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.

本篇研究分析了 EFL 学生在使用 ChatGPT 完成写作任务时的提示内容，探讨了提示质量和数量的模式，并提出了在 EFL 写作课堂中提供提示工程教育的必要性。

英语作为外语中学生通过 ChatGPT 完成写作任务的案例

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

We describe a method for proactive information retrieval targeted at
retrieving relevant information during a writing task. In our method, the
current task and the needs of the user are estimated, and the potential next
steps are unobtrusively predicted based on the user's past actions. We focus on
the task of writing, in which the user is coalescing previously collected
information into a text. Our proactive system automatically recommends the user
relevant background information. The proposed system incorporates text input
prediction using a long short-term memory (LSTM) network. We present
simulations, which show that the system is able to reach higher precision
values in an exploratory search setting compared to both a baseline and a
comparison system.

本研究通过使用 LSTM 网络和背景信息推荐系统，为写作任务提供积极信息检索，以提高搜索质量。