Fire investigation training programs aim to equip investigators with the skills to identify fire origins and causes, but the chaotic nature of post-fire scenes presents substantial challenges. Investigators must connect evidence and scene features to the fire dynamics that shaped a scene, which requires strong spatial-temporal reasoning skills. Immersive training in realistic environments is essential to help investigators piece together evidence, analyze fire progression, and accurately trace fire origins. However, most training programs in the U.S. rely on lectures and 2D visuals, lacking the immersive experience needed to develop these crucial reasoning skills. Further, many investigators lack formal education in fire science, which is essential for understanding fire behavior. This project seeks to create a multimodal embodied training platform that advances fire investigation training through adaptive deliberate practice and learning analytics, focusing on the spatial-temporal reasoning skills needed to reconstruct fire development from observed fire damage and scene features. This new training approach will improve the quality and effectiveness of fire investigation practices, benefiting public safety by enabling more accurate identification of fire origins and causes. Many of the ideas can be extended to related fields such as crime scene investigation and other STEM areas requiring advanced spatial-temporal reasoning skills. To achieve these goals, the training pl