  Reading and repeatedly retelling a short story is a common and effective
approach to learning the meanings and usages of target words. However, learners
often struggle with comprehending, recalling, and retelling the story contexts
of these target words. Inspired by the Cognitive Theory of Multimedia Learning,
we propose a computational workflow to generate relevant images paired with
stories. Based on the workflow, we work with learners and teachers to
iteratively design an interactive vocabulary learning system named RetAssist.
It can generate sentence-level images of a story to facilitate the
understanding and recall of the target words in the story retelling practices.
Our within-subjects study (N=24) shows that compared to a baseline system
without generative images, RetAssist significantly improves learners' fluency
in expressing with target words. Participants also feel that RetAssist eases
their learning workload and is more useful. We discuss insights into leveraging
text-to-image generative models to support learning tasks.
