CO-designing science teaching simulations to support pre-service teacher talk moves

The National Science Foundation Division of Undergraduate Education awarded James Madison University $400,000 on October 15, 2026, under the STEM Education program (CFDA 47.076) to develop and implement AI-supported teaching simulations that help pre-service elementary science teachers practice high-quality classroom conversation techniques with virtual students.

The project co-designs interactive simulations with science teachers and teacher educators across multiple institutions, enabling approximately 400 pre-service teachers to rehearse instructional conversations while engaging with dynamic science visualizations and receiving immediate, individualized AI-generated feedback. The simulations target teacher talk moves that support elementary student science sensemaking and collaborative problem-solving. Deliverables include openly available learning resources designed for broad adoption across teacher preparation programs.

The project uses mixed methods to evaluate outcomes, analyzing simulation transcripts, AI-generated feedback, pre- and post-award measures of teacher talk move knowledge, usability and engagement surveys, classroom observations, and qualitative data from pre-service teacher reflections and interviews. Expected results include increased pre-service teacher use of high-quality talk moves and advancement of understanding regarding how AI-based simulations support elementary science teacher development.

Performance occurs in Harrisonburg, Virginia, with an ultimate completion date of September 30, 2029. The award is classified as a Project Grant, a standard assistance type for NSF-sponsored education research and development initiatives.

Abstract

This project aims to serve the national interest by improving elementary science teacher preparation through the development and implementation of AI-supported simulation and feedback tools that strengthen teacher classroom conversations. The project creates meaningful rehearsal opportunities for pre-service teachers so that they can effectively engage elementary age students in science sensemaking discussions. Its significance and importance LIE in preparing a strong pipeline of elementary educators who can foster scientific literacy, curiosity, and collaborative problem-solving skills consistent with the mission of the national science foundation. This level 1 engaged student learning project plans to CO-design teaching simulations with science teachers and science teacher educators, that enable pre-service teachers to rehearse instructional conversations with virtual students while interacting with dynamic science visualizations and to receive immediate, individualized feedback. Expected outcomes include increased pre-service teacher use of high-quality teacher talk moves and the creation of openly available learning resources for broad adoption. The goals of this project are to advance understanding of how AI-based simulations can be designed to support the development of elementary science teacher talk moves. Through the iterative CO-design, implementation, and study of teaching simulations within elementary science methods courses across multiple institutions, and by engaging science teacher educators and approximately 400 pre-service teachers, the project plans to use mixed methods to analyze simulation transcripts and AI-generated feedback, pre/post measures of teacher talk move knowledge, surveys of usability and engagement, classroom observations, and qualitative data from pre-service teacher reflections, interviews, and teacher educator CO-design activities. Assessment and evaluation anticipate examining changes in pre-service teachers? Use and understanding of talk moves, patterns of interaction in simulations, user perspectives on learning and usability, and the conditions that support effective integration, scalability, and sustained use across course contexts. Dissemination plans include open-access simulation resources, peer-reviewed publications, conference presentations, and the development of a community of practice to support broader adoption and customization. The project aims to contribute new knowledge about designing AI-enabled rehearsal environments and identifying mechanisms that support meaningful, feedback-rich practice in teacher education. This project is supported through the NSF iuse:EDU program and the robert noyce teacher scholarship program (noyce). The noyce program supports the training and development of undergraduate majors and professionals to become effective k-12 stem teachers and experienced, exemplary k-12 teachers to become stem master teachers in high-need school districts. The NSF iuse: EDU program supports research and development projects to improve the effectiveness of stem education for all students. Through the 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.- subawards are not planned for this award.