Is children’s acquisition of structural knowledge driven by prediction errors? Error-driven models of language acquisition propose that children generate expectations about upcoming words (prediction), compare them to the input, and, when they detect a mismatch (i.e., prediction error signal), update their long-term linguistic knowledge. But we only have limited empirical evidence for this learning mechanism. Using a novel touch-screen app and a pre-post training between-subjects design, we tested the effect of prediction errors on 120 English-learning 4-year-olds’ understanding of challenging direct object datives. We hypothesized that children who are exposed to input that encourages the generation of prediction error signals should show greater improvements in their post-test comprehension scores. Consistent with error-driven models of language learning, we found that children exposed to sentences that encouraged the generation of incorrect linguistic predictions improved numerically more than those who were exposed to sentences that did not support predictions. However, we caution that these preliminary findings need to be confirmed by additional testing on much larger samples (we only tested 20–30 children per training condition). If confirmed, these findings would provide some of the strongest empirical support to date for the role of prediction error in the acquisition of linguistic structure.