Pedagogical Content Knowledge in Science Education

Definition

The concept of Pedagogical Content Knowledge (PCK) was introduced by Shulman in 1986 as a distinctive form of teacher knowledge that transcends mere content expertise or general pedagogical skills. Shulman described PCK as “the amalgam of content and pedagogy” that distinguishes the experienced teacher from the content specialist. This conceptualization revolutionized research on teacher knowledge by highlighting the importance of understanding how teachers transform subject matter into forms that are pedagogically sound and accessible to diverse learners. Since Shulman’s seminal work, numerous PCK models have been developed, leading to the Consensus Model of PCK published in 2015 and, subsequently, the Refined Consensus Model of PCK in 2019. Both frameworks move the field beyond static views of teacher knowledge and emphasize the recursive processes through which teachers plan, teach, reflect, and reshape their professional knowledge. Over four decades of PCK research, PCK models have differed in their epistemological grounding, as well as in the components used to represent the structure of the PCK construct.

1. Introduction

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.

2. Models of PCK

2.1. Historical Evolution of PCK Models

A previous literature review conducted by Chaitidou [10] aimed to identify distinct PCK models developed specifically for science education and to analyze their proposed components. This review identified twenty-one studies that presented distinct PCK models, each offering an explicit graphic or tabular representation and a clearly defined set of PCK components. In the present overview, we draw on these twenty-one models [10] and complement them with the Refined Consensus Model [6], which constitutes the most recent theoretical contribution to the field. Consequently, twenty-two PCK models are presented chronologically in

Table 1. Timeline of PCK models.

Authors/Year	Short Description
Tamir [11] 1988	Extended Shulman’s work [2,3] by identifying six domains of teacher knowledge presented as a table. Introduced subject matter specific pedagogical knowledge, integrated substantive and syntactic knowledge, and clarified that skills arise through experience rather than transmission.
Cochran et al. [12] 1993	Redefined PCK as Pedagogical Content Knowing (PCKg), emphasizing its dynamic nature and the integration of four interrelated components: content, learners, context, and pedagogy. Represented the model as intersecting circles, with PCKg at the center as an ellipse.
Geddis [13] 1993	Linked PCK to teachability, highlighting its role in teacher education. Focused on knowledge of students, instructional strategies, and representations, emphasizing the value of understanding misconceptions for effective teaching.
Fernandez-Balboa & Stiehl [14] 1995	Conceptualized general PCK among university lecturers as the integration of five key components: content, students, teaching strategies, context, and goals. Focused on teaching across multiple courses rather than subject-specific PCK.
Carlsen [15] 1999	Viewed PCK as one of five knowledge categories (students, aims, curriculum, and instructional strategies) represented as nested rectangles. Positioned PCK as being informed by general pedagogical knowledge and content knowledge, emphasizing the interaction with context.
Gess-Newsome [16] 1999	Proposed that PCK is the intersection of three components: pedagogical, content, and contextual knowledge. Suggested a “mixture” metaphor where components maintain their properties but integrate differently during teaching.
Magnusson et al. [17] 1999	Presented the most influential five-component model: orientations toward teaching science, curriculum, assessment, students, and instructional strategies. Viewed PCK as specialized knowledge transforming content for teaching, with reflection as a central element.
Morine-Dershimer & Kent [18] 1999	Offered a six-component model interrelating students, curriculum, assessment, context, pedagogy and content. Represented components as rectangles connected by arrows, noting strong links between instructional goals, assessment, and curriculum.
Veal & MaKinster [19] 1999	Developed a hierarchical taxonomy grounded in Bloom’s taxonomy, embedding student knowledge within content knowledge. Argued that understanding learners’ misconceptions underpins effective development of all other components.
Hashweh [20] 2005	Proposed a heptagonal model centered on teacher pedagogical constructions (TPCs)—individualized, topic-specific knowledge structures formed through teaching and reflection. Defined PCK as the sum of an individual teacher’s TPCs.
Lee & Luft [21] 2008	Identified seven PCK components that each teacher connects differently. Asserted that every teacher possesses a general PCK core consisting of content knowledge, goals, and knowledge of students.
Rollnick et al. [22] 2008	Positioned PCK at the interface between knowledge domains (content, students, context, and pedagogy) and their manifestations in practice (e.g., strategies, representations, assessment, and curricular saliency).
Park & Oliver [1] 2008	Expanded the Magnusson et al. [17] model to include teacher efficacy. Argued that PCK arises from the integration of six interdependent components, reinforced through reflection, and represented them as the vertices of a hexagon.
Abell et al. [23] 2009	Visualized PCK components (curriculum, assessment, strategies, and students) within a framework filtered through orientations toward teaching science, highlighting their overarching influence.
Davidowitz & Rollnick [24] 2011	Modified the model of Rollnick et al. [22] by adding beliefs about learning, students, and the teacher’s classroom role. Used double-headed arrows to show bidirectional interactions between components and beliefs.
Schneider & Plasman [25] 2011	Provided a 30-year literature review rather than a new model, examining learning progressions-defined as “successively more sophisticated ways of thinking” across teachers’ career stages (prospective, beginning, experienced, very experienced).
Park & Chen [26] 2012	Introduced a pentagonal model, asserting that PCK quality depends on the coherence and integration of five components: orientations, strategies, assessment, curriculum, and students. Positioned PCK at the center of the pentagon.
Mavhunga & Rollnick [27] 2013	Presented the model of topic-specific PCK (TSPCK) applied in a pre-service teacher education program. Identified a mutual relationship between PCK and content transformation, suggesting that they develop concurrently.
Otto & Everett [28] 2013	Presented a Venn diagram consisting of three intersecting domains—pedagogy, context, and content—to introduce the concept of PCK to prospective teachers, defining it as the integration of these three knowledge bases.
Sothayapetch et al. [29] 2013	Represented PCK as an ellipse incorporating two smaller ellipses (content knowledge and pedagogical knowledge). Emphasized the importance of general pedagogical knowledge (GPK), including classroom management and communication.
Consensus Model [5] 2015	Unified previous models by distinguishing five knowledge domains interacting with Topic-Specific Professional Knowledge (TSPK), which—through amplifiers and filters—is transformed into enacted, personal PCK. Introduced “PCK and Skills” (PCK&S), linking teacher knowledge directly to classroom enactment.
Refined Consensus Model [6] 2019	Conceptualized PCK as a complex system across three realms: collective (cPCK), personal (pPCK), and enacted (ePCK). Highlighted the cyclical process of planning, teaching, and reflecting, while acknowledging the influence of the learning context and the broader professional knowledge bases.

