Using Generative AI to Reframe Mathematical Tasks for Personalized Learning

Authors

Keywords:

Generative Artificial Intelligence,, Personalized Mathematics Tasks, Teacher Practice

Abstract

The authors explore how generative AI can reframe mathematical tasks for personalized learning. Building on prior work showing that interest-based tasks (e.g., sports, movies, video games) boost student engagement, this study examines teachers’ use of the MagicSchool tool for K–9 students. It reports on teachers’ positive and negative experiences, discusses AI’s affordances and limitations for personalization, and evaluates the readability of AI-generated problems.

Author Biographies

Theodora Beauchamp, Southern Methodist University

Theodora Beauchamp is a Ph.D. student and graduate research assistant at Southern Methodist University. She explores the use of generative AI to personalize K-12 mathematics learning. Her research examines how AI can enhance curriculum design, personalize learning and how math teachers can integrate AI-driven tools into their teaching.

Candace Walkington, Southern Methodist University

Candace Walkington is a Professor and the Annette and Harold Simmons Centennial Chair in the Department of Teaching and Learning at Southern Methodist University. Dr. Walkington was a recipient of a Spencer Postdoctoral Research Fellowship Grant and also received the Presidential Early Career Award for Scientists and Engineers from the U.S. Department of Education. Her research focuses on personalized learning and technology-enhanced interventions for learning mathematics. She examines technologies like intelligent tutoring systems, augmented and virtual reality, and generative AI. Dr. Walkington teaches courses for pre-service and in-service mathematics teachers at SMU.

References

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Beauchamp, T., & Walkington, C. (2024). Mathematics teachers using generative AI to personalize instruction of students' interests. AMTE Connections. [https://amte.net/connections/2024/05/connections-thematic-articles-artificial-intelligence-mathematics-teacher](https://amte.net/connections/2024/05/connections-thematic-articles-artificial-intelligence-mathematics-teacher)

Bernacki, M. L., & Walkington, C. (2018). The role of situational interest in personalized learning. Journal of Educational Psychology, 110(6), 864. [http://doi.org/10.1037/edu0000250](http://doi.org/10.1037/edu0000250)

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Biton, Y., & Segal, R. (2025). Learning to craft and critically evaluate prompts: the role of generative AI (ChatGPT) in enhanced preservice mathematics teachers' TPAK and problem-posing skill. International Journal of Education in Mathematics, Science, and Technology (IJEMST), 13(1), 202-223. [https://doi.org/10.46328/iemst.4654](https://doi.org/10.46328/iemst.4654)

Broutın, M. S. T. (2024). Exploring mathematics teacher candidates' instrumentation process of generative artificial intelligence for developing lesson plans. Yükseköğretim Dergisi, 14(1), 165-176. [https://doi.org/10.53478/yuksekogretim.1347061](https://doi.org/10.53478/yuksekogretim.1347061)

Kaplan, O. (2024). Thematic modeling preservice mathematics teachers' reflections on ChatGPT use: appraisals, ethical challenges and aspirations. Adiyaman University Journal of Educational Sciences, 14(1), 21-35. [https://doi.org/10.17984/adyuebd.1463794](https://doi.org/10.17984/adyuebd.1463794)

Lin, L., Lin, X., Zhang, X., & Ginns, P. (2024). The Personalized Learning by Interest Effect on Interest, Cognitive Load, Retention, and Transfer: A Meta-Analysis. Educational Psychology Review, 36(3), 88.

MagicSchool.ai (n.d.). Magic School: AI built for schools. [http://magicschool.ai](http://magicschool.ai)

Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932.

Wardat, Y., Tashtoush, M., AlAli, R., & Saleh, S. (2024). Artificial intelligence in education: mathematics teachers' perspectives, practices and challenges. Iraqi Journal for Computer Science and Mathematics, 5(1), 60-77. [https://doi.org/10.52866/ijcsm.2024.05.01.004](https://doi.org/10.52866/ijcsm.2024.05.01.004)

WebFX. (n.d.) Readability test tool. WebFX. Last accessed: April 2, 2025. [https://www.webfx.com/tools/read-able](https://www.webfx.com/tools/read-able)

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Published

2025-05-31

How to Cite

Beauchamp, T., Walkington, C., & Bainbridge, K. (2025). Using Generative AI to Reframe Mathematical Tasks for Personalized Learning . hio ournal of chool athematics, 99(1), 6–18. etrieved from http://voyager.janeway.systems:9999/index.php/OJSM/article/view/10498

Issue

Vol. 99: Spring 2025

Section

Articles

License

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