A practical framework for selecting educational robotics platforms to foster skills in higher education

Abstract

Educational robotics (ER) has great potential to develop both computational thinking (CT) and 21st-century skills in higher education (HE). However, structured frameworks that help educators align platform selection with desired learning outcomes are still lacking. This study presents a competency-oriented framework that organizes ER platforms along a functional continuum based on the dominant mode of student interaction. Rather than applying rigid classifications, the framework places platforms on a spectrum ranging from screen-based programming environments to systems in which behavioral logic is integrated into the physical morphology of the robot. Based on the analysis of thirty-two empirical studies, the framework connects interaction modalities with groups of CT dimensions and cross-cutting skills, such as creativity, collaboration, adaptability, leadership, and ethical reasoning. CT is conceptualized as a scaffold that mediates the development of these skills through embedded interaction with robotic systems. The resulting structure offers a practical tool for curriculum planning, platform selection, and instructional design in university settings. By mapping how different types of ER platforms support specific learning processes, the framework contributes to a more intentional and evidence-based integration of robotics in HE, aligned with educational goals and workforce demands across disciplines.