, each accompanied by a concise description of its key features.

This historical overview demonstrates that PCK conceptualizations have evolved from relatively simple distinctions, for example, Tamir’s [11] six domains, to increasingly nuanced frameworks that capture the complexity of teacher knowledge. Several key insights emerge from this historical development. First, the field has shifted from viewing PCK as a relatively static body of knowledge to understanding it as dynamic, context-specific, and continually shaped through reflection and practice. Second, PCK operates at multiple levels, namely collective professional knowledge, personal knowledge, and enacted knowledge. Third, although substantial consensus exists regarding core components such as knowledge of students’ understanding and instructional strategies, considerable variation remains in how researchers conceptualize the boundaries between components and their interrelationships. Fourth, recent models increasingly emphasize the role of teacher beliefs, orientations, and self-efficacy, acknowledging that effective teaching involves not only cognitive knowledge but also affective and motivational dimensions. Finally, the Refined Consensus Model represents the current state of the art, integrating insights from four decades of research.

2.2. Epistemological Aspects of PCK Models

The conceptualization of PCK necessitates consideration of whether it arises through the integration or the transformation of existing knowledge bases. Gess-Newsome [16] substantially advanced this discourse by articulating two epistemological models: the integrative model and the transformative model.

Within the integrative model, PCK is conceived as the intersection of three distinct knowledge domains: content knowledge, pedagogical knowledge, and context knowledge. This relationship is commonly illustrated through the analogy of a mixture, in which the constituent components retain their initial properties while being combined in such a way that they are no longer macroscopically distinguishable. PCK constitutes an integrated knowledge base that teachers draw upon in instructional contexts, where the three forms of knowledge interact in multiple configurations without undergoing fundamental transformation [16]. By contrast, the transformative model conceptualizes PCK as a qualitatively new form of knowledge generated through the transformation of the aforementioned knowledge bases. This process is often compared to the formation of a chemical compound, wherein the original elements lose their separability and initial properties, resulting in PCK as a novel construct [16].

Kind’s [30] classification further refines this distinction by suggesting that PCK models incorporating content knowledge as a core component are aligned with the integrative model, whereas models that position content knowledge outside of PCK correspond more closely to the transformative model. For instance, the model proposed by Morine-Dershimer and Kent [18] is categorized as integrative, given that content knowledge is explicitly defined as a constituent of PCK. Conversely, the influential model advanced by Magnusson et al. [17] does not include content knowledge as a component of PCK and is therefore situated within the transformative category. As noted by Gess-Newsome [16], the integrative and transformative models represent the two poles of a conceptual continuum. The integrative model emphasizes the flexible deployment of relatively stable knowledge components during instructional planning and enactment [31,32], whereas the transformative model posits that these components are synthesized during teaching, giving rise to new configurations of knowledge that support student understanding [32].

These epistemological distinctions carry significant implications for the design and orientation of teacher education programs. When PCK is conceptualized as an integrative construct (integrative model), teacher preparation tends to prioritize the systematic development of robust knowledge bases across distinct domains—namely content, pedagogy, and context—alongside opportunities for their deliberate and flexible integration. An illustrative example is provided by Chaitidou et al. [33], who demonstrate how primary school teachers can be explicitly introduced to these discrete knowledge domains and subsequently supported in coherently interrelating them within an inquiry-oriented instructional sequence addressing complex subject matter, such as nanotechnology.

Conversely, when PCK is framed as a transformative construct (transformative model), teacher education programs place greater emphasis on the underlying processes of pedagogical reasoning through which disciplinary knowledge is fundamentally reconstituted into forms that are pedagogically accessible and meaningful to learners. This perspective is exemplified in the work of Aydin et al. [34], who, employing a transformative analytical framework, illustrate that preservice chemistry teachers do not simply “combine” predefined knowledge components but instead undergo a profound reorganization of their reasoning. Through sustained mentoring and practicum-based experiences, teachers develop a highly idiosyncratic and internally coherent professional knowledge base in which subject matter is extensively reshaped in accordance with the specific demands of student understanding.

However, even pioneer researchers in PCK have questioned whether PCK is transformative or integrative. Kind [30] initially argued that transformative models implied cognitive mechanisms that transform content knowledge into PCK and, in turn, into students’ knowledge. This view seemed consistent with their early findings [4]. However, no clear mechanism was identified [30]. After re-examining the data, Kind found content knowledge embedded within preservice teachers’ PCK statements, leading to the acknowledgement that distinguishing between transformative and integrative PCK models may be irrelevant [30].

This ambiguity led the science education community to seek a more unified framework, moving beyond the integrative-transformative dichotomy toward more holistic representations. These efforts culminated in the development of the Consensus Model and its subsequent evolution, the Refined Consensus Model, which provide a multi-layered perspective on how professional knowledge is structured and enacted in practice.

2.3. Consensus Model and Refined Consensus Model of PCK in Science Education

Recognizing the need for a unified framework twenty-two PCK researchers with divergent perspectives on epistemological positions and models [5] participated in the first PCK Summit in 2012 in Colorado [35]. During the Summit, participants were challenged to reconsider their own models of PCK and to reflect on whether a unified model could be identified. Recursive presentations to the larger group enabled ideas to evolve and allowed underlying assumptions to be revealed or questioned. Ultimately, the most promising ideas were synthesized into a single model, including key definitions, examples, and relationships that shaped the Consensus Model [5].

