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.

翻译：自适应学习旨在提供定制化的教育活动（例如习题），以满足个体学习需求。然而，手工构建和推送此类活动是一个费力的过程。因此，本文研究了一项面向在线语言学习的自适应与个性化习题生成新任务。为此，我们结合了一种知识追踪模型（其根据每个学生的学习历史估计其不断演变的知识状态）和一种受控文本生成模型（其基于学生当前估计的知识状态及教师对所需属性（例如领域知识和难度）的要求，生成习题句子）。我们在多邻国的真实学习者交互数据上训练并评估了模型，证明了由学生状态引导的语言模型能够生成更优的习题。随后，我们通过多种模拟讨论了模型在教育应用中的潜在用途。这些模拟表明，我们的模型能够适应学生的个体能力，并通过个性化学习序列来提升其学习效率。