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 https://zeunie.github.io/RECIPE4U/.

翻译：生成式人工智能在教育领域的整合正在扩展，然而对学生与AI系统之间大规模真实交互的实证分析仍然有限。为弥补这一空白，我们提出了RECIPE4U（大学配方）数据集，该数据集源自对212名大学生在英语作为外语写作课程中进行的为期一学期的实验。研究期间，学生与ChatGPT进行对话以修改其论文。RECIPE4U包含这些交互的全面记录，包括对话日志、学生意图、学生自评满意度以及学生的论文编辑历史。特别地，我们基于编码方案对RECIPE4U中学生的话语进行标注，定义了13个意图标签。我们针对教育场景中面向任务对话系统的两个子任务建立了基线结果：意图检测与满意度评估。作为基础步骤，我们通过RECIPE4U探索学生-ChatGPT的交互模式，并聚焦于学生对话、论文数据统计以及学生论文编辑行为进行分析。我们进一步阐述了RECIPE4U数据集在增强教育框架中大型语言模型应用方面的潜在价值。RECIPE4U已公开发布于https://zeunie.github.io/RECIPE4U/。