Vocabulary learning support tools have widely exploited existing materials, e.g., stories or video clips, as contexts to help users memorize each target word. However, these tools could not provide a coherent context for any target words of learners' interests, and they seldom help practice word usage. In this paper, we work with teachers and students to iteratively develop Storyfier, which leverages text generation models to enable learners to read a generated story that covers any target words, conduct a story cloze test, and use these words to write a new story with adaptive AI assistance. Our within-subjects study (N=28) shows that learners generally favor the generated stories for connecting target words and writing assistance for easing their learning workload. However, in the read-cloze-write learning sessions, participants using Storyfier perform worse in recalling and using target words than learning with a baseline tool without our AI features. We discuss insights into supporting learning tasks with generative models.

翻译：词汇学习支持工具广泛利用现有材料（如故事或视频片段）作为语境，帮助用户记忆每个目标词汇。然而，这些工具无法为学习者感兴趣的任何目标词汇提供连贯的语境，且很少帮助练习词汇用法。本文通过与教师和学生的迭代协作，开发了Storyfier系统——该系统利用文本生成模型，使学习者能够阅读覆盖任意目标词汇的生成故事、完成故事完形填空测试，并在自适应AI辅助下使用这些词汇创作新故事。我们的受试者内实验（N=28）表明，学习者普遍青睐生成故事在连接目标词汇方面的作用，以及写作辅助在减轻学习负担方面的效果。然而，在"阅读-完形-写作"学习环节中，与使用不含AI功能的基线工具相比，使用Storyfier的参与者在目标词汇的回忆与运用方面表现更差。我们探讨了用生成模型支持学习任务的相关启示。