Reinforcement Learning for Tailored Vocabulary Growth in Second Language Education

Vocabulary training is an important aspect of language learning, particularly in ESL (English as a Second Language) learners, whose vocabulary training should be personalized. As a method that learns through interaction and feedback, Reinforcement Learning (RL) has the potential to be a successful way of dynamically personalizing vocabulary teaching. Current vocabulary learning systems tend to be based on the static information presentation and do not provide real-time adjustment to the specific learner improvement, motivation and cognitive requirements. To resolve them, this paper presents one framework Personalized Vocabulary Training based on Reinforcement Learning (PVT-RL) which can be used to adapt vocabulary drills based on the performance of learners, their engagement patterns, and the accuracy of their responses over time. The suggested approach employs an RL agent to suggest suitable vocabulary tasks, maximizing learning sequences in the course of the continuous feedback loop. The students are taken through a sequence of customized challenges in which the level of difficulty in the tasks grows relative to the changing mastery of the student. The experimental findings with the ESL learners of university level indicate that PVT-RL results in considerably higher vocabulary memory, enhanced engagement among the learners, and increased long-term memory as compared to non-adaptive learning systems. This framework enhances a more effective and interesting learning process by smartly matching learning instructions with specific learner profiles.

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