Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective
David James Woo, Kai Guo, Hengky Susanto

TL;DR
This study investigates how EFL students use prompt engineering with AI tools in story writing, revealing their purposes, characteristics, and implications for teaching and AI design.
Contribution
It applies Activity Theory to analyze students' prompt engineering in AI-assisted story writing, highlighting their purposes and activity system characteristics.
Findings
Students prompt AI mainly to overcome writer's block and improve stories.
Students lack awareness of their prompting purposes.
Activity system characteristics vary with students' skill and school achievement.
Abstract
This study applies Activity Theory to investigate how English as a foreign language (EFL) students prompt generative artificial intelligence (AI) tools during short story writing. Sixty-seven Hong Kong secondary school students created generative-AI tools using open-source language models and wrote short stories with them. The study collected and analyzed the students' generative-AI tools, short stories, and written reflections on their conditions or purposes for prompting. The research identified three main themes regarding the purposes for which students prompt generative-AI tools during short story writing: a lack of awareness of purposes, overcoming writer's block, and developing, expanding, and improving the story. The study also identified common characteristics of students' activity systems, including the sophistication of their generative-AI tools, the quality of their stories,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Storytelling and Education · Educational Games and Gamification · Creativity in Education and Neuroscience
MethodsAttentive Walk-Aggregating Graph Neural Network
