Welcome to CoPLA: New research group launches at the IPN

The IPN is delighted to announce that it has a new research group. CoPLA (Collaborative Problem-Solving in Teacher Education) commenced its work on May 1, 2025, with Dr. Anika Radkowitsch as lead researcher. The group, which has received funding for four years, is centering its work on a key issue in teacher education: how to provide student teachers with targeted support to gain skills in planning collaborative teaching.

Collaboration among teachers can help align the education delivered in classrooms with the real world students encounter and focus it on skills. CoPLA’s aim is to conduct research on collaborative problem-solving in a specific context, the planning of cross-curricular teaching with an emphasis on biology and math. The group conceives of collaborative teaching as being about more than just splitting tasks; the intent of its work is to promote collaborative processes that give rise to new knowledge as teachers work on problems together. Anika Radkowitsch, CoPLA’s lead researcher, who has been at the IPN since 2021, explains: “Collaborative problem-solving can be quite a challenge, but it also has potential above and beyond other methods. For example, it can help teachers to pool resources and bring a variety of points of view to their lesson planning.”

Another focus of CoPLA’s research is on the use of AI in this context; the group will be using agent-based simulations in which student teachers will work with AI-based agents to plan lessons. The aim of this work is to identify the conditions under which learning via these AI agents is effective and evaluate the efficacy of the scaffolds the agent provides. In this regard, CoPLA’s research builds on the IPN’s Kopilot project, which has previously created a simulation alongside teaching and learning materials.

The group will work closely with Prof. Dr. Daniel Sommerhof, vice-director of the IPN’s Department of Mathematics Education, and with Prof. Dr. Michael Sailer of the University of Augsburg, whose principal research interests include teacher education relating to technology and AI in education.

We caught up with Anika Radkowitsch to talk about her scientific career to date, the status of collaboration in teacher training, and her plans for the new research group:

IPN: How did you discover collaborative problem-solving in teacher training as a research topic, and what prompted you to set up your own junior research group to study it?

Dr. Anika Radkowitsch: Ever since my time as a doctoral researcher, I’ve been thinking about how people training for their job or profession learn how to work with others to solve problems. My doctoral research centered on collaboration among medical doctors; I studied ways for medical students to learn practices of collaborative diagnosis. Since joining the IPN, I’ve primarily been working on student teachers’ learning around solving problems relating to the assessment of students’ learning. For instance, I looked at the way preservice mathematics teachers proceed when evaluating how well a student has understood specific mathematical content and the role of their prior knowledge and motivation in this evaluation process. So how people learn to collaborate and how teachers develop their professional competencies are issues that I’ve been tackling in my research for some time now. The new research group has given me the opportunity to bring these two strands of my work together, contributing new topics and perspectives on education to the IPN in the process, and to head up a research group of my own.

IPN: Why is collaborative problem-solving a skill of particular relevance to teachers?

Dr. Anika Radkowitsch: Collaborative problem-solving means teachers working together toward a shared objective, which they will only achieve if each of them relates the knowledge they hold to the knowledge held by the others. This process, as it develops new solutions for achieving the objective, can often give rise to new knowledge. One example, that we’re studying in CoPLA, is planning and delivering cross-curricular teaching. So working together in collaborative problem-solving goes beyond the “normal,” typical ways of working together, like sharing learning resources or CPD material or splitting tasks. Collaborative problem-solving can be quite a challenge, but it also has potential above and beyond other methods. For example, it can help teachers to pool resources and bring a variety of points of view on students and on curricular content to their lesson planning and delivery. On top of this, working with others enables teachers to access feedback and points for reflection on their teaching, which gives their professional competencies a boost. All in all, good collaborative problem-solving skills can help make life easier for individual teachers and improve the quality of teaching.

IPN: The research group’s focus is on the planning and revision of cross-curricular teaching in the subjects of biology and math. Why these two subjects in particular?

Dr. Anika Radkowitsch: I’m focusing on these two subjects because I consider them a really interesting combination and think that linking them could generate long-term improvements to students’ knowledge acquisition and motivation. To me, the particularly interesting thing about this subject combination is that, at first glance, math and biology don’t really seem to have much in common. But if we take a closer look, we see a number of aspects in which they relate to each other. Understanding biology as a scientific discipline often calls for mathematical skills, which are needed, for example, for interpreting diagrams or statistical analyses of biology experiments. Alongside this, biology provides a context for the application of mathematical concepts that relates directly to real life; it helps contextualize abstract mathematical learning in a setting that we encounter in our day-to-day lifeworlds. An example here might be pandemics. We can look at them both from a biology perspective – what are viruses and how do they spread? How do viruses affect the human body? - and from a mathematical angle – how can we model the spread of viruses and what are the limitations of mathematical models in this regard? Bringing these two points of view together can make students’ classroom experience both more tangible and more relevant to their day-to-day lives.

IPN: Part of your work involves agent-based collaborative problem-solving with AI. Could you give us a brief explanation of the principle behind this? How can AI agents be of benefit when training collaborative skills?

Dr. Anika Radkowitsch: Agent-based collaborative problem-solving in our project involves student teachers working in simulations with simulated individuals based on artificial intelligence (AI). These individuals – AI agents – can respond flexibly to free-text messages and, unlike the conventional computer simulations that have been available up to now, enable a greater degree of authenticity in interactions. Using these means we can provide standardized opportunities to practice skills with relatively few resources; we have student math teachers working with an AI biology teacher in a simulation to plan interdisciplinary teaching. This AI “teacher” can give support tailored to the learners’ needs, by, for instance, structuring processes of collaboration or boosting their motivation. The idea behind this supported planning of cross-curricular teaching is to help student teachers learn to solve problems collaboratively.

IPN: What are the specific research questions your junior research group is studying?

Dr. Anika Radkowitsch: Our research focuses on two key areas. First, we’re looking at the development of agent-based simulations for educational research. We’re conducting a systematic review of existing research so we can discover the contexts and purposes in and for which research into agent-based collaborative problem-solving is happening, and find out how AI agents differ from one another. Further, we want to identify conditions under which individualized support for collaborative problem-solving is effective. In particular, we want to consider whether, and to what extent, it’s possible to use AI agents to boost motivation and to actually promote collaborative processes among student teachers in line with their characteristics.

IPN: How might your research go toward making concrete improvements to teacher training going forward?

Dr. Anika Radkowitsch: Our aim is to create systematic training resources for collaborative problem-solving by integrating AI-based simulations into student teachers’ learning – resources that currently don’t exist in this form, or only on a very isolated basis. We think that these simulations would give trainee teachers the opportunity to develop their collaborative skills in a safe space. We intend the knowledge generated by our research to enter directly into the practical training of teachers; we’ll develop a simulation on the basis of our findings and make it available to students. We hope in this way to do our part toward helping tomorrow’s teachers to be better prepared for the challenges they will face in modern schools.