Abstract:

The Department of Defense (DoD) acquisition workforce plays a critical role in assessing, procuring, and implementing emerging technologies to enhance mission readiness. However, a gap exists in evaluating the “learning maturity” of instructional technologies acquired through science and technology (S&T) investments. Unlike Technology Readiness Levels (TRLs) and Human Readiness Levels (HRLs), which guide acquisition professionals in determining product functional maturity, there is no standardized framework to ensure learning technologies are grounded in learning science with empirical learning efficacy and efficiency, to meet evolving training needs, and integrate within the broader DoD data strategy. This also applies to inwardly facing operations at DAU and their efforts in mission readiness and acquisition workforce preparation.

This paper proposes the development of Learning Readiness Measures (LRMs) to extend TRLs and HRLs, thereby providing the acquisition workforce with a structured, data-driven approach to assessing the maturity and effectiveness of learning technologies. By embedding learning engineering principles—centered on rigorous, measurable learning outcomes—into the acquisition lifecycle, LRMs will help mitigate the risk of adopting ineffective training solutions. These measures will also facilitate instrumentation and integration of a systematic learning engineering process into military acquisition, ensuring that learning technologies align with operational requirements and evolving workforce competencies.

Furthermore, this paper discusses how LRMs can serve as a risk mitigation strategy aligned with DoD policies, emphasizing the importance of reducing uncertainty in acquisition decisions. We will instrumentation strategies that enable collection of actionable data on learning technology effectiveness, ensuring that future DoD training investments are scientifically validated and operationally relevant, providing an example of how this approach is being integrated in the DAU administrative operations. By integrating learning engineering, and LRMs specifically, into acquisition practices, DoD can enhance its ability to select and sustain adaptive, data-driven learning solutions that meet the demands of modern defense training and operations.

Keywords: ACQUISITION MODELS;HUMAN FACTORS;HUMAN PERFORMANCE;LEARNING STANDARDS;LEARNING TECHNOLOGY STANDARDS;PROTOTYPING;TECHNOLOGY

Menu

Shawn Miller