Learning to See Artificial Intelligence Differently: A Critical Analysis of One Preservice Teacher’s Engagement in a Secondary Mathematics Content Course

Abstract

The growing presence of large language models such as ChatGPT and Khanmigo has raised important questions about how these tools can be meaningfully integrated into mathematics teaching while centering student learning. In this pare, we share findings from a study that examined a structured instructional intervention designed to support prospective secondary teachers (PSTs). Situated within a mathematics content course, the study presents an in-depth analysis of one PST’s experience (Vani) to explore how engagement with AI intersects with developing teacher identity. Vani's case illustrates how carefully designed tasks and structured reflection can support PSTs in repositioning AI as a resource for reasoning rather than answer generation. It also highlights the value of attending to PST perspectives in shaping more responsive and contextually grounded approaches to AI integration in mathematics teacher education. Building on this study, we hope to refine and extend the intervention to examine how specific forms of scaffolding, peer collaboration, and task design shape PSTs’ engagement with AI.Through this work, we aim to contribute to the development of pedagogical approaches that support mathematics teacher educators in preparing PSTs to engage AI in critically and instructionally meaningful ways.