Storyfier: Exploring Vocabulary Learning Support with Text Generation
  Models
Zhenhui Peng Xingbo Wang Qiushi Han Junkai Zhu Xiaojuan Ma Huamin Qu
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.