This work presents a study that used distributed language representations of test items to model test item difficulty. Distributed language representations are low-dimensional numeric representations of written language inspired and generated by artificial neural network architecture. The research begins with a discussion of the importance of item difficulty modeling in the context of psychometric measurement. A review of the literature synthesizes the most recent automated approaches to item difficulty modeling, introduces distributed language representations, and presents relevant predictive modeling methods. The present study used an item bank from a certification examination in a scientific field as its data set. The study first generated and assessed the quality of distributed item representations with a multi-class similarity comparison. Then, the distributed item representations were used to train and test predictive models. The multi-class similarity task showed that the distributed representations of items were more similar on average to items within their content domain versus outside of their domain in 14 out of 25 domains. The prediction task did not produce any meaningful predictions from the distributed representations. The study ends with a discussion of limitations and potential avenues for future research. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]