This study investigated the effect of cloze item practice on reading comprehension, where cloze items were either created by humans, by machine using natural language processing techniques, or randomly. Participants from Amazon Mechanical Turk (N = 302) took a pre-test, read a text, and took part in one of five conditions, Do-Nothing, Re-Read, Human Cloze, Machine Cloze, or Random Cloze, followed by a 24-hour retention interval and post-test. Participants used the MoFaCTS system [27], which in cloze conditions presented items adaptively based on individual success with each item. Analysis revealed that only Machine Cloze was significantly higher than the Do-Nothing condition on post-test, d = 0.58, CI[subscript 95][0.21,0.94]. Additionally, Machine Cloze was significantly higher than Human and Random Cloze conditions on post-test, d =0.49, CI[subscript 95][0.12,0.86] and d = 0.71, CI[subscript 95][0.34, 1.09] respectively. These results suggest that Machine Cloze items generated using natural language processing techniques are effective for enhancing reading comprehension when delivered by an adaptive practice scheduling system. [This paper was published in: E. Andre et al. (Eds.), "Proceedings of the 18th International Conference on Artificial Intelligence in Education" (pp. 262-273). Wuhan, China: Springer.]