From Prompts to Pedagogy: Preservice Teachers' Experiences with AI-Assisted Math and Science Integrated Lesson Planning.

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Bibliographic Details
Title:	From Prompts to Pedagogy: Preservice Teachers' Experiences with AI-Assisted Math and Science Integrated Lesson Planning.
Authors:	Cho, Hoyun¹
Source:	Ohio Journal of School Mathematics. Summer2026, Vol. 103, p58-73. 16p.
Subject Terms:	Lesson planning, Intelligent tutoring systems, Reflective teaching, Science education, Mathematics education, Student teachers, *Digital literacy, Prompt engineering
Abstract:	This study examines the experiences of 34 elementary preservice teachers who completed both traditional and AI-assisted lesson planning for integrated mathematics and science instruction using the 5E instructional model. Through qualitative analysis of structured reflections and AI interaction logs, we found that the process of crafting effective AI prompts served as a powerful catalyst for pedagogical reflection: preservice teachers who struggled to articulate what they wanted from AI were, in effect, confronting gaps in their own mathematical understanding and instructional planning. Three key findings emerged: (a) prompt engineering functions as a form of mathematical pedagogical reasoning, requiring teachers to decompose learning objectives with precision; (b) evaluating AI-generated mathematics content develops critical AI Literacy skills specific to the discipline, with important differences between general-purpose and education-specific platforms; and (c) the structured comparison of traditional and AI-assisted plans creates productive cognitive dissonance that deepens professional judgment. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	This study examines the experiences of 34 elementary preservice teachers who completed both traditional and AI-assisted lesson planning for integrated mathematics and science instruction using the 5E instructional model. Through qualitative analysis of structured reflections and AI interaction logs, we found that the process of crafting effective AI prompts served as a powerful catalyst for pedagogical reflection: preservice teachers who struggled to articulate what they wanted from AI were, in effect, confronting gaps in their own mathematical understanding and instructional planning. Three key findings emerged: (a) prompt engineering functions as a form of mathematical pedagogical reasoning, requiring teachers to decompose learning objectives with precision; (b) evaluating AI-generated mathematics content develops critical AI Literacy skills specific to the discipline, with important differences between general-purpose and education-specific platforms; and (c) the structured comparison of traditional and AI-assisted plans creates productive cognitive dissonance that deepens professional judgment. [ABSTRACT FROM AUTHOR]
ISSN:	24725986