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

Year 2025, Volume: 10 Issue: 2, 273 - 286, 02.07.2025

Seda Şahin , Bedirhan Teke

https://doi.org/10.53850/joltida.1667650

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

• 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
• 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
• 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
• 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
• 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
• Ayçin, E. (2023). Çok Kriterli Karar Verme: Bilgisayar uygulamalı çözümler (Multi-Criteria Decision Making: Computer-implemented solutions). Nobel Yayıncılık.
• 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
• 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
• 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
• 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
• Blum, W., & Borromeo Ferri, R. (2009). Mathematical modelling: Can it be taught and learnt. Journal of mathematical modelling and application, 1(1), 45-58.
• Bogost, I. (2022). ChatGPT is dumber than you think. https://www.theatlantic.com/technology/archive/2022/12/chatgpt-openai-artificial-intelligence-writing-ethics/672386/
• Borji, A. (2023). A categorical archive of ChatGPT failures. arXiv. https://doi.org/10.48550/arXiv.2302.03494
• Chang, D. H., Lin, M. P. C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921. https://doi.org/10.3390/su151712921
• Costa, N., Junior, C. P., Araujo, R., & Fernandez, M. (2019). Application of AI planning in the context of e-learning. In Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, 57-59. https://doi.org/10.1109/ICALT.2019.00021
• Cunska, A., & Savicka, I. (2012). Use of ICT teaching-learning methods make school math blossom. Procedia-Social and Behavioral Sciences, 69, 1481-1488. https://doi.org/10.1016/j.sbspro.2012.12.089
• Dąbrowicz-Tlałka, A. (2023). Edukacja matematyczna na poziomie akademickim na kierunkach ścisłych i technicznych w dobie technologii mobilnych i sztucznej inteligencji. e-mentor, 102(5), 57-64.
• del Olmo-Muñoz, J., González-Calero, J. A., Diago, P. D., Arnau, D., & Arevalillo-Herráez, M. (2023). Intelligent tutoring systems for word problem solving in COVID-19 days: could they have been (part of) the solution?. ZDM–Mathematics Education, 55(1), 35-48. https://doi.org/10.1007/s11858-022-01396-w
• Denecke, K., Glauser, R., & Reichenpfader, D. (2023). Assessing the potential and risks of ai-based tools in higher education: Results from an eSurvey and SWOT analysis. Trends in Higher Education, 2(4), 667-688. https://doi.org/10.3390/higheredu2040039
• Dikilitaş, K., Klippen, M.I.F., Keles, S. A. (2024). Systematic Rapid Review of Empirical Research on Students’ Use of ChatGPT in Higher Education. Nord. J. Syst. Rev. Educ., 2, 103-125. https://doi.org/10.23865/njsre.v2.6227
• Druzhinina, O. V., Karpacheva, I. A., Masina, O. N., & Petrov, А. А. (2021). Development of an integrated complex of knowledge base and tools of expert systems for assessing knowledge of students in mathematics within the framework of a hybrid intelligent learning environment. International Journal of Education and Information Technologies, 15, 122.
• Egara, F. O., & Mosimege, M. (2024). Exploring the integration of artificial intelligence-based ChatGPT into mathematics instruction: Perceptions, challenges, and implications for educators. Education Sciences, 14(7), 742. Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM–Mathematics Education, 56(2), 281-292. https://doi.org/10.1007/s11858-023-01530-2
• Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460-474. https://doi.org/10.1080/14703297.2023.2195846
• Forsström, S.E., Afdal, G. (2020). Learning mathematics through activities with robots. Digital Experiences in Mathematics Education, 6(1), 30-50. https://doi.org/10.1007/s40751-019-00057-0
• Frank, M.R., Wang, D., Cebrian, M., Rahwan, I., 2019. The evolution of citation graphs in artificial intelligence research. Nature Mach. Intell. 1(2), 79-85. https://doi.org/10.1038/s42256-019-0024-5
• Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., & Gerjets, P. (2023). ChatGPT in education: global reactions to AI innovations. Scientific Reports, 13(1), Article 15310. https://doi.org/10.1038/s41598-023-42227-6
• Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology, 34(2), 133-139. https://doi.org/10.1108/IJILT-09-2016-0048
• Galbraith, P., & Haines, C. (2001). The keyskills agenda: Exploring implications for mathematics. International Journal of Mathematical Education in Science and Technology,32(3), 337–354.
