Adaptive and Personalized Exercise Generation for Online Language Learning

This paper presents a system that combines knowledge tracing to estimate student language learning states with controlled text generation to create adaptive, personalized translation exercises for online language learning platforms like Duolingo. The system dynamically adjusts exercise difficulty and vocabulary based on individual student progress and instructor requirements.

Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language learning. To this end, we combine a knowledge tracing model that estimates each student’s evolving knowledge states from their learning history and a controlled text generation mo

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