The Consensus Model begins with the generic professional knowledge bases, representing the profession’s shared knowledge developed through research and best practice, including assessment knowledge, pedagogical knowledge, content knowledge, knowledge of students, and curricular knowledge. These bases interact with topic-specific professional knowledge, that is, knowledge of instructional strategies, content representations, student understandings, science practices, and habits of mind, emphasizing that content for teaching is organized at the topic level, integrates subject matter, pedagogy, and context, and may function as professional knowledge with a normative role [5].

Building on these knowledge bases, the Consensus Model recognizes the influence of teacher affect, which acts as an amplifier or filter of teacher learning and mediates instructional decisions. PCK is considered both as a knowledge base for planning and delivering topic-specific instruction in a specific classroom context and as a skill enacted during the teaching process. Student outcomes are also shaped by student amplifiers and filters (i.e., student beliefs, prior knowledge, and behaviors), thereby highlighting that learning is not automatic. Therefore, the Consensus Model is recursive and dynamic, since classroom practice and student outcomes inform the act of teaching, the topic-specific professional knowledge, and the broader teacher professional knowledge bases [5].

Following the publication of the Consensus Model [5], researchers at the second PCK Summit in Leiden (2016) identified a key limitation: the framework offered minimal detail regarding PCK itself. They noted that, although PCK was situated within a broader picture of professional knowledge and skills, it nevertheless required further elaboration. Specifically, the original model did not explicitly unpack the complex variables, layers, and complexities of PCK as well as its relation to teaching practice [6,36]. To address these limitations, summit participants collaborated to develop an updated version of the model. This refinement process was informed by further discussions at the National Association for Research in Science Teaching (NARST) and the European Science Education Research Association (ESERA) conferences in 2017, ultimately leading to the articulation of the Refined Consensus Model [6].

The Refined Consensus Model [6] places PCK at the center of science teaching, illustrating how teachers’ knowledge and experience shape practice and mediate student outcomes. It identifies three realms of PCK: collective, personal, and enacted (Figure 1). Collective PCK (cPCK) is the shared knowledge base among professionals for teaching specific subject matter to particular learners within a defined context. Personal PCK (pPCK) represents the evolving knowledge and skills an individual teacher develops through personal experience and through interactions with others. Enacted PCK (ePCK) refers to the knowledge and skills a teacher draws on when planning, teaching, and reflecting on instruction and student outcomes. The model also recognizes that broader professional knowledge—including content, pedagogical, curricular, and assessment knowledge—are essential for teacher PCK. Finally, the learning context, encompassing policy, community values, and student characteristics, continually shapes the teaching and learning process [6].

Across a science teacher’s professional trajectory, knowledge flows bidirectionally among the model’s concentric circles. The knowledge and skills drawn from each domain are gradually filtered, mediated, and refined, shaping the teacher’s personal PCK (pPCK). Beliefs and attitudes concerning students, the nature of scientific content, and the teacher’s role function as amplifiers or filters that influence how this personal knowledge evolves over time [6]. Knowledge is also exchanged when teachers make instructional decisions about how to teach particular content to particular students in specific classroom contexts. These decisions are moderated by the teacher’s amplifiers and filters and shape the professional knowledge used in teaching, namely enacted PCK (ePCK). Experiences from classroom implementation then provide feedback that further informs and develops a teacher’s personal PCK. Finally, through discussion and shared professional activity, such as participating in research projects or professional learning communities, teachers may contribute to the development of collective PCK (cPCK), which in turn informs the broader professional knowledge base [6].

However, although the Refined Consensus Model is an updated framework, the researchers who contributed to its development emphasize that it should not be viewed as a replacement for other models [6]. For example, the model proposed by Magnusson et al. [17] and its subsequent variations remain a valuable tool for further unpacking what teachers do and think during the development of personal PCK and enacted PCK. Specifically, Magnusson’s framework allows for a detailed focus on specific PCK components, namely: orientation to science teaching, knowledge of science curricula, knowledge of students’ understanding, knowledge of instructional strategies, and knowledge of assessment of scientific literacy (Figure 2).

3. Components of PCK Models

Having outlined the historical development and epistemological foundations of PCK models in science education, attention now turns to an examination of their constituent components. This analysis draws on the models presented in Table 1 and is based on a systematic review of each model’s visual or tabular representation, complemented by close reading of the associated textual descriptions [10]. Components were defined as knowledge elements explicitly depicted as distinct entities (e.g., labeled domains, nodes, or categories) and described in the text as constitutive parts of the model. Textual sources were used to clarify the meaning and function of visually represented components, not to infer additional elements. Concepts mentioned only descriptively, or processes not explicitly represented as structural components, were excluded. This procedure ensured analytical consistency and limited interpretation to elements explicitly articulated by the original authors [10].

The twenty-two models examined vary considerably in both the number and type of components proposed. Such variation reflects underlying theoretical differences as well as terminological inconsistencies in component definition and labeling. Across these models, nine distinct components were identified. Knowledge of students’ understanding is the most prevalent, appearing in nearly all models. A second group of seven components—knowledge of instructional strategies, knowledge of context, content knowledge, knowledge of curriculum, pedagogical knowledge, orientations to teaching science, and knowledge of assessment—appears in the majority of models. Teacher self-efficacy, included in only one model, constitutes a singular case.

The following subsections provide a detailed discussion of each component, informed by the theoretical and empirical literature that underpins current understandings of science teachers’ professional knowledge. Although presented separately for analytical purposes, these components are intrinsically interconnected in practice, and their integrated enactment constitutes the core of effective science teaching.

3.1. Knowledge of Students’ Understanding of Science

Knowledge of students’ understanding of science constitutes one of the most widely recognized and theoretically robust components of PCK in science education. It refers to teachers’ knowledge of students’ prior conceptions, alternative ideas, learning difficulties, and cognitive prerequisites associated with specific scientific topics [12,17,20]. This component has been consistently acknowledged as central to effective science teaching [17]. The importance of understanding students’ thinking is rooted in Shulman’s foundational work on PCK, which emphasized teachers’ awareness of learners’ preconceptions and misconceptions as a prerequisite for meaningful instruction [2,3].

