An acute challenge within all modern institutional education and training environments is the adoption of well-established learning science that shows humans learn more effectively by actively constructing declarative, procedural and tacit knowledge needed for academic and occupational goals.  Research has repeatedly reproduced data outcomes showing how active experiential learning is more effective than the current didactic, lecture-based lessons and written assessment, that the majority of institutional courses employ, despite its proven lower quality of learning.   Based on constructivism and Hebbian theories, the premise is that knowledge learning through multiple active experiences, associated with realistic contexts, is more effectively encoded in memory, leading to significantly better retention and transfer later in real situations.  Today most military training courses still employ traditional didactic instructional models, we believe mainly because there are no other alternative models available.  This paper will document a learning engineering use-case for the application of what is called a competency-based experiential learning (CBEL) instructional model within a US Army institutional course of instruction.  The model is intended to offer an alternative to traditional instructional models, and is based on the latest US Army’s Data Strategy and Learning Concept for 2030-2040.  Primary goals of this research is to employ synthetic content and adaptive instructional systems to increase tacit knowledge along with early skill acquisition.  This research will also endeavor to apply, evaluate and inform an institutional application of portions of the US Army’s Synthetic Training Environment (STE) program. This paper will document and discuss the challenges, and the learning engineering process to design methods to collect and analyze data, design learning science-based strategies, using adaptive instructional tools and technologies, with novel CBEL based content, and implement and assess its long-term impact on a random population of students with follow-on data analysis.