Artificial Intelligence in Mathematics Education


Özet Görüntüleme: 14 / PDF İndirme: 7

Yazarlar

  • Bekir DANIŞ Dr

DOI:

https://doi.org/10.5281/zenodo.18002663

Anahtar Kelimeler:

Mathematics, education, artificial intelligence

Özet

This article comprehensively examines the current development, theoretical foundations, pedagogical implications, and applications of artificial intelligence (AI) methods in mathematics education. In recent years, advances in deep learning, natural language processing, generative models, and student modeling techniques have led to transformative innovations in mathematics education, such as adaptive learning, automated problem solving, personalized feedback, and pedagogical agents. This study discusses the epistemological impacts of AI on mathematical thinking processes and assesses critical limitations such as algorithmic biases, data privacy, verifiability, pedagogical fit, and cognitive dependency. The findings suggest that AI offers significant opportunities in mathematics education but requires careful pedagogical, ethical, and methodological considerations.

Referanslar

Drori, I., Zhang, S., Shuttleworth, R., Tang, L., Lu, A., Ke, E., ... & Udell, M. (2022). A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level. Proceedings of the National Academy of Sciences, 119(32), e2123433119. https://doi.org/10.1073/pnas.2123433119

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Psromises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/our-work/artificial-intelligence-in-education/

Kasneci, E., Sessler, K., Eling, N., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Koedinger, K. R., & Aleven, V. (2016). Different futures of adaptive collaborative learning support. International Journal of Artificial Intelligence in Education, 26(1), 1–14. https://doi.org/10.1007/s40593-015-0083-0

Liu, R., & Koedinger, K. R. (2017). Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning gains. Journal of Educational Data Mining, 9(1), 25–41. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/JEDM912

Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 28, 505–513. https://papers.nips.cc/paper/2015/hash/5654-deep-knowledge-tracing-Abstract.html

VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227–265. https://dl.acm.org/doi/10.5555/1435386.1435388

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995

İndir

Yayınlanmış

2025-12-20

Nasıl Atıf Yapılır

DANIŞ, B. (2025). Artificial Intelligence in Mathematics Education. ASES EDUSCI (INTERNATIONAL JOURNAL OF EDUCATIONAL SCIENCES) ISSN: 2822-6844, 5(2), 671–676. https://doi.org/10.5281/zenodo.18002663

Sayı

Cilt 5 Sayı 2 (2025): ASES INTERNATIONAL JOURNAL OF EDUCATIONAL SCIENCES (ISSN: 2822-6844)

Bölüm

Makaleler

Lisans

Telif Hakkı (c) 2025 ASES EDUSCI (INTERNATIONAL JOURNAL OF EDUCATIONAL SCIENCES) ISSN: 2822-6844

Creative Commons License

Bu çalışma Creative Commons Attribution-NonCommercial 4.0 International License ile lisanslanmıştır.