The current time-consuming, costly process of defining training requirements and designing an appropriate training system is inefficient and outdated. We propose the application of a stream-lined, time-sensitive model to support current rapid acquisition needs. It expeditiously assigns training objectives to an expanded realm of training technologies. The proposed model, built upon existing research, evaluation, and best practice guidance by the DOD, has three interrelated concepts: (1) an algorithmic assignment process, rendering consistent, data-determined technology requirements; (2) a re-conceptualization of training media as technology affordances; and (3) a reorientation of the task list toward cross-utilization of training system data and learning objectives. Re-conceptualized, training technologies' affordances are used to match training requirements to specific training capabilities. The model standardizes the identification of training tasks in terms of scope, classification, and characteristics, and retains data in user-friendly formats for future analyses. This function-based approach to defining tasks and objectives facilitates the transfer of existing data to future analyses, and creates a hierarchal task constellation structure of related and necessary tasks and training objectives.

To date, the model has been applied to eight DOD platforms, successfully assigning training technologies to learning objectives. The platforms are both manned and unmanned aircraft, some on the forefront in terms of technological advancement and human interaction. Resulting training requirements correspond to established training modalities such as computer-based training, part-task trainers, and flight training devices. The model supports rapid acquisition because it reduces the time necessary to conduct training requirements analyses through the standardization of data and data collection, the allocation process employed, and its decisions points. These features heighten transparency and increase customer visibility into the assignment process, resulting in improved customer confidence in training system recommendations. This paper will discuss the model and the results of its application to requirements definition across DOD platforms.