The authors of this article use text mining techniques to uncover hidden or latent topics in economic education. The common use of JEL codes only identifies the academic setting for each paper but does not identify the underlying economic concept the paper addresses. An unsupervised machine learning algorithm called Latent Dirichlet Allocation is utilized to identify 15 hidden topics in economic education scholarly work. The text mining model identifies economic education topics by finding correlations in word usage across different documents. The authors show that these newly identified research topics explain more variation in citation counts than the commonly adopted JEL codes. Moreover, specific journals display preferences for certain topics within economic education research.