Integrating ChatGPT into Science Lesson Planning in Malaysian PrimarySchools: A Mixed-Methods Study

Abstract

This mixed-methods study examined the integration of ChatGPT into Science lesson planning among teachers in National-Type Chinese Primary Schools (SJKC) in Selangor, Malaysia. Drawing on Constructivist Learning Theory, Differentiated Instruction, and the Technology Acceptance Model (TAM), the study employed a convergent parallel mixed-methods design. Quantitative data were collected from 100 Science teachers through structured surveys, while qualitative data were gathered through semi-structured interviews with 30 teachers. Findings reveal that ChatGPT significantly enhances lesson planning efficiency (78% agreement), creativity (M=4.12), and student engagement (M=4.20). However, challenges persist, including lack of professional development (M=4.05), curriculum alignment difficulties, and infrastructure limitations. Regression analysis identified professional training (β=-0.37, p<.001) and infrastructure availability (β=-0.29, p<.01) as significant predictors of adoption willingness. The study proposes strategies including structured professional development, curriculum-aligned guidelines, and Professional Learning Communities to optimize ChatGPT integration. Implications for teachers, school leaders, and policymakers are discussed.