Pedagogical Approach
Our project builds on the idea that meaningful professional development happens through active inquiry, reflection, and experimentation.
Our blended training course combines online and face-to-face learning, grounded in evidence-informed pedagogy and human-centred AI principles.
At its core, the course uses pure Question-Based Learning (pQBL) – an innovative, evidence-based methodology that transforms learning into a process of guided questioning and feedback. This approach allows participants to learn by reasoning, not by memorizing.
What is pQBL?
pure Question-Based Learning (pQBL) is a research-based methodology developed and refined by KTH Royal Institute of Technology. It replaces traditional lectures and passive learning with carefully designed sequences of questions that lead learners through a structured discovery process.
Each question is followed by immediate, constructive feedback, helping participants identify misconceptions, refine their understanding, and connect new insights to real teaching contexts.
In our project, pQBL is not only a teaching strategy, but it is also used as a learning analytics framework, enabling continuous monitoring of how teachers interact with the material, reflect on their learning, and apply AI in their own practice.
How We Apply It
In the Online Modules
Participants engage with pQBL-driven activities that introduce them to AI concepts, ethics, and educational applications. Log data from the platform (e.g. percentage of correct answers, attempts, and learning progress) provide insight into learning quality and support iterative course improvement.
In the Blended Course
pQBL is combined with flipped learning, peer learning, and video reflection. Teachers design small-scale AI experiments, pilot them in their classrooms, and share outcomes with peers through the project’s digital platform.
In Quality Monitoring
The pQBL structure underpins the project’s Quality Monitoring and Analysis Plan (QMAP). Pre- and post-self-reflections on participants’ AI preparedness and feedback surveys provide both qualitative and quantitative data to measure progress and impact.