The spaced repetition technique is widely used in language learning applications to improve long-term memory retention in learners. However, most traditional algorithms for spaced repetition are simple functions with a few parameters and lack temporal information to model the forgetting process, and recent reinforcement learning methods utilize model-free algorithms where student memory retention is not estimated. Consequently, these models are inadequate at estimating a learner’s performance and unable to be used adaptively based on user feedback, which results in unsatisfactory review schedules for the learner. To address this issue, this research proposes a personalized language learning framework that utilizes deep learning to design a trainable, adaptive, and efficient spaced repetition scheduling method. Specifically, the framework includes a forgetting-aware knowledge tracing model to track students’ memory and a reinforcement learning based spaced repetition scheduling algorithm to achieve greater memorization efficiency. Experiments based on real-world datasets have shown performance improvement of the proposed approach over other baseline methods.