Response times (RTs) have recently attracted a significant amount of attention in the literature as they may provide meaningful information about item preknowledge. In this study, a new model, the Deterministic Gated Lognormal Response Time (DG-LNRT) model, is proposed to identify examinees with item preknowledge using RTs. The proposed model was applied to two different data sets and performance was assessed with false-positive rates, true-positive rates, and precision. The results were compared with another recently proposed Z-statistic. Follow-up simulation studies were also conducted to examine model performance in settings similar to the real data sets. The results indicate that the proposed model is viable and can help detect item preknowledge under certain conditions. However, its performance is highly dependent on the correct specification of the compromised items.