RE-EMERGING TEACHER EDUCATION: AI FOR CONTINUOUS PROFESSIONAL DEVELOPMENT FOR PHYSICS TEACHERS

Abstract

Teacher professional development in physics education is at a critical juncture. Traditional models of training, dominated by episodic workshops and generic instructional improvement sessions, are increasingly misaligned with the complex needs of physics teachers, who must grapple with conceptual abstraction, experimental pedagogy, and rapid technological change. Artificial Intelligence (AI) has emerged as a transformative force, offering adaptive, personalised, and continuous professional development (CPD). This paper explores how AI can reframe teacher education for physics educators, drawing upon contemporary research, case studies, and theoretical frameworks. It examines AI’s role in adaptive tutoring, virtual laboratories, feedback systems, professional learning communities, and credentialing mechanisms. Benefits include increased accessibility, contextualised learning, reflective practice, and global networking. Yet significant challenges remain, including over-reliance on simulations, equity gaps, data ethics, and AI literacy requirements. Anchored in emerging studies and empirical evidence, this paper argues for re-emerging teacher education that positions AI as an enabler of lifelong reflective practice and responsive professional growth in physics education. Future directions include AI-integrated dashboards, immersive VR/AR lab training, global AI-powered physics teacher academies, and robust policy frameworks. The study concludes that AI, responsibly deployed, can catalyse a paradigm shift, redefining physics teacher continuous professional development as an adaptive, personalised, and sustainable journey of professional empowerment.