RECONFIGURATIONS OF PEDAGOGICAL CONTENT KNOWLEDGE IN THE AGE OF GENERATIVE ARTIFICIAL INTELLIGENCE: A THEORETICAL ESSAY FOR THE TEACHING OF PHYSICS AND CHEMISTRY

Abstract

This theoretical essay proposes the G-PCK (Generative Pedagogical Content Knowledge) framework to analyze the reconfigurations in the teaching of Physics and Chemistry prompted by Generative Artificial Intelligence (GAI). Grounded in Shulman’s Pedagogical Content Knowledge and employing conceptual analysis, the study examines how GAI transforms teacher expertise, shifting the educator’s role from knowledge transmitter to orchestrator of hybrid cognitive ecosystems. The G-PCK framework is structured into three dimensions: Generative Content Knowledge (GAI's capabilities and limitations); Generative Pedagogical Knowledge (mediation and personalization strategies); and Generative Ethical-Epistemological Knowledge (authorship, bias, equity, validation). Contextualized within the Brazilian educational landscape, the study discusses challenges such as student dropout, lack of laboratory resources, and insufficient teacher training. It presents implications for teacher education, proposing transversal integration of GAI into curricula, immersive methodologies, and continuous professional development, and outlines a tripartite research agenda (empirical investigations, methodological challenges, and epistemological reflections). It concludes that the G-PCK framework serves as a conceptual tool for a critical, pedagogically sound, and ethically grounded integration of GAI into science education.