Abstract
This study investigated how ChatGPT-based chatbots can support preservice mathematics teachers’ (PMTs) understanding of core teaching practices, specifically collective mathematical argumentation. Drawing on a situated learning perspective and principles of practice-based teacher education, we focused on the first two phases of the Generative Role-play AI Simulation for Pedagogy (GRASP) model, in which we designed customized AI chatbots to function as both knowledge builders and situation generators. Ten PMTs enrolled in a mathematics education course at a U.S. research university engaged with these chatbots to learn foundational ideas of collective argumentation, analyze argument structure, and examine the productivity of the provided classroom scenarios. Data sources included written assignments, reflections, and pre- and post-surveys. Analyses across multiple data sources indicated that most PMTs in this study developed a robust understanding of collective mathematical argumentation and of the teacher moves that facilitate it through their interactions with the customized AI chatbots, while also demonstrating awareness of the chatbots’ usefulness and inherent limitations. These findings highlight both the promise and the constraints of integrating AI tools into teacher preparation and point to design considerations for creating effective AI-supported learning environments.
- Almarashdi, H. S., Abu Khurma, O., AlArabi, K., Abulibdeh, E., & Yousef, J. (2026). AI-enhanced STEM education: A bibliometric study of research trends toward achieving sustainable development goals. European Journal of STEM Education, 11(1), Article 16. https://doi.org/10.20897/ejsteme/18190
- Andriessen, J. (2006). Arguing to learn. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 443–459). Cambridge University Press.
- Australian Curriculum Assessment and Reporting Authority [ACARA]. (n.d.). The Australian curriculum: Mathematics. https://www.australiancurriculum.edu.au/f-10-curriculum/mathematics/
- 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
- Biton, Y., & Segal, R. (2025). Learning to craft and critically evaluate prompts: The role of generative AI (ChatGPT) in enhancing pre-service mathematics teachers' TPACK and problem-posing skills. International Journal of Education in Mathematics, Science and Technology, 13(1), 202–223.
- Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. National Academy Press.
- Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage.
- Conner, A., Singletary, L. M., Smith, R. C., Wagner, P. A., & Francisco, R. T. (2014). Teacher support for collective argumentation: A framework for examining how teachers support students' engagement in mathematical activities. Educational Studies in Mathematics, 86(3), 401–429. https://doi.org/10.1007/s10649-014-9532-8
- Cross, D. I. (2009). Creating optimal mathematics learning environments: Combining argumentation and writing to enhance achievement. International Journal of Science and Mathematics Education, 7(5), 905–930. https://doi.org/10.1007/s10763-008-9144-9
- Davis, T. J., Merchant, Z., & Kwok, O. M. (2022). An examination of practice-based virtual simulations and preservice mathematics teaching efficacy and outcome expectancy. Education Sciences, 12, Article 262. https://doi.org/10.3390/educsci12040262
- Department for Education. (2014). National curriculum in England: Mathematics programmes of study. Her Majesty's Stationery Office.
- Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
- Ensor, P. (2001). From preservice mathematics teacher education to beginning teaching: A study in recontextualizing. Journal for Research in Mathematics Education, 32(3), 296–320. https://doi.org/10.2307/749829
- Francisco, J. M. (2025). Teacher support of collective argumentation in mathematics classrooms: Insights from an afterschool program. Journal of Mathematics Teacher Education, 28(1), 211–237. https://doi.org/10.1007/s10857-024-09651-5
- Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., ... & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886
- Gravetter, F. J., & Wallnau, L. B. (2021). Statistics for the behavioral sciences (11th ed.). Cengage Learning.
- Grossman, P., Hammerness, K., & McDonald, M. (2009). Redefining teaching, re-imagining teacher education. Teachers and Teaching: Theory and Practice, 15(2), 273–289. https://doi.org/10.1080/13540600902875340
- Gurl, T. J., Markinson, M. P., & Artzt, A. F. (2024). Using ChatGPT as a lesson planning assistant with preservice secondary mathematics teachers. Digital Experiences in Mathematics Education, 1–26. https://doi.org/10.1007/s40751-024-00162-9
- Habermas, J. (1984). Theory of communicative action: Volume one: Reason and the rationalization of society (T. McCarthy, Trans.). Beacon Press.