• Giray, L., Jacob, J., & Gumalin, D.L. (2024). Strengths, weaknesses, opportunities, and threats of using ChatGPT in scientific research. International Journal of Technology in Education, 7(1), 40-58. https://doi.org/10.46328/ijte.618
• Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134-147. https://doi.org/10.1016/j.ijis.2020.09.001
• Hill, T., & Westbrook, R. (1997). SWOT analysis: It's time for a product recall. Long range planning, 30(1), 46-52. https://doi.org/10.1016/S0024-6301(96)00095-7
• Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584-603. https://doi.org/10.3390/math9060584
• Hwang, G. J., Sung, H. Y., Chang, S. C., & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1, Article 100003.
• Jablonski, S. (2022). Mathematical reasoning outside the classroom – A case study with primary school students solving math trail tasks. In J. Hodgen, E. Geraniou, G. Bolondi, & F. Ferretti (Eds.), Proceedings of the Twelfth Congress of the European Society for Research in Mathematics Education (pp. 201-208). Free University of Bozen-Bolzano and ERME.
• Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1), 37. https://doi.org/10.1186/s41239-023-00406-5
• Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023). Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence, 4, 100138. https://doi.org/10.1016/j.caeai.2023.100138
• Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
• Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., et al. (2022). Toward learning progression analytics-Developing learning environments for the automated analysis of learning using evidence centered design. Frontiers in Education. https://doi.org/10.3389/feduc.2022.981910
• Li, K.C. & Wong, B.T.-M. (2023). Artificial intelligence in personalised learning: a bibliometric analysis. Interactive Technology and Smart Education, 20(3), 422-445. https://doi.org/10.1108/ITSE-01-2023-0007
• Li, K.C. & Wong, B.T.M. (2021). Features and trends of personalised learning: a review of journal publications from 2001 to 2018. Interactive Learning Environments, 29(2), 182-195.
• Li, M. (2024). Integrating artificial intelligence in primary mathematics education: Investigating internal and external influences on teacher adoption. International Journal of Science and Mathematics Education, 1-26. https://doi.org/10.1007/s10763-024-10515-w
• Lin, H. C., & Hwang, G. J. (2019). Research trends of flipped classroom studies for medical courses: a review of journal publications from 2008 to 2017 based on the technology-enhanced learning model. Interactive Learning Environments, 27(8), 1011-1027. https://doi.org/10.1080/10494820.2018.1467462
• Lo, C. K., & Hew, K. F. (2020). A comparison of flipped learning with gamification, traditional learning, and online independent study: the effects on students’ mathematics achievement and cognitive engagement. Interactive Learning Environments, 28(4), 464–481. https://doi.org/10.1080/10494820.2018.1541910
• Marín, V.I.; Castañeda, L. (2023). Developing Digital Literacy for Teaching and Learning. In Handbook of Open, Distance and Digital Education, pp. 1089–1108. Springer. https://doi.org/10.1007/978-981-19-2080-6_64.
• Markos, A., Prentzas, J., & Sidiropoulou, M. (2024). Pre-Service Teachers’ Assessment of ChatGPT’s Utility in Higher Education: SWOT and Content Analysis. Electronics, 13(10), 1985. https://doi.org/10.3390/electronics13101985
• McCarthy, J. (2007). What is artificial intelligence. Monroy Andrade, J. (2024). El uso de las nuevas tecnologías en la enseñanza de las matemáticas: una revisión sistemática. Tecnología, Ciencia y Educación, 28, 115-140. https://doi.org/10.51302/tce.2024.18987
• Muzahidul, M., Akter, L., Pervez, A. K., Nabi, M. N., Uddin, M. M., & Arifin, Z. (2020). Application of combined SWOT and AHP for strategy development: Evidence from pottery industry of Bangladesh. Asian Journal of Agriculture and Rural Development, 10(1), 81-94. https://doi.org/10.22004/ag.econ.342238
• Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
• Ngo, T. T. A. (2023). The perception by university students of the use of ChatGPT in education. International Journal of Emerging Technologies in Learning (Online), 18(17), 4-19. https://doi.org/10.3991/ijet.v18i17.39019
• Nikolova, E. (2024). Artificial intelligence tools into higher mathematics education: Opportunities, challenges, and student perceptions. Mathematics & Informatics, 67(4).