In the influential model proposed by Magnusson et al. [17], knowledge of students’ understanding is identified as one of five key PCK components. This knowledge directly informs instructional decisions, including the selection of representations, examples, and teaching strategies. Similarly, Park and Oliver’s [1] pentagonal model positions knowledge of students as dynamically interconnected with other PCK components, including assessment, and orientations to teaching science. The model highlights the reciprocal relationships among components and emphasizes that teachers’ understanding of student thinking evolves through classroom interaction and reflection. More recent frameworks, such as the Refined Consensus Model of PCK, further reinforce the centrality of this component by distinguishing between shared, research-based knowledge of student understanding and teachers’ personal, context-specific knowledge developed through practice [6].

Across integrative and transformative PCK models, knowledge of students’ understanding of science consistently functions as a mediating construct between content knowledge and pedagogical action, underscoring its indispensable role in the transformation of scientific knowledge into teachable forms.

3.2. Knowledge of Instructional Strategies

Knowledge of instructional strategies refers to teachers’ knowledge of how to render specific scientific content accessible, intelligible, and learnable for students through appropriate pedagogical approaches. It operates simultaneously at general and topic-specific levels, reflecting the specialized and situated nature of PCK.

A particularly influential and widely adopted conceptualization of this component was articulated by Magnusson et al. [17], who defined knowledge of instructional strategies as comprising two interrelated dimensions. The first dimension of this component concerns subject-specific instructional strategies, which include general pedagogical approaches aligned with the aims of science education, such as inquiry-based instruction and conceptual change strategies. The second dimension involves topic-specific instructional strategies, referring to pedagogical approaches tailored to teaching particular science topics or instructional units. This distinction highlights the dual character of instructional strategy knowledge as both broadly pedagogical and deeply content-sensitive.

Magnusson et al. [17] further distinguished between two types of instructional strategies: representations and activities. Representations refer to the ways in which teachers present scientific concepts and phenomena, including analogies, models, examples, and visualizations, as well as teachers’ awareness of the affordances and limitations of these representations for student understanding. Activities include demonstrations, experiments, investigations, and simulations that engage students with scientific phenomena and support the development of conceptual understanding. Together, representations and activities constitute the primary means through which teachers transform disciplinary knowledge into forms that are pedagogically powerful and accessible.

Across subsequent PCK frameworks, knowledge of instructional strategies has been positioned and interpreted in diverse yet convergent ways. Geddis [13] foregrounded instructional strategies and representations as central to the transformation of subject-matter knowledge into teachable content, emphasizing their role in enabling student understanding. In more comprehensive multi-component models, instructional strategies are consistently depicted as interacting dynamically with other PCK components. Magnusson et al. [17] portrayed instructional strategies as one of five interrelated components, stressing that effective teaching depends on coherence among all PCK elements.

Empirical studies further elaborated the integrative function of instructional strategy knowledge. Lee and Luft [21] demonstrated that knowledge of instructional strategies is strongly connected to knowledge of students and evolves differently across teachers and career stages. Park and Oliver [1] retained the dual nature of instructional strategy knowledge in their hexagonal model, emphasizing reciprocal relationships among all PCK components. Park and Chen [26] identified knowledge of instructional strategies, together with knowledge of students, as central to component integration, showing that orientations to teaching science guide the selection and enactment of specific strategies.

More recent conceptualizations, such as the Consensus Model, position knowledge of instructional strategies and content representations as a core element of topic specific professional knowledge, which is subsequently transformed into enacted PCK through contextual and personal filters [5]. Across these frameworks, knowledge of instructional strategies emerges as indispensable for understanding how teachers operationalize PCK in classroom practice.

Knowledge of instructional strategies represents a central pillar of PCK, capturing teachers’ capacity to transform scientific knowledge into pedagogically meaningful forms. Its consistent presence across a wide range of PCK models underscores its foundational role in effective science teaching and its critical contribution to the coherence, integration, and quality of teachers’ PCK.

3.3. Knowledge of Context

Knowledge of context constitutes a significant, variably conceptualized component of PCK in science education. It encompasses teachers’ understanding of the multiple situational, social, cultural, institutional, and material factors that shape and constrain instructional practice. Across PCK models, context is referred to by various terms—including context, environment, resources, and socioculturalism—reflecting its multidimensional nature [18,19,20,28].

Foundational models explicitly incorporated context knowledge within PCK. Cochran et al. [12] identified “knowledge of environmental contexts” as one of four components in their Pedagogical Content Knowing (PCKg) model, interacting with content knowledge, pedagogical knowledge, and knowledge of students. Fernandez-Balboa and Stiehl [14] and Gess-Newsome [16] similarly emphasized context as an essential dimension for effective teaching, highlighting its influence on classroom practice and teacher development.

Subsequent models elaborated on the complexity and situatedness of context knowledge. Rollnick et al. [22], Davidowitz and Rollnick [24], and Mavhunga and Rollnick [27] integrated context as one of four knowledge domains that collectively produce PCK, with bidirectional interactions reflecting the reciprocal influence of context on teacher beliefs, pedagogical decisions, and content transformation. Otto and Everett [28] included context as one of three spheres in a Venn diagram, demonstrating its intersection with pedagogy and content in shaping classroom enactment of PCK.

Transformative perspectives, such as Carlsen [15], highlighted the dynamic relationship between context knowledge and PCK development: teachers adapt to contextual constraints while simultaneously shaping classroom environments through evolving instructional decisions. The Consensus Model reconceptualized context as an “amplifier and filter,” mediating how topic-specific professional knowledge translates into classroom practice while maintaining analytical distinction between knowledge and situational factors [5].

