This innovative practice article reports on the piloting of vibe coding (using natural language to create software applications with AI) for English as a Foreign Language (EFL) education. We developed a human-AI meta-languaging framework with three dimensions: talking to AI (prompt engineering), talking through AI (negotiating authorship), and talking about AI (mental models of AI). Using backward design principles, we created a four-hour workshop where two students designed applications addressing authentic EFL writing challenges. We adopted a case study methodology, collecting data from worksheets and video recordings, think-aloud protocols, screen recordings, and AI-generated images. Contrasting cases showed one student successfully vibe coding a functional application cohering to her intended design, while another encountered technical difficulties with major gaps between intended design and actual functionality. Analysis reveals differences in students' prompt engineering approaches, suggesting different AI mental models and tensions in attributing authorship. We argue that AI functions as a beneficial languaging machine, and that differences in how students talk to, through, and about AI explain vibe coding outcome variations. Findings indicate that effective vibe coding instruction requires explicit meta-languaging scaffolding, teaching structured prompt engineering, facilitating critical authorship discussions, and developing vocabulary for articulating AI mental models.

This poster proposes a human computation approach to collecting representative pictures for words so that the collected pictures can efficiently and effectively convey the meaning of the words or concepts. A large collection of representative pictures can be used in text-to-picture communication systems, and may also be used to teach computers to learn what representative pictures are. We have developed a web application to help players of Draw Something, a popular social mobile game, search pictures for drawing inspiration while at the same time they implicitly help us collect representative pictures for words. Our preliminary result shows that the proposed approach has the potential to harvest Draw Something players for collecting desired data.