Abstract

This qualitative research investigates how preservice mathematics teachers structure their instructional design processes with artificial intelligence [AI] by establishing a connection between mathematical understanding and explanation. This study, which views explanation as both a knowing and a teaching action that supports understanding, investigates how AI-powered environments affect preservice mathematics teachers' transitions in explanation. Using a qualitative multiple-case design, data were collected from twelve preservice mathematics teachers through lesson plans, AI prompt logs, micro-teaching videos (with and without AI support), and pre- and post-interviews. Thematic analysis revealed five interrelated themes: pedagogical transformation through AI support; shifts in explanatory strategies; transformation of mathematical understanding; reflective and epistemological awareness; and constraints and tensions in AI integration. The findings suggest that AI functions not merely as a technical assistant providing ready-made content, but as a dialogical partner prompting a rethinking of how explanations are constructed, justified, and adapted. AI-powered instructional designs enhanced relational understanding by promoting visualization, contextualization, and multiple forms of representation. By demonstrating how this process and understanding can support epistemological growth, it may contribute to research on AI in teacher education. Also, based on the findings, some suggestions for further research and significant implications for teacher education are provided. 

Keywords

References

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.