Artificial Intelligence in the Study of Fractal Patterns in Ethnomathematics
DOI:
https://doi.org/10.63561/fnas-jmse.v7i2.1129Keywords:
Ethnomathematics, Artificial Intelligence, Patterns, Fractals, PerformanceAbstract
This study investigated artificial intelligence in the study of fractal patterns in ethnomathematics. Two research questions were answered and two null hypotheses tested at 0.05 significant level. The research design employed was quasi experimental which involved intact classes, pretest and posttest control group. The design presented one experimental group and one control group. The population of the study was made up of all the level 300 undergraduate students of Mathematics education in the three universities in Rivers State. The purposive sampling technique was used to select thirty-six level 300 undergraduate mathematics education students from two universities. The instrument used to collect data was fifty multiple-choice test items named “Ethnomathematics Fractal Achievement Test” (EFAT). The instrument was content validated and Kuder-Richardson formula 21 was used to obtain an internal stability of 0.81. Students in the experimental group were taught ethnomathematical fractal pattern with Mandelbulb 3D AI software while those in the control group were taught with direct instruction. The topics taught were tree fractals, bead fractals, wave fractals, sea shell fractal, arts and design fractals. The mean and standard deviation were used to answer the research questions while analysis of covariance was used to test the null hypotheses. The result showed that students that were taught ethnomathematical fractal pattern with Mandelbulb 3D AI software had a higher performance than those taught with direct instruction with a statistically significant difference. The result also showed that the male students that were taught with Mandelbulb 3D AI software had a higher performance than their female counterpart in the same group with no statistically significance difference. Based on the findings it was concluded that the integration of artificial intelligence in the teaching of ethnomathematical fractal patterns enhanced students’ performance in ethnomathematics.
References
Adang A.M.H., Sowanto, M., F., & Dety, F. (2024). Innovation of ethnomathematics-based digital teaching materials to enhance junior high school students’ mathematical problem-solving skills. Journal of Xi’an Shiyou University, Natural Science Edition, 20(8), 113-120.
Adeten, L., & Faroyo, R.D. (2021). Culturally responsive education. Pearl Press.
Adhitya, Y., & Supriyadi, E. (2024). Ethnomathemtics in Javanese and Sundanese batik: AN AI-assisted comparative analysis. Doi: 10.30738/union.v12i3.18024
Atta, S. A., Bonyah, E., Boateng, F. O., & Kusi, P. (2025). The Akan ethnomathematics as pedagogical tool under the context of technology integration in promoting conceptual understanding in mensuration. African Educational Research Journal, 13(3), 342-351.
Auuke, B. E., & Uyanahan, U.T. (2023). Delivering ethnomathematical content to high school students via digital technologies. Journal of Culture and Educational Development, 1(2), 41-50.
Hammond, Z. (2015). 3 tips to make any lesson more culturally responsive. https://www.cultofpedagogy.com
Johnson, J., & Rajuroy, A. (2025). Interdisciplinary ai applications in ethnomathematics, cultural heritage preservation, and smart infrastructure development through computational geometry and machine learning. https://www.researchgate.net
Ladson-Billings, G. (1994). Culturally responsive teaching. https://www.national equityproject.org
Machaba, F., & Dhlamini, J. (2021). Ethnnomathematics as a fundamental teaching approach. DOI:10.1007/978-3-030-82723-6_5
Madu, A., Rimo, I. H. E., Nenotaek, B., & Agil, A. F. (2025). Development of ethnomathematics-based e-modules to improve student learning outcomes on cirvle materials. https://doi.org/10.53894/ijirss.v8i4.8069
Nwoke, B.I., Uzoma, P. O., Okorie, S.. C., & Mjemanze, C.A. (2025). Artificial intelligence in mathematics education in teacher training institutions: Prospects and challenges. Global Journal of Artificial Intelligence and Technology Development, 3(3), 47-56.
Stewart, I. (2026). The nature of mathematics. https://www.scribd.com