Across models, knowledge of context is recognized as a critical element enabling teachers to align content, pedagogy, and learner characteristics within real-world teaching environments, underscoring its central role in effective science instruction.

3.4. Content Knowledge

Content knowledge constitutes a foundational component of teachers’ professional knowledge. Within PCK research, content knowledge is understood as a complex multidimensional understanding of the structure, organization, and epistemology of scientific knowledge. The way content knowledge is conceptualized within PCK models is closely related to their underlying epistemological assumptions, particularly the distinction between integrative and transformative perspectives discussed in Section 2.2.

Early theoretical formulations emphasized that content knowledge comprises more than conceptual mastery. Tamir [11], drawing on Shulman’s work, distinguished between substantive knowledge, which includes scientific concepts, principles, laws, and theories, and syntactic or procedural knowledge, referring to understanding how scientific knowledge is generated and validated. This includes familiarity with scientific methods, experimental design, data interpretation, and theory justification. This distinction established that content knowledge encompasses both the products and the processes of science. From an epistemological standpoint, such a distinction supports integrative views of PCK, in which relatively stable knowledge domains retain identifiable characteristics while interacting in instructional contexts.

Several PCK models explicitly incorporate understanding of the nature of science within content knowledge. Carlsen [15] conceptualized content knowledge as encompassing declarative knowledge, procedural knowledge, and epistemological understanding of science as a dynamic and socially situated enterprise. Similarly, Lee and Luft [21] defined content knowledge to include not only core scientific concepts, but also knowledge of scientific practices, modes of inquiry, and epistemic norms. In these models, knowing science involves understanding science as a way of thinking, reasoning, and constructing explanations, rather than simply as a body of established facts. Such conceptualizations align with epistemological positions that treat content knowledge as a meaningful and explicitly articulated knowledge base, whether it is considered a constituent of PCK or a foundational domain informing its development.

Other frameworks emphasize the structural and organizational qualities of content knowledge. Rollnick et al. [22] and Davidowitz and Rollnick [24] describe content knowledge as a distinct knowledge domain characterized by conceptual coherence, depth, and internal consistency. From this perspective, content knowledge is evaluated not only in terms of accuracy, but also in terms of how well scientific ideas are hierarchically organized and interconnected. This emphasis on internal structure reflects an integrative epistemological orientation, in which content knowledge remains a recognizable component that interacts dynamically with pedagogical and contextual knowledge during teaching.

Several models stress the topic-specific nature of content knowledge, thereby supporting epistemological positions closer to the transformative end of the continuum. Hashweh [20] argues that content knowledge develops around particular teaching topics rather than as a generalized and context-free body of knowledge. In his framework, content knowledge interacts dynamically with teachers’ pedagogical constructions, suggesting that content knowledge acquires instructional relevance through its transformation into topic-specific pedagogical content. Similarly, the Consensus Model treats content knowledge as a foundational professional knowledge base that contributes to topic-specific professional knowledge (TSPK), rather than as a direct component of PCK itself. In this view, content knowledge becomes instructionally powerful only when reorganized and transformed in relation to specific learning goals and classroom contexts.

Across PCK models, there is strong agreement that content knowledge is multidimensional, integrating conceptual understanding, procedural competence, and epistemological awareness. Differences among models lie primarily in how content knowledge is positioned epistemologically—either as an identifiable component interacting with other knowledge bases (integrative model) or as foundational knowledge that is transformed into PCK through teaching practice (transformative model).

PCK scholarship converges on the view that content knowledge involves knowing scientific concepts, understanding how those concepts are generated and justified, and recognizing the epistemic characteristics of scientific knowledge. Whether treated as a component of PCK or as knowledge that is transformed into PCK, this rich and structured understanding of content forms the essential foundation upon which pedagogical reasoning and instructional decision-making are built.

3.5. Knowledge of Science Curriculum

Magnusson et al. [17] characteristically point out that knowledge of the science curriculum is what differentiates a content specialist from a pedagogue who teaches that content. This component of PCK refers to teachers’ understanding of the field of science education, the significance of specific teaching topics, and the aspects of scientific content that are considered essential for students to learn [11,25]. It also involves teachers’ knowledge of the organization of the taught content, the specifications for lesson design and instruction [11,25], as well as the resources and materials that will be used during teaching [22,25].

Furthermore, it encompasses teachers’ understanding of both the horizontal curriculum—that is, the learning objectives of all subjects taught within a specific grade level—and the vertical curriculum, which refers to what students have already been taught and what they are expected to learn subsequently [17,22]. An important element of curriculum knowledge is curriculum saliency, which relates to teachers’ ability to decide which topics to omit within a unit and how a particular topic fits into the overall curriculum structure [1,22]. This component is indicative of a teacher’s capacity to perceive the relative importance of specific content areas within the entire curriculum. It enables teachers to identify key concepts, adapt classroom activities, and remove unnecessary material from instruction. In this sense, it captures the essential difference between “covering the syllabus” and “teaching the syllabus for understanding” [1].

3.6. Pedagogical Knowledge

Pedagogical knowledge is commonly defined as knowledge about teaching and learning processes, instructional organization, and classroom practice [37]. Within PCK research, pedagogical knowledge is widely acknowledged as a critical component of teaching expertise, yet its scope, internal structure, and relationship to content remain variably conceptualized across models.

Across the literature, pedagogical knowledge is consistently portrayed as multidimensional. One core dimension concerns classroom management, including the organization of learning environments, time management, handling materials, and managing student behavior [18,29,38]. This dimension emphasizes the practical conditions that enable instruction to occur and learning to be sustained. A second central dimension involves instructional models and strategies. Pedagogical knowledge includes teachers’ understanding of major instructional approaches—such as direct instruction, inquiry-based learning, hands-on activities, demonstrations, and the use of visual or digital media [18,28]. A third recurring dimension is classroom communication, referring to discourse practices such as questioning techniques, discussion facilitation, feedback provision, and teacher–student interaction patterns [18,38]. This dimension highlights pedagogy as an interactive and dialogic process rather than a purely technical application of methods. Several models also emphasize teaching techniques, understood as specific instructional actions such as formulating questions, scaffolding activities, engaging students in hands-on work, or adjusting explanations in response to student input [29]. These techniques represent the operational level of pedagogical knowledge, where broader instructional principles are enacted in classroom practice.

