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Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective (2306.01798v2)

Published 1 Jun 2023 in cs.CY and cs.AI

Abstract: This study applies Activity Theory to investigate how English as a foreign language (EFL) students prompt generative AI tools during short story writing. Sixty-seven Hong Kong secondary school students created generative-AI tools using open-source LLMs 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, and their school's overall academic achievement level, for their prompting of generative-AI tools for the three purposes during short story writing. The study's findings suggest that teachers should be aware of students' purposes for prompting generative-AI tools to provide tailored instructions and scaffolded guidance. The findings may also help designers provide differentiated instructions for users at various levels of story development when using a generative-AI tool.

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Authors (3)
  1. David James Woo (9 papers)
  2. Kai Guo (38 papers)
  3. Hengky Susanto (12 papers)
Citations (2)

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