• Pamučar, D., Ecer, F., Cirovic, G., & Arlasheedi, M. A. (2020). Application of improved best worst method (BWM) in real-world problems. Mathematics, 8(8), 1342.
• Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22), 1-13. https://doi.org/10.1186/s41039-017-0062-8
• Rezaei, J. (2015). Best -worst multi -criteria decision - making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
• Sahin, S. (2024). Utilizing AHP and conjoint analysis in educational research: Characteristics of a good mathematical problem. Educ Inf Technol 29, 25375–25401. https://doi.org/10.1007/s10639-024-12830-9
• Sarker, I. H. (2022). AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN COMPUT. SCI. 3, 158. https://doi.org/10.1007/s42979-022-01043-x
• Schindler, M., & Lilienthal, A. J. (2022). Students’ collaborative creative process and its phases in mathematics: an explorative study using dual eye tracking and stimulated recall interviews. ZDM–Mathematics Education, 54(1), 163-178. https://doi.org/10.1007/s11858-022-01327-9
• Sleeman, D. H. (1983). Intelligent tutoring systems: a review. Proceedings of EdCompCon '83 meeting. IEEE Computer Society, pp. 95-101.
• Song, Y. (2020). How to flip the classroom in school students’ mathematics learning: bridging in-and out-of-class activities via innovative strategies. Technology, Pedagogy and Education, 29(3), 327-345. https://doi.org/10.1080/1475939X.2020.1749721
• Spreitzer, C. & Straser, O. (2024). Zehetmeier, S.; Maaß, K. Mathematical Modelling Abilities of Artificial Intelligence Tools: The Case of ChatGPT. Educ. Sci., 14, 698. https://doi.org/10.3390/educsci14070698
• Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, Article 100065. https://doi.org/10.1016/j.caeai.2022.100065
• Tajer, E., & Demir, S. (2022). Ecotourism strategy of UNESCO city in Iran: Applying a new quantitative method integrated with BWM. Journal of Cleaner Production, 376, 134284.
• Terenzini, P. T., Springer, L., Pascarella, E. T. et al. (1995). Influences affecting the development of students' critical thinking skills. Res High Educ, 36, 23-39. https://doi.org/10.1007/BF02207765
• Torres-Peña, R. C., Peña-González, D., Chacuto-López, E., Ariza, E. A., & Vergara, D. (2024). Updating calculus teaching with AI: A classroom experience. Education Sciences, 14(9), 1019. https://doi.org/10.3390/educsci14091019
• Tran, T. A., Nguyen, L. T., & Le, Q. P. (2024, July). Humans and AI: SWOT Analysis of Teaching Methods and Knowledge Acquisition of Students. In 2024 9th International STEM Education Conference (iSTEM-Ed) (pp. 1-5). IEEE. https://doi.org/10.1109/iSTEM-Ed62750.2024.10663159
• Virvou, M., & Sidiropoulos, S. C. (2013, July). An Intelligent Tutoring System over a social network for mathematics learning. In IISA 2013 (pp. 1-4). IEEE.
• Voskoglou, M. G., & Salem, A. B. M. (2020). Benefits and limitations of the artificial with respect to the traditional learning of mathematics. Mathematics, 8(4), Article 611. https://doi.org/10.3390/math8040611
• Walkington, C. & Bernacki, M. L. (2018). Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact. Int. J. Artif. Intell. Educ., 29, 58-88. https://doi.org/10.1007/s40593-018-0168-1
• Wu, Y. (2020, February 17). The marketing strategies of IKEA in China using tools of PESTEL, Five Forces Model and SWOT Analysis [Paper Presentation]. International Academic Conference on Frontiers in Social Sciences and Management Innovation, Beijing, China.
• Yanev, N. I., Getova, I. D., Hristova, T. V., Kostadinova, I., Dimitrov, G. P., & Mihaylova, E. (2024, October). SWOT Analysis of the Possibility of Using AI for Education. In 2024 International Conference Automatics and Informatics (ICAI) (pp. 539-545). IEEE. https://doi.org/10.1109/ICAI63388.2024.10851649
• Yoon, H., Hwang, J., Lee, K., Roh, K. H., & Kwon, O. N. (2024). Students’ use of generative artificial intelligence for proving mathematical statements. ZDM–Mathematics Education, 1-21. https://doi.org/10.1007/s11858-024-01629-0
• Zhai, X., Yin, Y., Pellegrino, J. W., Haudek, K. C., & Shi, L. (2020). Applying machine learning in science assessment: a systematic review. Studies in Science Education, 56(1), 111–151. https://doi.org/10.1080/03057267.2020.1735757
• Zhang, P., & Tur, G. (2024). A systematic review of ChatGPT use in K‐12 education. European Journal of Education, 59(2), e12599. https://doi.org/10.1111/ejed.12599
• Zhu, C., Sun, M., Luo, J., Li, T., & Wang, M. (2023). How to harness the potential of ChatGPT in education? Knowledge Management & ELearning, 15(2), 133–152. https://doi.org/10.34105/j.kmel.2023.15.008