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In the field of science education, Pedagogical Content Knowledge (PCK) has become a central construct for understanding teaching effectiveness and guiding teacher professional development. The importance of PCK in science teaching stems from the unique challenges posed by scientific concepts, which often involve abstract thinking and the need to engage students in authentic scientific practices. Effective science teachers must not only understand scientific concepts deeply but also anticipate student difficulties, select appropriate representations and analogies, design meaningful investigations, and assess student understanding in ways that promote scientific literacy [1].

Since Shulman’s [2,3] seminal work, numerous researchers have attempted to model and operationalize PCK, particularly in science education contexts. These efforts have resulted in diverse conceptualizations, each reflecting different interpretations of Shulman’s ideas and emphasizing different components of teacher knowledge [4]. Over four decades of research, several PCK models have been proposed, culminating in the publication of the Consensus Model of PCK [5] and its subsequent refinement in the Refined Consensus Model [6]. In parallel, developments in educational technology have further extended discussions around PCK. The integration of technology into teaching and learning led to the formulation of the Technological Pedagogical Content Knowledge (TPACK) framework, which emphasizes the interplay between content, pedagogy, and technology in effective instruction [7]. More recently, these discussions have intensified in response to the growing influence of Generative Artificial Intelligence (GenAI) in science education [7]. The continued relevance and evolution of PCK are also reflected in contemporary science education research. Notably, the 2025 conference of the European Science Education Research Association (ESERA) included a dedicated symposium examining the role and relevance of PCK in addressing twenty-first-century science education challenges, alongside systematic efforts to identify indicators of high-quality PCK [8,9].

In light of these developments, the present paper engages with the evolving discussion on the conceptualization of PCK. Its aim is threefold: (a) to present an overview of PCK models in science education, tracing their evolution from Shulman’s framework [2,3] to the Refined Consensus Model [6]; (b) to describe the components of these models and their theoretical foundations; and (c) to discuss PCK’s significance in addressing contemporary educational challenges, particularly the integration of GenAI in science teaching.