From simulation to flight: Simulation-assisted drone learning with teacher-AI co-designed scaffolds for secondary students’ STEM knowledge and competencies

A persistent challenge in drone-based STEM education is the scarcity of teacher-verified, interactive simulations that effectively support both conceptual learning and competency development. While Generative AI offers promising avenues for accelerating content creation, its outputs often lack pedagogical validity and contextual relevance. This study examines whether drone instruction supported by teacher-AI co-designed simulations, yields superior learning outcomes compared to the same hands-on drone curriculum delivered without simulations. Employing a quasi-experimental pretest–posttest design with 30 secondary students (aged 13 - 17), both groups completed identical drone tasks under the same instructor, while the experimental group additionally engaged with five interactive simulations. Quantitative analyses revealed significantly greater improvements in the experimental group for both STEM content knowledge and overall learning competencies, including critical thinking, collaboration, communication, and creativity. Complementary qualitative data from interviews and observations revealed that simulations offered a low-stakes environment that reduced cognitive load, rendered causal flight mechanisms more visible, supported hypothesis testing, and facilitated transfer to physical drone operation. These findings suggest that simulations can serve as effective pre-flight, formative scaffolds that enhance learning visibility for teachers.The teacher–AI co-design model appears promising and potentially scalable for developing validated educational resources, although the results are constrained by the modest sample size and the quasi-experimental research design.