EFL Students' Attitudes and Contradictions in a Machine-in-the-loop Activity System

This study applies Activity Theory and investigates the attitudes and contradictions of 67 English as a foreign language (EFL) students from four Hong Kong secondary schools towards machine-in-the-loop writing, where artificial intelligence (AI) suggests ideas during composition. Students answered an open-ended question about their feelings on writing with AI. Results revealed mostly positive attitudes, with some negative or mixed feelings. From a thematic analysis, contradictions or points of tension between students and AI stemmed from AI inadequacies, students' balancing enthusiasm with preference, and their striving for language autonomy. The research highlights the benefits and challenges of implementing machine-in-the-loop writing in EFL classrooms, suggesting educators align activity goals with students' values, language abilities, and AI capabilities to enhance students' activity systems.

Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT

ChatGPT is a state-of-the-art (SOTA) chatbot. Although it has potential to support English as a foreign language (EFL) students' writing, to effectively collaborate with it, a student must learn to engineer prompts, that is, the skill of crafting appropriate instructions so that ChatGPT produces desired outputs. However, writing an appropriate prompt for ChatGPT is not straightforward for non-technical users who suffer a trial-and-error process. This paper examines the content of EFL students' ChatGPT prompts when completing a writing task and explores patterns in the quality and quantity of the prompts. The data come from iPad screen recordings of secondary school EFL students who used ChatGPT and other SOTA chatbots for the first time to complete the same writing task. The paper presents a case study of four distinct pathways that illustrate the trial-and-error process and show different combinations of prompt content and quantity. The cases contribute evidence for the need to provide prompt engineering education in the context of the EFL writing classroom, if students are to move beyond an individual trial-and-error process, learning a greater variety of prompt content and more sophisticated prompts to support their writing.

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

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, 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.

The Role of AI in Human-AI Creative Writing for Hong Kong Secondary Students

The recent advancement in Natural Language Processing (NLP) capability has led to the development of language models (e.g., ChatGPT) that is capable of generating human-like language. In this study, we explore how language models can be utilized to help the ideation aspect of creative writing. Our empirical findings show that language models play different roles in helping student writers to be more creative, such as the role of a collaborator, a provocateur, etc