Advancing Mathematics Teacher Education with GenAI as Students: An Innovative Conversational Role-Play Approach

Texas A&M University 

Principal Investigator

(Co-P.I.: Zhengzhong Tu, Texas A&M University)

Funded by Collaborative Seed Grants (Internal). Targeted Proposal Teams, Texas A&M University. 

The purpose of this project is to develop an innovative, open-source Generative AI (GenAI) platform designed to support practice-based teaching for preservice mathematics teachers (PMTs) through simulated role-play scenarios. Specifically, the project aims to simulate authentic student thinking, including misconceptions and diverse thought processes related to mathematical functions and proportion reasoning. In this project, we will invite mathematics education experts, including practicing mathematics teachers and mathematics teacher educators, to test the AI platform and provide structured feedback. Their insights will be used to iteratively refine the platform to better support the training of PSMTs in teacher education programs. The findings from this study will be disseminated through peer-reviewed journal publications and presentations at national conferences to contribute to the broader field of AI integration in mathematics teacher education.

Empowering Preservice Math Teachers to Build Argumentation Skills through GenAI 

Texas A&M University 

Principal Investigator

(Co-P.I.: Trina Davis, Texas A&M University; Xiangquan Yao, Pennsylvania State University)

Funded by Emerge: Artificial Intelligence (AI) Grants (Internal). College of Education & Human Development, Texas A&M University 

This project aims to transform mathematics teacher education by developing a research-informed Generative Role-Play AI Simulation for Pedagogy (GRASP) Model powered by ChatGPT, designed to prepare preservice mathematics teachers (PMTs) to effectively support mathematical argumentation—one of the most complex and critical core practices in STEM education. This project responds to the call from the National Council of Teachers of Mathematics (NCTM, 2024) to integrate Generative AI (GenAI) into mathematics teacher education. The GRASP Model aims to empower PMTs to engage in practice-based teaching through role-play simulations, equipping them to support mathematical argumentation. Through a structured four-phase learning cycle—initiating, exploring, enacting, and reflecting—the GRASP model aims to create a dynamic and interactive environment in which PSMTs engage in guided trial-and-error experiences to develop and refine their pedagogical practices. 

Technology-Mediated Lesson Study for Rural Math and Science Teachers: A Professional Development and Leadership Initiative

Texas A&M University 

Co Investigator

(P.I.: Karen Rambo-Hernandez, Texas A&M University; Rebecca Sansom, Texas A&M University)

Funded by R3 (Review, Revise, & Resubmit) Grant (Internal), College of Education & Human Development, Texas A&M University.  Originally submitted for NSF 24-577: National STEM Teacher Corps Pilot Program. 

This project outlines a comprehensive year-long professional development program designed to address these challenges by supporting eight middle school teachers (four mathematics and four science educators) from rural schools through an innovative Technology-Mediated Lesson Study (TMLS) experience. 

The program centers on developing teachers' expertise in implementing argumentation as a core mathematical and scientific practice—a critical component of contemporary standards that requires students to construct viable arguments, critique reasoning, and engage in evidence-based discourse. Through collaborative lesson planning, iterative teaching cycles, and technology-enhanced reflection, participating teachers will not only strengthen their own instructional practices but also develop leadership capacity to share their learning with the broader educational community. 

Exploring the Potential of AI-assisted Tools in Enhancing Prospective Secondary Mathematics Teachers’ Argumentative Skills

Emporia State University 

Principal Investigator

(Co-P.I.: Si Zhang, Georgia State University)

Funded by The Kathrine K. White Faculty Incentive Grant Program, (Internal). Emporia State University.

The purpose of this project is to investigate the potential benefits and efficacy of leveraging artificial intelligence (AI) technologies, such as ChatGPT, in the methods course for prospective secondary mathematics teachers (PSTs). This project aims to explore whether AI-based educational technologies can efficiently improve PSTs teaching skills, such as generating high-level questions and supporting classroom-based argumentation. By exploring this intersection of AI technology and pedagogical training, we hope to offer insights into innovative methods of enhancing the preparation of the next generation of mathematics educators.

Use Artificial Intelligence to Promote Students’ Mathematic Learning in College-Level Calculus Classes

Emporia State University 

Principal Investigator

(Co-P.I.: Si Zhang, Georgia State University)

This project is to investigate the potential of integrating artificial intelligence (AI) technologies, such as ChatGPT, into college calculus instruction to improve students' understanding of calculus concepts, such as limits, differentiation, and the Mean Value Theorem. The project aims to develop, implement, and evaluate AI-driven teaching tools and methodologies that can effectively supplement traditional teaching approaches. By implementing a more interactive and personalized AI-assisted learning experience, we will investigate students’ mathematical learning through the lens of cognitive, affective, participatory, and equity goals. 

Promoting Learning of Mathematical Proofs through Collective Argumentation Online 

Emporia State University 

Principal Investigator

Considering the critical role of mathematical collective argumentation in students' proving and argumentation activities, this project was designed to promote learning of mathematical proofs through collective argumentation in an online interactive learning community. The primary objective of this study is to analyze students' proving processes and examine how the use of collective argumentation, along with instructional tools based on Habermas' (1998) construct, influences their learning of mathematical proofs.

Principal Investigators: Timothy Foutz, AnnaMarie Conner, Barbara Crawford, Roger Hill, David Jackson, ChanMin Kim, & Sidney Thompson

The Collective Argumentation and Learning Coding (CALC) project is a collaboration between faculty at the Mary Frances Early College of Education and the College of Engineering at the University of Georgia.

Elementary school teachers impact student motivation to pursue STEM fields of study and careers and are being increasingly asked to emphasize key STEM content areas such as computer science in their teaching. The CALC project, funded by NSF, designed a practice that elementary school teachers can use to integrate the teaching of coding with the standard practices already used to teach mathematics, science, and other curriculum content.

The project also developed a model course that prepares teachers to educate students in interdisciplinary, holistic ways to learn mathematics, science, and coding and equips them to guide students through reasoning processes while learning to code.

I have been involved in this project since it started in 2017, initially as a graduate research assistant. I later served as a post-doctoral researcher for one year and have continued my work on the project. In a critical case study I led, we examined how teachers could use educational robotics to integrate mathematics and coding (Zhuang et al., 2022). Our findings suggest that educational robotics, combined with argumentation, can effectively teach mathematics concepts in a manner consistent with an integrative STEM perspective. Additionally, we investigated the types of argument dialogue encountered in elementary classrooms focused on learning concepts in science, mathematics, and computer coding (Foster et al., 2022). The results of these studies provide valuable insights for teachers in planning and guiding students on how to argue and learn through argumentation in STEM education. Currently, we are developing a framework to examine teacher actions in supporting STEM activities through the lens of collective argumentation (manuscript in preparation). We propose that this framework will be beneficial for STEM and coding instruction across various grade levels and disciplines.

Principal Investigator: AnnaMarie Conner

This project documented how mathematics teachers learned to support their students in engaging in productive collective argumentation. The research team followed a cohort of prospective secondary mathematics teachers through their mathematics education coursework, observing their engagement in collective argumentation and opportunities to learn about supporting this practice.

I have been working on this project since 2015. To date, we have published several research articles based on this project, including studies on how teachers use teacher actions (e.g., display and teacher questions) to support classroom-based argumentation, and how prospective teachers recontextualize their understanding of argumentation into classroom practices.