 10(6), 2145. https://doi.org/10.3390/app10062145
Villegas-Ch, W., Buenano-Fernandez, D., Navarro, A. M., & Mera-Navarrete, A. (2025). Adaptive intelligent tutoring systems for STEM education: Analysis of the learning impact and effectiveness of personalized feedback. Smart Learning Environments, 12, Article 1. https://doi.org/10.1186/s40561-025-00389-y
Zhang, Y., Liu, S., & Shang, X. (2021). An MRI study on effects of math education on brain development using multi-instance contrastive learning. Frontiers in Psychology, 12, 765754. https://doi.org/10.3389/fpsyg.2021.765754
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