  In second language vocabulary learning, existing works have primarily focused
on either the learning interface or scheduling personalized retrieval practices
to maximize memory retention. However, the learning content, i.e., the
information presented on flashcards, has mostly remained constant. Keyword
mnemonic is a notable learning strategy that relates new vocabulary to existing
knowledge by building an acoustic and imagery link using a keyword that sounds
alike. Beyond that, producing verbal and visual cues associated with the
keyword to facilitate building these links requires a manual process and is not
scalable. In this paper, we explore an opportunity to use large language models
to automatically generate verbal and visual cues for keyword mnemonics. Our
approach, an end-to-end pipeline for auto-generating verbal and visual cues,
can automatically generate highly memorable cues. We investigate the
effectiveness of our approach via a human participant experiment by comparing
it with manually generated cues.