While these dimensions appear with remarkable consistency, models differ in how pedagogical knowledge is delimited conceptually. Some treat pedagogical knowledge as general, applicable across subjects and independent of content, encompassing broad competencies such as classroom management and communication [11,15]. Others define pedagogical knowledge as content-integrated, arguing that instructional strategies, discourse practices, and teaching techniques acquire meaning only when applied to specific subject matter [12,22]. Some frameworks articulate pedagogical knowledge as context-sensitive. Rollnick et al. [22] define it as knowledge of applied learning theories concerning what constitutes good teaching and which instructional approaches are appropriate in particular contexts. Similarly, Morine-Dershimer and Kent [18] emphasize that pedagogical knowledge must always be adapted to classroom conditions, student characteristics, and instructional goals. More differentiated conceptualizations, such as Sothayapetch et al. [29], distinguish between general pedagogical knowledge (e.g., classroom management, communication, instructional models) and pedagogy within PCK, which includes content-specific teaching techniques such as science-focused questioning or hands-on activities.

Overall, across PCK models, pedagogical knowledge is consistently defined as encompassing classroom organization, instructional approaches, communication practices, and teaching techniques. Variations among models concern not the importance of pedagogical knowledge, but the degree to which it is treated as general, content-integrated, or differentiated into subtypes. Despite these differences, pedagogical knowledge is universally recognized as a foundational component enabling teachers to design learning environments, enact instruction, and support student understanding in systematic and purposeful ways.

3.7. Orientations to Teaching Science

Orientations to teaching science constitute a core component of PCK in science education. This component encompasses teachers’ knowledge and beliefs about the purposes and goals of teaching science across different grade levels within the same educational stage [17]. More specifically, it includes teachers’ conceptions regarding the purposes and goals of science teaching, the nature of science, and the nature of teaching and learning science for students [25]. Conceptually, this component has been approached either through an emphasis on instructional goals and purposes or through teachers’ broader views about teaching and learning science [39]. Park and Oliver [1] describe orientations to teaching science as a “conceptual map” that guides teachers’ instructional decisions.

Since Shulman’s initial formulation of PCK, orientations to teaching science have undergone substantial conceptual development [1,5,17,20,25,26]. In the model proposed by Magnusson et al. [17], orientations to teaching science is positioned as one of five interrelated PCK components. It functions as a guiding framework for instructional decisions concerning daily objectives, instructional materials, learning activities, and assessment practices. The authors emphasize that coherence among PCK components is essential; misalignment may hinder effective teaching and PCK development.

Hashweh’s [20] heptagonal model situates orientations to teaching science among seven components surrounding a central PCK core. In this framework, PCK is conceptualized as a collection of topic-specific pedagogical constructions, with orientations interacting dynamically with other components to form a coherent cognitive system. Similarly, Park and Oliver [1] expanded the Magnusson et al. [17] model by introducing teacher self-efficacy as a sixth component. Their hexagonal representation highlights reciprocal relationships among components, suggesting that PCK evolves through reflection and increasing coherence, with orientations playing a central directive role.

Further refinement is evident in Park and Chen’s [26] pentagonal model, which emphasizes the integrative nature of PCK. Their empirical findings demonstrate that orientations to teaching science guide instructional strategies and contribute to the overall quality of PCK, which depends both on component strength and inter-component coherence. The Consensus Model offers a different perspective, positioning orientations as one of several “amplifiers and filters” through which professional knowledge is transformed into personal, enacted PCK [5].

Across these models, orientations to teaching science emerge as a critical lens through which teachers interpret and enact their professional knowledge. Their consistent inclusion underscores the recognition that effective science teaching is shaped not only by content and pedagogy, but also by coherent beliefs and goals that guide instructional decision-making.

3.8. Knowledge of Assessment

Knowledge of assessment constitutes a critical component of PCK in science education. It is commonly defined as encompassing two interrelated dimensions: teachers’ knowledge of what aspects of science learning should be assessed and their knowledge of how these aspects can be assessed [11,17]. The assessed dimensions of science learning typically include understanding of scientific concepts and phenomena, aspects related to the nature of science, and inquiry-related skills such as data collection, analysis, and interpretation [17].

Within PCK research, assessment knowledge is not viewed merely as a set of technical procedures, but as a form of pedagogical reasoning closely connected to instructional goals, student understanding, and teaching strategies. This broader conceptualization explains its inclusion as a distinct component in several influential PCK models developed over the past decades. Among the PCK models, knowledge of assessment appears explicitly in a substantial number of frameworks [11,17,18,19,21].

Tamir [11] was among the first to conceptualize assessment knowledge as a component of subject-matter-specific pedagogical knowledge. In his framework, assessment knowledge is distinguished from assessment skills, emphasizing that teachers require both theoretical understanding of assessment tools and practical ability to apply them in authentic classroom contexts. This distinction highlighted assessment as an integral, experience-dependent aspect of teaching expertise.

In the model of Magnusson et al. [17], knowledge of assessment is positioned as one of five core PCK components. It is defined as teachers’ knowledge of the dimensions of science learning that are important to assess and of the methods by which student learning can be evaluated. The model emphasizes reciprocal interactions among all PCK components, arguing that assessment knowledge cannot develop independently from knowledge of students, instructional strategies, curriculum, and orientations to teaching science. Morine-Dershimer and Kent [18] further reinforced this interdependence by explicitly linking knowledge of assessment with knowledge of instructional goals. Their model suggests that assessment decisions are inherently goal-driven and play a mediating role between curricular intentions and instructional practice.

