Research Article

Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis

Volume: 10 Number: 2 July 2, 2025
EN

Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis

Abstract

The rapid advancement of artificial intelligence (AI) and its accessibility to almost everyone necessitate a clear definition of its role in education. The primary step in effectively integrating AI into mathematics education (ME) is formulating instructional strategies that consider its advantages and disadvantages. This study aims to develop strategic recommendations for integrating AI into ME by utilizing SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis and the Best-Worst Method (BWM). A SWOT analysis of studies on the use of AI in mathematics education was conducted, and a group of 19 mathematics education experts evaluated these criteria through a paired comparison method. The data was analyzed through BWM to determine the impact level of the criteria, and a SWOT matrix was created to develop key strategies to optimize the role of AI in ME. Strategic recommendations include leveraging AI for personalized learning, integrating AI-driven teaching models, and ensuring that AI complements rather than replaces teacher-student interactions. The findings emphasize the necessity of AI literacy for both educators and students in mitigating its drawbacks. By providing a structured framework for assessing AI’s impact and proposing actionable strategies for its effective implementation in ME, this study contributes to the ongoing discourse on AI in education.

Keywords

artificial intelligence, SWOT analysis, BWM, mathematics education

References

  1. Alonso, F., Lopez, G., Manrique, D., & Vines, J. M. (2005). An instructional model for web-based E-learning education with a blended learning process approach. British Journal of Educational Technology, 36, Article 217e235. https://doi.org/10.1111/j.1467-8535.2005.00454.x
  2. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of The Learning Sciences, 4(2), 167-207. https://doi.org/10.1207/s15327809jls0402_2
  3. Antonietti, A., & Cantoia, M. (2000). To see a painting versus to walk in a painting: an experiment on sense-making through virtual reality. Comput. Educ., 34(3e4), 213e223. http://doi:10.1016/s0360-1315(99)00046-9
  4. Arici, F. (2024). Investigating the Effectiveness of Augmented Reality Technology in Science Education in Terms of Environmental Literacy, Self-Regulation, and Motivation to Learn Science. International Journal of Human–Computer Interaction, 40(24), 8476-8496. https://doi.org/10.1080/10447318.2024.2310921
  5. Arnon, I., Cottrill, J. Dubinsky, E., Oktac, A., Roa, S., Trigueros, M., & Weller, K. (2014), APOS Theory: A Framework for Research and Curriculum Development in Mathematics Education, Springer, NY, Heidelberg, Dondrecht, London. https://doi.org/10.1007/978-1-4614-7966-6
  6. Ayçin, E. (2023). Çok Kriterli Karar Verme: Bilgisayar uygulamalı çözümler (Multi-Criteria Decision Making: Computer-implemented solutions). Nobel Yayıncılık.
  7. Azeroual, O., Ershadi, M. J., Azizi, A., Banihashemi, M., & Abadi, R. E. (2021). Data quality strategy selection in CRIS: using a hybrid method of SWOT and BWM. Informatica, 45(1), 65-80. https://doi.org/10.31449/inf.v45i1.2995
  8. Benzaghta, M. A., Elwalda, A., Mousa, M. M., Erkan, I., & Rahman, M. (2021). SWOT analysis applications: An integrative literature review. Journal of Global Business Insights, 6(1), 54-72. https://www.doi.org/10.5038/2640-6489.6.1.1148
  9. bin Mohamed, M. Z., Hidayat, R., binti Suhaizi, N. N., bin Mahmud, M. K. H., & binti Baharuddin, S. N. (2022). Artificial intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education, 17(3), Article em0694. https://doi.org/10.29333/iejme/12132
  10. Bin-Hady, W.R.A., Al-Kadi, A., Hazaea, A. and Ali, J.K.M. (2023). Exploring the dimensions of ChatGPT in English language learning: A global perspective. Library Hi Tech. https://doi.org/10.1108/LHT-05-2023-0200
APA
Şahin, S., & Teke, B. (2025). Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis. Journal of Learning and Teaching in Digital Age, 10(2), 273-286. https://doi.org/10.53850/joltida.1667650
AMA
1.Şahin S, Teke B. Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis. JOLTIDA. 2025;10(2):273-286. doi:10.53850/joltida.1667650
Chicago
Şahin, Seda, and Bedirhan Teke. 2025. “Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis”. Journal of Learning and Teaching in Digital Age 10 (2): 273-86. https://doi.org/10.53850/joltida.1667650.
EndNote
Şahin S, Teke B (July 1, 2025) Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis. Journal of Learning and Teaching in Digital Age 10 2 273–286.
IEEE
[1]S. Şahin and B. Teke, “Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis”, JOLTIDA, vol. 10, no. 2, pp. 273–286, July 2025, doi: 10.53850/joltida.1667650.
ISNAD
Şahin, Seda - Teke, Bedirhan. “Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis”. Journal of Learning and Teaching in Digital Age 10/2 (July 1, 2025): 273-286. https://doi.org/10.53850/joltida.1667650.
JAMA
1.Şahin S, Teke B. Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis. JOLTIDA. 2025;10:273–286.
MLA
Şahin, Seda, and Bedirhan Teke. “Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis”. Journal of Learning and Teaching in Digital Age, vol. 10, no. 2, July 2025, pp. 273-86, doi:10.53850/joltida.1667650.
Vancouver
1.Seda Şahin, Bedirhan Teke. Artificial Intelligence Integration in Mathematics Education: A SWOT-BWM Analysis. JOLTIDA. 2025 Jul. 1;10(2):273-86. doi:10.53850/joltida.1667650