Science education researchers have developed a refined understanding of the structure of science teachers’ pedagogical content knowledge (PCK), but how to develop applicable and situation-adequate PCK remains largely unclear. A potential problem lies in the diverse conceptualisations of the PCK used in PCK research. This study sought to systematize existing science education research on PCK through the lens of the recently proposed refined consensus model (RCM) of PCK. In this review, the studies’ approaches to investigating PCK and selected findings were characterised and synthesised as an overview comparing research before and after the publication of the RCM. We found that the studies largely employed a qualitative case-study methodology that included specific PCK models and tools. However, in recent years, the studies focused increasingly on quantitative aspects. Furthermore, results of the reviewed studies can mostly be integrated into the RCM. We argue that the RCM can function as a meaningful theoretical lens for conceptualizing links between teaching practice and PCK development by proposing pedagogical reasoning as a mechanism and/or explanation for PCK development in the context of teaching practice.

Reflection is hypothesized to be a key component for teachers’ professional development and is often assessed and facilitated through written reflections in university-based teacher education. Empirical research shows that reflection-related competencies are domain-dependent and multi-faceted. However, assessing reflections is complex. Given this complexity, novel methodological tools such as non-linear, algorithmic models can help explore unseen relationships and better determine quality correlates for written reflections. Consequently, this study utilized machine learning methods to explore quality correlates for written reflections in physics on a standardized teaching situation. N = 110 pre- and in-service physics teachers were instructed to reflect upon a standardized teaching situation in physics displayed in a video vignette. The teachers’ written reflections were analyzed with a machine learning model which classified sentences in the written reflections according to elements in a reflection-supporting model. A quality indicator called level of structure (LOS) was devised and further used to validate machine learning classifications against experts’ judgements. Analyses show that LOS is positively correlated with experts’ judgements on reflection quality. We conclude that LOS of a written reflection is one important indicator for high-quality written reflections which is able to exclude typical quality correlates such as text length. With the help of the machine learning model, LOS can be useful to assess pre-service physics teachers written reflections.

Important prerequisites for effective teaching in science, technology, engineering, and mathematics (STEM) are outlined in the refined consensus model of pedagogical content knowledge: Teachers need to become able to apply their pedagogical content knowledge in practice, called enacted pedagogical content knowledge. To support pre-service STEM teachers to develop enacted pedagogical content knowledge, scholars in STEM fields need to implement opportunities of lesson planning, teaching, and reflecting upon teaching, and provide assessments and guidance for professional development. This theoretical article reviews advances in artificial intelligence-based methods in reference to applications of these methods in pre-service STEM teacher education with a focus on the refined consensus model for pedagogical content knowledge. A number of selected studies provides valuable insights into opportunities and challenges of applying AI in STEM teacher education. We found that AI technologies, based on their differing degrees of sophistication, provide different affordances with regards to research and professional development. AI technologies already prevail STEM teacher education and STEM learning more generally. To enhance STEM teacher education with AI technologies, affordances of the technologies with reference to potentials for automation and feedback have to be recognized. The refined consensus model provides a valuable lens to conceptualize these affordances.

Reflective writing is a core component of teacher education, especially during practical internships. However, providing high-quality feedback on reflections is resource-intensive. This study examines descriptively observable associations between an early dual-feedback approach combining basic (automated) and elaborate (human-generated) feedback and structural features of preservice physics teachers’ reflective writing, prior to the widespread adoption of generative AI in education. Using an exploratory, non-equivalent, non-concurrent cohort design, we analyzed participant-level aggregates of written reflections from a non-intervention cohort (N = 22) and an intervention cohort (N = 32), applying a validated reflection-supporting model to assess structural composition and discursive elements of reflective writing. In the intervention, basic feedback was generated by a previously validated BERT-based machine learning model focusing on structural reflection elements, while elaborate feedback addressed content-related and pedagogical depth. In this study, the automated model was employed as an analytic measurement instrument drawing on validation work demonstrating its transferability across comparable reflection contexts. Quantitative analyses did not reveal systematic longitudinal growth in indicators of reflective writing quality in either cohort. Across comparable measurement points, descriptively different structural reflection profiles were observed between cohorts, without permitting causal or developmental interpretations. Feedback acceptance was high overall, although structural AI feedback was perceived as less personalized and less useful. These findings highlight the descriptive value of early, non-generative AI-based approaches for scalable structural diagnostics of reflective writing, while underscoring the continued importance of human-generated, content-focused feedback. The study establishes an empirical baseline for evaluating contemporary generative AI–based feedback systems in teacher education.

Important prerequisites for effective teaching in science, technology, engineering, and mathematics (STEM) are outlined in the refined consensus model of pedagogical content knowledge: Teachers need to become able to apply their pedagogical content knowledge in practice, called enacted pedagogical content knowledge. To support pre-service STEM teachers to develop enacted pedagogical content knowledge, scholars in STEM fields need to implement opportunities of lesson planning, teaching, and reflecting upon teaching, and provide assessments and guidance for professional development. This theoretical article reviews advances in artificial intelligence-based methods in reference to applications of these methods in pre-service STEM teacher education with a focus on the refined consensus model for pedagogical content knowledge. A number of selected studies provides valuable insights into opportunities and challenges of applying AI in STEM teacher education. We found that AI technologies, based on their differing degrees of sophistication, provide different affordances with regards to research and professional development. AI technologies already prevail STEM teacher education and STEM learning more generally. To enhance STEM teacher education with AI technologies, affordances of the technologies with reference to potentials for automation and feedback have to be recognized. The refined consensus model provides a valuable lens to conceptualize these affordances.

Für die fachgerechte Planung, Durchführung oder Reflexion von Physikunterricht ist eine solide Wissensbasis unerlässlich. Wichtiger als die einzelnen Wissensfacetten ist jedoch deren kohärente Vernetzung. Kohärenzbildung kann durch Dozierende unterstützt werden, gleichzeitig obliegt die eigentliche Wissensvernetzung den Studierenden selbst. Die Methode der Content Representation (CoRe; tabellarische Darstellung von kontextspezifischem Wissen) kann die Ausprägung sowie den Grad der Vernetzung von im Studium entwickelten Wissensfacetten abbilden und zur Evaluation von Kohärenzbildung verwendet werden. Umgekehrt bietet die Methode Potential zur Kohärenzbildung selbst, welches als neue Sichtweise im Beitrag diskutiert wird. (DIPF/Orig.)