Veal and MaKinster [19] incorporated assessment knowledge into a hierarchical taxonomy of PCK, proposing that its development presupposes strong content knowledge and deep understanding of students. Similarly, Lee and Luft [21], based on empirical research, showed that assessment knowledge most frequently connects with instructional strategies and knowledge of students, forming part of a teacher-specific PCK core that evolves over time. Park and Oliver [1] extended earlier frameworks by adding teacher self-efficacy, while maintaining assessment knowledge as a central component that interacts dynamically with the others. Park and Chen [26] further demonstrated that although assessment knowledge often shows weaker overall connections, it plays a crucial role in coordinating instructional strategies and responsiveness to students. More recent conceptualizations, such as the Consensus Model, situate assessment knowledge within a broader professional knowledge base that informs classroom-enacted PCK [5]. Across these models, knowledge of assessment consistently emerges as a key element in transforming subject matter knowledge into meaningful learning opportunities.

Overall, the treatment of assessment knowledge across PCK models reflects a shared understanding that effective science teaching requires not only knowing what students should learn, but also how evidence of that learning can be elicited, interpreted, and used to guide instruction.

3.9. Teacher Self-Efficacy

The introduction of teacher self-efficacy reflects an expanded conceptualization of PCK that acknowledges the role of teachers’ beliefs about their capability to enact content-specific instructional practices. Within PCK research, self-efficacy is not treated as a conventional domain of professional knowledge, but as a construct that conditions the development, integration, and enactment of PCK in classroom contexts.

Teacher self-efficacy was explicitly incorporated into PCK model by Park and Oliver [1]. In contrast to traditional PCK components, which are predominantly cognitive, self-efficacy was conceptualized as an “attitude cluster”, thereby extending PCK beyond strictly knowledge-based domains. Crucially, it is defined as teachers’ confidence in teaching particular science topics rather than as a generalized sense of teaching competence. This focus aligns with the topic-specific orientation of PCK articulated in earlier models.

Across major PCK models, teacher self-efficacy appears explicitly in only one framework, namely that of Park and Oliver [1]. This limited inclusion underscores both the novelty of integrating affective constructs into PCK theory and the lack of consensus regarding their status as core PCK components. Most PCK models continue to prioritize professional knowledge domains and conceptualize beliefs as contextual or mediating influences rather than as constitutive elements of PCK.

In their hexagonal model, teacher self-efficacy is positioned alongside five established PCK components: orientations to teaching science, knowledge of students, curriculum knowledge, knowledge of instructional strategies, and knowledge of assessment. These components are interconnected through bidirectional relationships, highlighting the integrated and dynamic nature of PCK, while reflection is identified as the key mechanism supporting coherence and development. Within this framework, teacher self-efficacy is theorized to exert a pervasive influence on the enactment of other PCK components. Teachers with higher self-efficacy are more likely to implement diverse instructional strategies, engage with students’ ideas, and adapt curricular materials. Conversely, successful enactment of PCK may enhance self-efficacy, indicating a reciprocal relationship.

Although later frameworks, including the Consensus Model [5], do not treat self-efficacy as a PCK component, positioning beliefs instead as “amplifiers and filters,” Park and Oliver’s [1] model highlights the importance of teachers’ confidence in their capacity to enact content-specific instruction for effective science teaching.

4. PCK in the Generative Artificial Intelligence Era

Having identified key components of PCK in science education, we now consider how these components are drawn upon when teachers engage with GenAI tools such as ChatGPT in lesson planning. The advent of GenAI has introduced new opportunities and challenges for science education. GenAI, a subfield of AI, focuses on generating new content including text, images, audio, video, and code. As Chan and Colloton [40] (p. 9) note, “the term generative refers to AI’s ability to produce novel outputs rather than merely replicating, sorting, processing, or analyzing given inputs”. A growing body of literature has highlighted ways in which ChatGPT can be utilized within educational contexts. For educators, the tool offers support in instructional tasks, such as designing lesson plans, developing learning resources, and constructing assessments [41,42,43,44].

Within this evolving technological landscape in education, PCK extends to the framework of TPACK, which highlights the types of knowledge teachers need for the effective integration of technology into their teaching [7,45]. Technological Knowledge (TK) refers to teachers’ understanding of both standard and advanced technologies, including digital tools, and the skills required to operate them effectively. Technological Content Knowledge (TCK) concerns the reciprocal relationship between technology and subject matter, while Technological Pedagogical Knowledge (TPK) involves an understanding of the components, and affordances of various technologies as they are applied in teaching and learning contexts. Finally, TPCK represents an emergent and integrative form of knowledge that underpins effective teaching with technology. It involves understanding how technological tools can be used to transform pedagogical strategies and content in effective ways for learners, acknowledging how technology affects students’ understanding of particular topics [45,46].

In the age of GenAI, the TPACK framework should be reimagined to support the effective integration of GenAI tools in teaching and learning [7]. This entails understanding the nature of GenAI and using this knowledge to explain how these technologies can enhance pedagogical processes [7]. Consequently, the technological dimension of TPACK should be enriched with AI-related competencies that enable teachers to engage critically and creatively with such tools. A primary example widely discussed in the recent literature is prompt engineering [46], which refers to “the processes and techniques for composing input to produce GenAI output that more closely resembles the user’s desired intent” [47] (p. 11). Effective strategies include instructing the model to respond from a specific persona or perspective, providing clear and detailed instructions, and using layer prompts, also referred to as chain of thought prompting, where step-by-step instructions guide the model’s reasoning. In addition, providing reference texts, such as scientific papers, to the GenAI tool may enhance the relevance and accuracy of the generated responses [48,49,50]. However, these approaches do not guarantee accuracy; therefore, human evaluation and critical oversight remain essential.

In this regard, when interacting with GenAI tools, teachers not only formulate prompts; they also evaluate the quality of the generated outputs in terms of scientific accuracy and pedagogical appropriateness [46]. In this process, teachers’ PCK is essential for the effective and responsible use of GenAI tools in science education [43,44,46]. However, research examining lesson planning with GenAI as a co-designer through the lens of PCK remains in its infancy [43,44]. Building on this emerging line of inquiry, recent studies have begun to illustrate how teachers engage with GenAI tools, employing PCK as a robust analytical lens to understand the complex interplay between human expertise and machine output.

