Exploring EFL students’ prompt engineering in human–AI story writing: an activity theory perspective

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 their own 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, 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.

KEYWORDS:

• Generative artificial intelligence
• prompt engineering
• EFL students
• story writing
• human–machine collaboration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in the Open Science Framework (OSF) at http://doi.org/10.17605/OSF.IO/J7RKY

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Additional information

Notes on contributors

David James Woo

David James Woo is a secondary school teacher. His research interests are in generative artificial intelligence and English language writing education.

Kai Guo

Kai Guo is a Ph.D. candidate in the Faculty of Education at The University of Hong Kong. His research focuses on second-language writing, computer-supported collaborative learning, artificial intelligence in education, and gamification in education. His recent publications have appeared in international peer-reviewed journals such as Computers & Education, Interactive Learning Environments, Journal of Educational Computing Research, and TESOL Quarterly, and Assessing Writing.

Hengky Susanto

Hengky Susanto received his BS, MS and PhD degree in computer science from the University of Massachusetts system. He was a postdoctoral research fellow at the University of Massachusetts Lowell and the Hong Kong University of Science and Technology. He was also senior researcher at Huawei Future Network Theory Lab. Currently, he is a principal researcher in a startup-mode research laboratory and a lecturer at Education University of Hong Kong. His research interests include applied AI (computer vision and NLP), smart city, and computer networking.