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 model that generates exercise sentences based
on the student's current estimated knowledge state and instructor requirements
of desired properties (e.g., domain knowledge and difficulty). We train and
evaluate our model on real-world learner interaction data from Duolingo and
demonstrate that LMs guided by student states can generate superior exercises.
Then, we discuss the potential use of our model in educational applications
using various simulations. These simulations show that our model can adapt to
students' individual abilities and can facilitate their learning efficiency by
personalizing learning sequences.

本文介绍了一项新的任务，旨在为在线语言学习提供个性化的练习生成，我们结合知识追踪模型和受控文本生成模型，训练并在 Duolingo 的真实学习者交互数据上评估我们的模型，并展示了学生个性化生成的优越性能以及模型在教育应用中的潜在用途。