- Huynh, M. T., & Aichner, T. (2025). In generative artificial intelligence we trust: Unpacking determinants and outcomes for cognitive trust. AI & Society, 1–21. https://doi.org/10.1007/s00146-025-02378-8
- 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
- Kim, Y. R., Park, M. S., & Joung, E. (2025). Exploring the integration of artificial intelligence in math education: Preservice teachers' experiences and reflections on problem-posing activities with ChatGPT. School Science and Mathematics, 1–15. https://doi.org/10.1111/ssm.18336
- Kishnani, D. (2025). The uncanny valley: An empirical study on human perceptions of AI-generated text and images [Doctoral dissertation, Massachusetts Institute of Technology].
- Kosko, K. W., Rougee, A., & Herbst, P. (2014). What actions do teachers envision when asked to facilitate mathematical argumentation in the classroom? Mathematics Education Research Journal, 26(3), 459–476. https://doi.org/10.1007/s13394-013-0116-1
- Krummheuer, G. (1995). The ethnography of argumentation. In P. Cobb & H. Bauersfeld (Eds.), The emergence of mathematical meaning: Interaction in classroom cultures (pp. 229–269). Erlbaum.
- Lampert, M., Franke, M. L., Kazemi, E., Ghousseini, H., Turrou, A. C., Beasley, H., Cunard, A., & Crowe, K. (2013). Keeping it complex: Using rehearsals to support novice teacher learning of ambitious teaching. Journal of Teacher Education, 64(3), 226–243. https://doi.org/10.1177/0022487112473837
- Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
- Ledger, S., & Fischetti, J. (2020). Micro-teaching 2.0: Technology as the classroom. Australasian Journal of Educational Technology, 36, 37–54. https://doi.org/10.14742/ajet.4561
- Lee, D., Son, T., & Yeo, S. (2024). Impacts of interacting with an AI chatbot on preservice teachers' responsive teaching skills in math education. Journal of Computer Assisted Learning, 41(1), Article e13091. https://doi.org/ 10.1111/jcal.13091
- Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191, Article 104646. https://doi.org/10.1016/j.compedu.2022.104646
- Martin, A. J., Collie, R. J., Kennett, R., Liu, D., Ginns, P., Sudimantara, L. B., ... & Rüschenpöhler, L. G. (2025). Integrating generative AI and load reduction instruction to individualize and optimize students' learning. Learning and Individual Differences, 121, Article 102723. https://doi.org/10.1016/j.lindif.2025.102723
- Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press.
- McDonald, M., Kazemi, E., & Kavanagh, S. S. (2013). Core practices and pedagogies of teacher education: A call for a common language and collective activity. Journal of Teacher Education, 64(5), 378–386. https://doi.org/10.1177/0022487113493807
- Ministry of Education. (2007). The New Zealand curriculum.
- National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics.
- National Governors Association Center for Best Practices & Council of Chief State School Officers. (2010). Common Core State Standards for Mathematics. http://www.corestandards.org/Math/
- Schleiss, J., Laupichler, M. C., Raupach, T., & Stober, S. (2023). AI course design planning framework: Developing domain-specific AI education courses. Education Sciences, 13(9), Article 954. https://doi.org/10.3390/ educsci13090954
- Son, T., Yeo, S., & Lee, D. (2024). Exploring elementary preservice teachers' responsive teaching in mathematics through an artificial intelligence-based chatbot. Teaching and Teacher Education, 146, Article 104640.https://doi.org/10.1016/j.tate.2024.104640
- Staples, M., & Newton, J. (2016). Teachers' contextualization of argumentation in the mathematics classroom. Theory Into Practice, 55(4), 294–301. https://doi.org/10.1080/00405841.2016.1208070
- Staples, M., Newton, J., Kosko, K. W., Conner, A., Cirillo, M., Bieda, K., Yopp, D., Zaslavsky, O., Hummer, J., Strachota, S., Singh, R., An, T., Going, T., & Zhuang, Y. (2017). Using artifacts to explore conceptions and consequences of argumentation, justification, and proof [White Paper]. Working Group of the 39th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. Available at https://www.researchgate.net/profile/Karl-Kosko/publication/317267228_ White_ Paper_Using_Artifacts_to_Explore_Conceptions_and_Consequences_of_Argumentation_Justification_and_Proof/links/592f0edb0f7e9beee752bcd0/White-Paper-Using-Artifacts-to-Explore-Concep
- Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16. https://doi.org/10.1007/s11423-019-09701-3
- Toulmin, S. E. (2003). The uses of argument (Updated ed.). Cambridge University Press. (Original work published 1958)
