Over ten years ago, our collaborators conducted a NSF-funded project identifying four broad categories of change strategies used to improve undergraduate STEM education through a comprehensive interdisciplinary literature review of articles from 1995 to 2008. Since this first iteration, there have been many major developments in undergraduate STEM education; particularly, the rapid development in sophisticated technology tools. These developments affect the nature of classroom instruction as well as expand the bounds on analyzing a corpus of articles. Thus, it is crucial to repeat this review to better understand the changes in STEM education from the more recent past. Our goal is to use machine learning to identify, potentially new, themes in the recent literature. We plan to compare and contrast both AI-assisted modeling and traditional, human-qualitative coding approaches in an effort to: (1) identify the benefits and faults of using AI verses human coding, and (2) portray a comprehensive story of change instruction literature from 2010. This lightning talk will describe the data extraction process and preliminary results from machine learning models. In addition to sharing the beginnings of our work, we hope to gain new perspectives and ideas from computing education scientists regarding our data representation and modeling choices.