Integration of generative AI in creative mathematics learning: A correlational analysis between the quality of prompts and the logical reasoning of student teachers in the education degree programme
Abstract
The presence of Generative Artificial Intelligence (GenAI) based on Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude has triggered a massive paradigm shift in mathematics education. GenAI no longer functions as an information search engine (Luo et al., 2025); it has evolved into a cognitive partner capable of performing complex mathematical reasoning. The integration of GenAI into creative mathematics learning is particularly important because it enables students to explore multiple solution strategies, develop original mathematical ideas, and refine their reasoning through continuous interaction and feedback. As creativity in mathematics requires both divergent thinking and logical justification, GenAI provides opportunities for students to articulate, evaluate, and improve their mathematical arguments in real time. Various recent studies show that the use of artificial intelligence in STEM education can enhance student engagement and offer personalised learning (Huang et al., 2025; Nikhil, 2025). However, the transition from conventional learning to AI-based interaction presents new pedagogical challenges, particularly regarding how students communicate with machines to structure their mathematical thinking.
Downloads
-
Abstract0
-
PDF0
References
Adnan, A. H. M., Salim, M. S. A. M., Shah, D. S. M., Yusuf, A. H. M., Salim, M. N. F. M., & Tahir, M. H. M. (2025). Cheating Using AI and Copy-Pasting from LLMs: New Realities in Higher Education. International Conference on Business and Technology, 399–410. https://doi.org/10.1007/978-3-032-00250-1_36
Aizikovitsh-Udi, E., & Cheng, D. (2015). Developing critical thinking skills from dispositions to abilities: Mathematics education from early childhood to high school. Creative Education, 6(04), 455. https://doi.org/10.4236/ce.2015.64045
Amo Filva, D., Guàrdia Ortiz, L., Donate Beby, B., Bautista Pérez, G., & Fanni, L. (2026). Integración de la Inteligencia Artificial y la Alfabetización de Datos en la ESO: Análisis de percepciones y condiciones de adopción. 26(83). https://doi.org/10.6018/red.690641
Bieda, K. N. (2010). Enacting proof-related tasks in middle school mathematics: Challenges and opportunities. Journal for Research in Mathematics Education, 41(4), 351–382. https://doi.org/10.5951/jresematheduc.41.4.0351
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chen, F., Chen, J., & Xu, Y. (2025). The More Anxious, the More Dependent? The Impact of Math Anxiety on AI-Assisted Problem-Solving. Psychology in the Schools, 62(8), 2685–2701. Scopus. https://doi.org/10.1002/pits.23500
Constantinou, A. C., Guo, Z., & Kitson, N. K. (2023). The impact of prior knowledge on causal structure learning. Knowledge and Information Systems, 65(8), 3385–3434. https://doi.org/10.1007/s10115-023-01858-x
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Hendriks, F., Kienhues, D., & Bromme, R. (2015). Measuring laypeople’s trust in experts in a digital age: The Muenster Epistemic Trustworthiness Inventory (METI). PloS One, 10(10), e0139309. https://doi.org/10.1371/journal.pone.0139309
Hidayatullah, A., Setiyawan, R., & Syarifuddin. (2026). Primary education students’ beliefs about the nature of mathematics, self-efficacy, and mathematics educators; a descriptive study from Indonesia. Education 3-13, 54(4), 958–973. https://doi.org/10.1080/03004279.2024.2380468
Huang, J., Zhong, Y., & Chen, X. (2025). Adaptive and personalized learning in STEM education using high-performance computing and artificial intelligence. The Journal of Supercomputing, 81(8), 981. https://doi.org/10.1007/s11227-025-07481-7
Jamaluddin Z, W., Supriadi, N., & Suherman, S. (2025). Creative AI in education: The role of technological dependence, motivation, and student participation. Revista de Estudios e Investigación En Psicología y Educación, 12(2), e11997–e11997. https://doi.org/10.17979/reipe.2025.12.2.11997
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., & Hüllermeier, E. (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
Lithner, J. (2017). Principles for designing mathematical tasks that enhance imitative and creative reasoning. Zdm, 49(6), 937–949. https://doi.org/10.1007/s11858-017-0867-3
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Luo, X., Xu, D., Li, Y., & Wan, L. C. (2025). Advancing information search through GenAI: the roles of search type, travel motive and GenAI customization level. International Journal of Contemporary Hospitality Management, 37(5), 1725–1743. https://doi.org/10.1108/IJCHM-06-2024-0941
Mollick, E., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. arXiv Preprint arXiv:2306.10052. https://doi.org/10.2139/ssrn.4475995
Nabhan, S., & Habók, A. (2026). Language teachers’ AI literacy: A psychometric study based on the ED-AI framework. Computers and Education: Artificial Intelligence, 10, 100583. https://doi.org/10.1016/j.caeai.2026.100583
Nikhil, V. (2025). A Comprehensive Study on AI-Enhanced Personalized Learning in STEM Courses. In Adopting Artificial Intelligence Tools in Higher Education (pp. 149–170). CRC Press.
