Exploring prompt iteration process through generative AI image creation in EFL vocabulary learning

Abstract Amid the growing use of GenAI in second language education and its emerging multimodality, this study investigated how Korean undergraduates studying EFL enacted prompt iteration with a text-to-image generator to support multimodal vocabulary learning. Participants read etymology-rich word stories, sketched visualizations, and iteratively refined prompts until the images aligned with their intentions. Data included AI-generated artworks, reflective papers documenting the process, and a vocabulary learning strategies survey. Overall, learners reported high use of strategies after the task. Analysis of artworks and reflections revealed a metacognitive–strategic cycle: learners monitored and evaluated outputs, then strategically varied contextual features to negotiate meaning and achieve nuanced imagery. Framed as both a prompt-engineering technique and a literacy practice involving planning, monitoring, and evaluation, prompt iteration emerged as central to GenAI-mediated vocabulary learning, highlighting learners’ agency in exploring word meanings.