The integration of generative AI in education is expanding, yet empirical
analyses of large-scale and real-world interactions between students and AI
systems still remain limited. Addressing this gap, we present RECIPE4U (RECIPE
for University), a dataset sourced from a semester-long experiment with 212
college students in English as Foreign Language (EFL) writing courses. During
the study, students engaged in dialogues with ChatGPT to revise their essays.
RECIPE4U includes comprehensive records of these interactions, including
conversation logs, students' intent, students' self-rated satisfaction, and
students' essay edit histories. In particular, we annotate the students'
utterances in RECIPE4U with 13 intention labels based on our coding schemes. We
establish baseline results for two subtasks in task-oriented dialogue systems
within educational contexts: intent detection and satisfaction estimation. As a
foundational step, we explore student-ChatGPT interaction patterns through
RECIPE4U and analyze them by focusing on students' dialogue, essay data
statistics, and students' essay edits. We further illustrate potential
applications of RECIPE4U dataset for enhancing the incorporation of LLMs in
educational frameworks. RECIPE4U is publicly available at
this https URL

将生成式人工智能技术应用于教育领域的研究正在扩展，本文通过对 212 名大学生的英语作为外语写作课程进行为期一学期的实验，创造了名为 RECIPE4U 的数据集，记录了学生与 ChatGPT 对话修订文章的全过程，并对学生的意图和满意度进行了标注、分析和学生的修订文章进行了统计，进一步探索了 RECIPE4U 数据集在教育框架中应用人工智能技术的潜力。