Pandey, C. S., Mishra, P., Pandey, S. R., & Pandey, S. (2026). Epistemic trust in generative AI for higher education scale (ETGAI-HE scale). AI & SOCIETY, 41(2), 1387–1400. https://doi.org/10.1007/s00146-025-02566-6
Polydoros, G., Galitskaya, V., Pergantis, P., Drigas, A., Antoniou, A.-S., & Beazidou, E. (2025). Innovative AI-driven approaches to mitigate math anxiety and enhance resilience among students with persistently low performance in mathematics. Psychology International, 7(2), 46. https://doi.org/10.3390/psycholint7020046
Qian, Y. (2025). Prompt engineering in education: A systematic review of approaches and educational applications. Journal of Educational Computing Research, 63(7–8), 1782–1818. https://doi.org/10.1177/07356331251365189
Samtani, P. (2026). Self-explanation Prompts in STEM: Comparing Human and AI Metacognitive Accuracy. American Journal of Computer Science and Technology, 9(1), 19–29. https://doi.org/10.11648/j.ajcst.20260901.13
Serrano, S. M. (2026). Critical Generative AI Literacy for Social Studies Educators: A Typology of GenAI Errors and Their Impacts on Epistemology. Journal of Geography, 1–12. https://doi.org/10.1080/00221341.2026.2621014
Suherman, S., & Vidákovich, T. (2024). Relationship between ethnic identity, attitude, and mathematical creative thinking among secondary school students. Thinking Skills and Creativity, 51, 101448. https://doi.org/10.1016/j.tsc.2023.101448
Supriadi, N., Jamaluddin, W., Suherman, S., & Komarudin, K. (2025). The Role of Blended Learning in Improving Students’ Numerical Ability and Learning Creativity. Revista de Educación a Distancia (RED), 25(81). https://doi.org/10.6018/red.619061
Supriadi, N., Jamaluddin Z, W., & Suherman, S. (2024). The role of learning anxiety and mathematical reasoning as predictor of promoting learning motivation: The mediating role of mathematical problem solving. Thinking Skills and Creativity, 52, 101497. https://doi.org/10.1016/j.tsc.2024.101497
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3613904.3642902
Wang, X. (2025). The Influence of Gen‐AI Assisted Learning on Primary School Students’ Math Anxiety: An Intervention Study. Applied Cognitive Psychology, 39(4). https://doi.org/10.1002/acp.70088
Wang, X., Liu, Q., Pang, H., Tan, S. C., Lei, J., Wallace, M. P., & Li, L. (2023). What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis. Computers & Education, 194, 104703. https://doi.org/10.1016/j.caeai.2023.100120
Wardat, Y. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Available at SSRN 5653030. https://doi.org/10.29333/ejmste/13272
Xavier, A., Naeem, S. S., Rizwi, W., & Rabha, H. (2026). Comparing AI-Assisted Problem-Solving Ability With Internet Search Engine and e-Books in Medical Students With Variable Prior Subject Knowledge: Cross-Sectional Study. JMIR Medical Education, 12(1), e81264. https://doi.org/10.2196/81264
Copyright (c) 2026 Distance Education Journal

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Las obras que se publican en esta revista están sujetas a los siguientes términos:
1. El Servicio de Publicaciones de la Universidad de Murcia (la editorial) conserva los derechos patrimoniales (copyright) de las obras publicadas, y favorece y permite la reutilización de las mismas bajo la licencia de uso indicada en el punto 2.
2. Las obras se publican en la edición electrónica de la revista bajo una licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 3.0 España (texto legal). Se pueden copiar, usar, difundir, transmitir y exponer públicamente, siempre que: i) se cite la autoría y la fuente original de su publicación (revista, editorial y URL de la obra); ii) no se usen para fines comerciales; iii) se mencione la existencia y especificaciones de esta licencia de uso.
3. Condiciones de auto-archivo. Se permite y se anima a los autores a difundir electrónicamente las versiones pre-print (versión antes de ser evaluada) y/o post-print (versión evaluada y aceptada para su publicación) de sus obras antes de su publicación, ya que favorece su circulación y difusión más temprana y con ello un posible aumento en su citación y alcance entre la comunidad académica. Color RoMEO: verde.






