A survey on the recent discourse in competence research on mathematics teachers and its links to expertise research

In the recent discourse on the theoretical foundation of the teaching profession, several different paradigmatic approaches have emerged. The competence-oriented approach to teacher professionalism has strongly influenced the discourse through its links with extensive large-scale studies (amongst other the Teacher Education and Development Study (TEDS-M Study)). The various theoretical frameworks developed within this discourse refer on the one hand to Shulman's approach to pedagogical content knowledge and thus differentiate between various knowledge-related domains. On the other hand, cognitive as well as affective and volitional aspects are considered. The discourse has evolved from an initially dominant cognitive perspective with a focus on teachers' knowledge to the inclusion of a situation-specific perspective mainly conceptualized as teachers’ professional noticing. In the paper theoretical and empirical results from the TEDS Research program will be used to illustrate the development of the aforementioned discourse.

Finally, perspectives for the further development of the discourse will be discussed taking up the performance-oriented expertise discourse, which has so far been largely limited to well-structured domains and which may broaden our understanding of the teaching profession.

From Assessment to Inquiry: Exploring the Potential of AI in Science Education

The rapid advancement of Artificial Intelligence (AI) is transforming the landscape of science education, creating new opportunities and challenges for teaching, learning, assessment, and educational research. Increasingly, studies have explored how AI can be used appropriately and effectively to support science learning, teaching, and research. In this presentation, I will share three cases that illustrate both the potential and limitations of AI in science education, highlighting what AI can and cannot do in educational research and classroom practice.

The first case examines the use of machine learning for automated scoring of open-ended science assessment responses. Data were collected from 896 students in Grades 10–12 in northern Taiwan who had previously studied chemistry in Grades 8 and 9. The findings indicate that machine-learning-based automated scoring can be applied not only to English-language responses but also to responses written in Chinese, achieving acceptable levels of reliability and validity. These results demonstrate the potential of AI-assisted assessment to support large-scale educational evaluation.

The second case focuses on chemistry textbook analysis. Textbook analysis has traditionally been time-consuming and labor-intensive, despite the central role textbooks play in guiding both teaching and learning. Using Latent Dirichlet Allocation (LDA), a machine learning technique for topic modeling, we analyzed six Taiwan high school chemistry textbooks and identified seven major content themes. The analysis revealed different patterns of topic progression and interconnections among key concepts, providing insights into curriculum coherence, content alignment, and instructional planning.

The third case proposes an inquiry-based framework for integrating generative AI into science inquiry activities. The framework illustrates how generative AI can support students' questioning, evidence evaluation, explanation construction, and reflection while also highlighting the importance of critical engagement with AI-generated information.

Drawing upon these three cases, I will discuss the competencies that educators and researchers need to integrate AI into science education effectively. I will conclude by proposing guidelines and recommendations for the integration of AI in chemistry education, with the goals of deepening students' epistemic understanding of disciplinary knowledge, fostering interdisciplinary connections, and promoting the application of knowledge in authentic contexts—key dimensions of meaningful learning in an increasingly AI-driven world.

Keywords

Artificial Intelligence, Machine Learning, LDA, automated scoring, textbook analysis, inquiry

Smartphone Experiments with phyphox: From Technology to Teaching Practice

Smartphones have evolved into powerful, sensor-rich devices that provide unprecedented opportunities for experimental science education. Equipped with a vast selection of sensors, they enable a wide range of experiments using modern data-acquisition techniques without requiring specialized laboratory equipment. The free and open source app phyphox (physical phone experiments) has been specifically developed at the RWTH Aachen University to transform these sensors into versatile scientific instruments, allowing learners to collect, analyze, and visualize data in real time while engaging with authentic scientific inquiry.

This talk presents the technological possibilities for using smartphones as measuring devices and gives examples of a wide range of educational settings as well as different concepts for how smartphone experiments can be used.

The technological possibilities are facilitated by the breadth of sensors available in modern smartphones. In addition to the commonly used motion and orientation sensors, the talk explores the potential of microphones, cameras, and other integrated components, demonstrating how familiar everyday devices can serve as accessible yet capable scientific instruments. Furthermore, phyphox allows sensors to be combined and enables customization of the user interface as well as experiment-specific data analysis and incorporation of external sensors via Bluetooth such as self-built Arduino-based devices.

Beyond showcasing experimental examples, the presentation discusses several educational settings for incorporating smartphone experiments into teaching, ranging from secondary school classrooms and university teaching to laboratory courses and informal learning environments. Each of these offers different concepts for incorporating smartphones into experimental tasks, from individual investigative learning to real-time collaborative experiments for large audiences. Smartphone experiments can take the form of short experimental assignments, project-based learning formats in which students design and conduct their own investigations, and large-scale live experiments that combine measurements from many participants to generate rich datasets for immediate analysis – or even a planet-wide experiment conducted by users around the globe.