Designing Chatbot-Based Learning Experiences to Enhance Preservice Elementary Teachers’ Responsiveness to Student Mathematical Thinking

This project aims to serve the national interest by improving preservice elementary teachers’ responsive teaching skills in mathematics, leading to improved STEM learning of their students. Specifically, principal investigators at Kennesaw State University and the University of Missouri-Columbia, will collaborate to develop and nurture responsive teaching skills in mathematics among their preservice teachers by using Artificial Intelligence (AI)-based chatbots. Laying a solid mathematical foundation in students’ early years is essential to the development of a STEM workforce. Responsive teaching promotes students’ mathematical reasoning abilities and positive attitudes toward mathematics. However, many preservice teachers have limited opportunities to practice responsive teaching prior to working with real students. In this project, researchers will explore how chatbots can serve as an innovative learning platform to provide extensive opportunities to practice and refine responsive teaching skills such as noticing, eliciting, and exploring student thinking. The outcomes of the project will advance the knowledge base and expand best practices for teacher preparation in mathematics. Using principles of design-based research, the project’s goals are to develop, enact, evaluate, and refine three modules in the context of teaching fractions, a critical concept in early mathematics learning. In each module, users will: 1) be presented with a representation of teaching practice, 2) engage in a decomposition of practice, 3) participate in authentic interaction with a chatbot that serves as both a virtual student sharing a particular mathematical strategy and a mentor teacher supporting the interaction, and 4) reflect on their interactions. The scope of the project's activities includes: 1) developing the instructional modules including AI-based chatbots and 2) disseminating the modules for broader impact. The process entails developing, enacting, evaluating, and refining the modules through three iterations. Various data will be collected to improve the chatbots and evaluate their efficacy. This will generate design principles that can be used in teacher education across other mathematical topics and STEM fields. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.