Designing a Layered Learning Infrastructure with a Digital Twin Component

The increasing availability and complexity of data across professional domains require decision-makers to integrate diverse information in uncertain situations, highlighting the role of professional education in developing data-informed decision-making competence. While digital twins are widely used to model and optimise complex systems, their potential as pedagogically structured learning environments remains underexplored. This study presents the design of a layered learning infrastructure for data-informed decision-making, in which a digital twin functions as a component within a simulation-based learning environment. The study proposes a reconceptualisation of digital twins as components of learning processes. Drawing on multi-professional co-creation process in a smart agriculture context, the study suggests that pedagogical orchestration makes learners’ reasoning visible by supporting multi-source data integration, trade-off evaluation, and engagement with uncertainty. The paper contributes a design-oriented framework and argues that the educational value of digital twins depends on pedagogical orchestration that renders decision-making processes explicit and open to reflection.