The case study provides insights into how teachers actually use the adaptive features of the ITS compared to their intended use. It identifies which teaching skills are supported by the tool and examines whether AI helps reduce teacher workload or introduces new skill requirements. The study also produces practical guidelines for AI design and teacher support, drawing on real collaboration through peer mentoring and co-design between teachers, researchers, and an industry partner.
The ITS directly supports differentiation, helping teachers address one of the most challenging and essential tasks in primary education. It enables evidence-based grouping decisions and allows teachers to adjust instruction according to students’ individual needs. While the tool can potentially reduce workload, it also supports the development of new skills in interpreting data and making informed instructional decisions, fostering professional growth and reflective teaching practices.
Curriculum adaptivity addresses the growing diversity in classrooms, allowing teachers to differentiate learning paths and tailor instruction to individual student needs. Rather than delivering the same sequence of lessons to every class, teachers can adjust content and pacing based on the unique needs of their students. This approach is crucial for ensuring that all learners receive effective support, particularly in heterogeneous classrooms where differences in skills and learning styles are significant.
Addresses several key challenges. First, teachers often do not fully utilize the actionable information available from dashboards to inform and plan student learning. Practically, engaging teachers in research collaboration can be difficult due to their heavy workload. Finally, ensuring that high-quality educational software reaches schools is complicated by financial constraints, such as the cost of licenses.