Feldman-Maggor et al. [46], illustrated this process through examples of teacher–AI dialogues that demonstrate how teachers apply their PCK to evaluate GenAI-generated outputs. For instance, when teaching the differences between molecular and ionic materials, a chemistry teacher engaged in an iterative dialogue with ChatGPT (GPT-3.5). The teacher identified specific misconceptions that the model failed to address and subsequently formulated refined prompts to enhance both the accuracy and pedagogical relevance of the responses. Furthermore, the teacher detected inaccuracies in the chemical notation produced by ChatGPT, correcting them before using the material in the classroom. This case highlights how teachers’ PCK serves as a robust framework for critically evaluating and refining AI-generated educational content [46].

Peikos and Stavrou [44], in an exploratory study, investigated ChatGPT-assisted lesson planning in primary science education through the lens of PCK. In this study, PCK served a dual purpose: it guided the design of prompts used to interact with ChatGPT (GPT-4o) and provided the analytical framework for evaluating the generated outputs. Four interactions with ChatGPT were conducted. The first two were based on single prompts differing in the PCK components included, whereas the third and fourth employed layer prompts, in which step-by-step instructions were aligned with specific PCK components. In the final interaction, a scientific paper was provided as a reference source to guide the model’s responses. Findings revealed that layer prompts, incorporating specific PCK aspects together with the provision of reference texts related to the teaching topic, strengthened the PCK characteristics of ChatGPT’s outputs. This approach enhanced the integration of pedagogy with content by aligning appropriate teaching strategies with subject matter. Consequently, the model’s focus shifted from primarily quantitative explanations to qualitative ones that were more suitable for primary school students, particularly in lesson plans addressing the concepts of density and floating/sinking. Likewise, examining the integration of content with context (that is, capturing students’ conceptions of the topic) as well as the integration of pedagogy with context (that is, using approaches to reach all students in the classroom) showed that interactions incorporating explicit PCK elements, step-by-step guidance, and reference texts elicited more appropriate activities that could reveal and address students’ misconceptions.

Based on these findings, the researchers proposed guidelines for teacher–GenAI co-design of lesson plans grounded in PCK, where educators guide ChatGPT by crafting PCK-informed prompts and refining them iteratively. In this framework, the teacher formulates prompts that integrate PCK components and employs layer prompts to provide clear, step-wise instructions. Supplying the model with reference texts that represent collective PCK, such as relevant scientific articles, further supports the generation of accurate outputs. ChatGPT then produces an initial output—for example, for a specific phase of an inquiry teaching model—which the teacher evaluates through their PCK. The prompts are subsequently revised to address specific PCK components, such as students’ misconceptions or grade-level alignment. Finally, the teacher selects and adapts activities to suit the specific classroom context, resulting in the final lesson plan [44].

5. Conclusions and Prospects

PCK remains one of the most influential constructs in science education for conceptualizing what it means to teach science effectively. Since its introduction by Shulman [2,3], PCK has evolved through a diverse landscape of PCK models that reflect different epistemological positions as well as components of teacher knowledge. This paper traced the historical development of PCK models, highlighted the central epistemological debate between integrative and transformative perspectives, and synthesized the core components that recur across PCK models in science education. Despite differences in terminology and theoretical orientation, there is agreement regarding the centrality of knowledge of students’ understanding, instructional strategies, context, content, curriculum, pedagogy, orientations to teaching science, and assessment as key dimensions of effective science teaching.

The Refined Consensus Model of PCK [6] constitutes a significant milestone by conceptualizing PCK as a dynamic, multi-layered system operating across collective, personal, and enacted realms. This model moves the field beyond static views of teacher knowledge and foregrounds the recursive processes through which teachers plan, teach, reflect, and subsequently reshape their professional knowledge. At the same time, earlier models—particularly those proposed by Magnusson et al. [17] and subsequent refinements—continue to offer analytically robust lenses for unpacking the topic-specific structure of PCK and remain valuable for both research and teacher education.

In the contemporary educational landscape, the emergence of GenAI introduces both challenges and opportunities for science education. As illustrated in this paper, GenAI tools such as ChatGPT are increasingly involved in instructional design processes, particularly by supporting teachers in lesson planning. However, GenAI outputs are closely dependent on both their inherent limitations and the inputs provided by teachers and may not always be accurate or pedagogically appropriate for classroom implementation. Drawing on the Refined Consensus Model of PCK [6], it seems that when teachers use GenAI tools for instructional design, an activity situated within the realm of enacted PCK, they are required to design and reflect on lesson plans based on their personal PCK. This process is influenced by collective PCK and by the specific learning context. For example, research-based literature on teaching and learning specific science concepts may be provided to GenAI tools as reference material to inform lesson plan suggestions and help reduce inaccuracies. At the same time, teachers, drawing on their personal PCK, retain responsibility for critically examining GenAI-generated content and activities, evaluating their suitability for the intended learning context, and ensuring their pedagogical appropriateness in classroom enactment.

Beyond the specific focus on GenAI, within a broader research landscape, a symposium entitled “Re-considering the role and relevance of PCK within the challenges of 21st century science education”, held at the European Science Education Research Association (ESERA) Conference in Copenhagen in 2025, and chaired by Amanda Berry with Janet Carlson as discussant, highlighted emerging research directions. These included the implications of inter- and transdisciplinary science education for teacher’s PCK [8], pre-service teachers’ enacted PCK in the context of socioscientific issues [51], teachers’ adaptive expertise examined through a PCK framework [52], and systematic efforts to identify indicators of high-quality PCK [9]. In conclusion, research on PCK continues to evolve, maintaining its relevance as a conceptual framework for addressing contemporary challenges in science education.