The case started with an observation study of how teachers work with the dashboard-based AI's suggestions for learning goals and student groupings, mapping the task with Hierarchical Task Analysis and identifying the new skill and knowledge demands that emerge when teachers evaluate, adjust, or override algorithmic recommendations. Building on these insights, the case now moves into a longitudinal field study examining how the introduction of an agentic chatbot reshapes this collaboration: on which subtasks teachers actually deploy the chatbot, how usage patterns and verification behaviour evolve, whether AI reduces workload or introduces new skill demands (such as prompt formulation, trust calibration, and critical appraisal of AI output), and how these shifts relate to teacher characteristics like AI literacy and self-efficacy. The case will produce practical guidelines for AI design and teacher support, grounded in real collaboration through peer mentoring and co-design between teachers, researchers, and an industry partner, feeding into the cross-case TAICo guidelines for developers, school leaders, and policymakers.
The tool directly supports differentiation, one of the most challenging and essential tasks in primary education. It enables evidence-based grouping and lesson-objective decisions, allowing teachers to adjust instruction to students' individual needs while keeping interpretive and decisional authority firmly with the teacher. Although the tool can reduce workload on routine data analyses, it also supports the development of new skills in interpreting student data, evaluating algorithmic suggestions, and, with the introduction of the chatbot, formulating effective prompts and critically appraising AI output. Through the co-implementation cycles and peer improvement sessions (structured discussion sessions between teachers and the industry partner in which workflows, concerns, and design suggestions are shared directly), teachers are not only users of the tool but active partners in shaping how it is designed and how professional learning around it is organised, fostering professional growth and reflective teaching practice.
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.
