AI-Enhanced Physics Teacher Preparation: A Conceptual Design Framework for Developing Pre-Service Teachers' Topic-Specific Pedagogical Content Knowledge

The persistent difficulty students experience in learning Physics concepts has been widely linked to limitations in teachers' topic-specific pedagogical content knowledge (TSPCK). While teacher education programmes aim to strengthen pre-service teachers' instructional competence, many methodology courses remain insufficiently structured to support systematic TSPCK development and its enactment in classroom practice. Concurrently, the emergence of generative artificial intelligence (AI) tools has opened new possibilities for teacher education, yet limited research exists on how AI can be deliberately integrated into methodology courses to develop pre-service physics teachers' TSPCK. This conceptual paper presents a design framework for developing pre-service Physics teachers' TSPCK through the strategic integration of AI tools within a methodology course. Drawing on the TSPCK model (Mavhunga & Rollnick, 2013) and practice-based teacher education literature, the framework incorporates AI-enhanced Content Representations (CoRe), AI-assisted lesson observation and feedback, and AI-supported Video-Stimulated Recall (VSR) interviews to scaffold planning, enactment, and reflection. The paper synthesizes existing empirical evidence to justify the design features and demonstrates how AI tools can promote coherence between theory and practice in Physics teacher preparation. Ethical considerations, including bias, data privacy, and the role of AI as a pedagogical tool rather than a replacement for teacher reasoning, are discussed. The framework contributes to Physics education research by offering a context-responsive, technology-enhanced approach to TSPCK development that aligns with the transformative potential of AI in science teacher education.