- Uğraş, H., Uğraş, M., Papadakis, S., & Kalogiannakis, M. (2024). Innovative early childhood STEM education with ChatGPT: Teacher perspectives. Technology, Knowledge and Learning, 30, 809–831. https://doi.org/10.1007/ s10758-024-09804-8
- Wagner, P. A., Smith, R. C., Conner, A., Singletary, L. M., & Francisco, R. T. (2014). Using Toulmin's model to develop prospective secondary mathematics teachers' conceptions of collective argumentation. Mathematics Teacher Educator, 3(1), 8–26. https://doi.org/10.5951/mathteaceduc.3.1.0008
- Walkington, C. (2025). The implications of generative artificial intelligence for mathematics education. School Science and Mathematics, 1–10. https://doi.org/10.1111/ssm.18356
- Wood, T. (1999). Creating a context for argument in mathematics class. Journal for Research in Mathematics Education, 30(2), 171–191. https://doi.org/10.2307/749609
- Yackel, E. (2002). What we can learn from analyzing the teacher's role in collective argumentation. The Journal of Mathematical Behavior, 21(4), 423–440. https://doi.org/10.1016/S0732-3123(02)00143-8
- Yackel, E., & Cobb, P. (1996). Sociomathematical norms, argumentation, and autonomy in mathematics. Journal for Research in Mathematics Education, 27, 458–477. https://doi.org/10.5951/jresematheduc.27.4.0458
- Zhuang, Y., & Conner, A. (2020). Teacher questioning strategies in supporting validity of collective argumentation: Explanation adapted from Habermas' communicative theory. In A. I. Sacristán, J. C. Cortés-Zavala, & P. M. Ruiz-Arias (Eds.), Mathematics education across cultures: Proceedings of the 42nd Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 2288–2296). Cinvestav/AMIUTEM/PME-NA. https://doi.org/10.51272/pmena.42.2020
- Zhuang, Y., & Conner, A. (2022). Teachers' use of rational questioning strategies to promote student participation in collective argumentation. Educational Studies in Mathematics, 111(2), 345–365. https://doi.org/10.1007/ s10649-022-10160-6
- Zhuang, Y., & Zhang, S. (2025a). Pre-service mathematics teachers' perceptions of using GenAI for practicing teacher questioning: A semester-long study. Eurasia Journal of Mathematics, Science and Technology Education, 21(9), Article em2689.
- Zhuang, Y., & Zhang, S. (2025b). Integrating ChatGPT in mathematics teacher education: AI-based simulation role-playing to support practice-based teaching. International Journal of Artificial Intelligence in Education, 35(6), 3873–3895.https://doi.org/10.1007/s40593-025-00519-0
APA 7th edition
In-text citation: (Zhuang et al., 2026)
Reference: Zhuang, Y., Nudze, W. Y., Yao, X., & Davis, T. J. (2026). Utilizing customized ChatGPT-based chatbots to support preservice teachers’ understanding of collective mathematical argumentation.
European Journal of STEM Education, 11(1), Article 39.
https://doi.org/10.20897/ejsteme/18918
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Zhuang Y, Nudze WY, Yao X, Davis TJ. Utilizing customized ChatGPT-based chatbots to support preservice teachers’ understanding of collective mathematical argumentation.
European Journal of STEM Education. 2026;11(1), 39.
https://doi.org/10.20897/ejsteme/18918
Chicago
In-text citation: (Zhuang et al., 2026)
Reference: Zhuang, Yuling, Wisdom Yao Nudze, Xiangquan Yao, and Trina J. Davis. "Utilizing customized ChatGPT-based chatbots to support preservice teachers’ understanding of collective mathematical argumentation".
European Journal of STEM Education 2026 11 no. 1 (2026): 39.
https://doi.org/10.20897/ejsteme/18918
Harvard
In-text citation: (Zhuang et al., 2026)
Reference: Zhuang, Y., Nudze, W. Y., Yao, X., and Davis, T. J. (2026). Utilizing customized ChatGPT-based chatbots to support preservice teachers’ understanding of collective mathematical argumentation.
European Journal of STEM Education, 11(1), 39.
https://doi.org/10.20897/ejsteme/18918
MLA
In-text citation: (Zhuang et al., 2026)
Reference: Zhuang, Yuling et al. "Utilizing customized ChatGPT-based chatbots to support preservice teachers’ understanding of collective mathematical argumentation".
European Journal of STEM Education, vol. 11, no. 1, 2026, 39.
https://doi.org/10.20897/ejsteme/18918
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Zhuang Y, Nudze WY, Yao X, Davis TJ. Utilizing customized ChatGPT-based chatbots to support preservice teachers’ understanding of collective mathematical argumentation. European Journal of STEM Education. 2026;11(1):39.
https://doi.org/10.20897/ejsteme/18918