Adaptive instructional systems (AISs) can provide individualized training to new learners through algorithms that alter the learning experience. Research emphasizes adaptive difficulty as one such technique to improve learning outcomes. However, recent research has identified cases where adaptive difficulty can be inefficient for some learners, such as those who struggle to cope with changes in the demands of the task they are learning or those who struggle to perform during early phases of training. The present work examines another perspective as to why adaptive difficulty helps some learners but not others. Specifically, we investigated the quality of a learner’s initial encoding and its latent effects throughout the training experience. Human instructors can often determine which students understand a task and which do not based on factors such as learners’ processing speed, quiz scores, mistakes committed, and questions asked, and we explored the potential for AISs to make such determinations. Our results suggest that adaptive difficulty is less beneficial for learners with poor initial encoding quality and that pre-task measures could provide insights into encoding quality that can improve adaptive training outcomes. We encourage AIS designers to incorporate encoding measures into algorithm design to enhance the traditional effectiveness of adaptive difficulty techniques.