At the recent GIFTSym11, we presented recent applied research regarding the similarities between the function of AI-enabled intelligent tutoring systems and the process of Learning Engineering.

In the same way that Learning Engineering itself is developing into a sort of bridge between these disciplines in the pursuit of scaling meaningful and measurable learning experiences, likewise, as a data broker between the worlds of synthetic training and distributed learning analytics, GIFT serves as a sort of AI-enabled technological bridge.

Within a research context, there is a need for large and relevant activity data sets to test hypotheses regarding competency assertion. But where do these data sets come from?