A Deep Learning Model Integrating Reinforcement Learning for Personalized English Language Learning and Automated Feedback in CALL Systems

Traditional CALL systems remain somewhat restricted by their static rule-based paradigm and inability to adapt to individual learner needs. These systems present a standardized set of exercises and feedback, creating difficulties for the design of personalized learning paths that evolve with the learner's strengths and weaknesses. The RNN component serves to promote language learning by the effective sequential processing of text and speech data, helping the system develop recognition of grammatical structures, correct pronunciation, and better sentence constructions. When combined with these DRL techniques, RNN types such as LSTM and variants such as GRU are used for their capability to capture information even from the very end part of the input sequence which may refer to things that would strengthen the hold of linguistic structures over time. Additionally, the DRL component optimizes learning measures according to the learner's performance record, such as undergoing different levels of difficult activities adapted according to the task at hand for the provision of real-time hints. This is why it creates a state-action-reward mechanism personalized learning environment by rewarding good learning practices followed by the eradication of jumbled errors from the learning pathway. The signature features of this research included an AI-based automated feedback system, providing instant corrections that were matched to human inputs, as well as a dynamic curriculum generation process that modified learning materials according to the tracked progress through the use of engagement measures. Experimentation against the most traditional CALL systems records major improvements in foreign language proficiency, bringing errors down by 37% and learner engagement up by 35%.