THURS-077 - Embedding Health in Every Subject: A Multi-year Case Study Using Generative AI in Schools

Brief Abstract Summary: Learn how a generative AI assisted planning process integrated National Health Education Standards (NHES) into core instruction at a middle school. In this multi year case study, teachers began with English Language Arts, then expanded integration to mathematics, science, and social studies. Evidence from teacher reflections, lesson artifacts, student feedback, and classroom assessments suggests consistent alignment to health standards, strong student engagement, and more efficient planning, without compromising subject rigor. This work lends support to the feasibility of AI supported integration and suggests potential utility for programs that prepare educators to embed health content within mandated subjects.

Detailed abstract description: Health education is often marginalized because of testing priorities, limited instructional time, and uneven teacher preparation, even though health literacy contributes to student success. This project, Health in Every Subject (HIES), was conducted as a multi year case study at one middle school. Prior to implementation, the school had no structured health education minutes and no designated health course. To address this gap, a team used generative AI as a collaborative planning tool to help teachers integrate National Health Education Standards (NHES) into existing courses. Year 1 focused on English Language Arts. Year 2 expanded integration to mathematics, science, and social studies. The central question was practical: can AI supported planning help educators embed meaningful health education within core subjects while maintaining each subject’s rigor?

Teachers participated in short, targeted professional learning on health education pedagogy and the practical use of generative AI for lesson design. Working with AI, they co developed lessons, assessments, and rubrics that aligned NHES standards/indicators with course objectives and standards in each subject. The work emphasized authentic connections between health education and existing subject standards, ensuring that health concepts were meaningfully embedded in classroom learning rather than added as separate or supplemental content. Implementation was documented through teacher reflections, review of instructional materials, student surveys, and informal classroom assessments gathered across the two years.

The accumulated evidence suggests consistent alignment with health education standards across subjects, steady student performance on standard-aligned tasks, and positive student perceptions of relevance and real-world applicability. Importantly, there was no observed decrease in performance in the existing academic subjects when health education was embedded. Teachers reported stronger engagement during many integrated lessons, clearer rubrics, and time savings in planning. Early hurdles included selecting the most precise standards for each unit, refining prompts, and calibrating assessments across subjects. These challenges diminished with iterative practice, brief coaching, and peer collaboration.

Taken together, these results provide preliminary evidence that AI assisted planning can help schools embed health education within the existing schedule and course structures, even where no health course is offered. The approach appears adaptable across subjects and lends support to models that aim to grow health literacy, relevance, and equity without displacing mandated content. While further study is warranted, the work provides emerging evidence that embedding health education across the curriculum could inform the design of future teacher preparation and health education programs.