The integration of generative AI in education is expanding, yet empirical analyses of large-scale, real-world interactions between students and AI systems still remain limited. In this study, we present ChEDDAR, ChatGPT & EFL Learner's Dialogue Dataset As Revising an essay, which is collected from a semester-long longitudinal experiment involving 212 college students enrolled in English as Foreign Langauge (EFL) writing courses. The students were asked to revise their essays through dialogues with ChatGPT. ChEDDAR includes a conversation log, utterance-level essay edit history, self-rated satisfaction, and students' intent, in addition to session-level pre-and-post surveys documenting their objectives and overall experiences. We analyze students' usage patterns and perceptions regarding generative AI with respect to their intent and satisfaction. As a foundational step, we establish baseline results for two pivotal tasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. We finally suggest further research to refine the integration of generative AI into education settings, outlining potential scenarios utilizing ChEDDAR. ChEDDAR is publicly available at https://github.com/zeunie/ChEDDAR.

翻译：生成式人工智能在教育领域的整合正在扩展，但对学生与AI系统之间大规模真实交互的实证分析仍然有限。本研究提出了ChEDDAR（ChatGPT与EFL学习者修改作文对话数据集），该数据集来源于一项持续一学期的纵向实验，涉及212名参加英语作为外语（EFL）写作课程的大学生。学生被要求通过与ChatGPT的对话来修改作文。ChEDDAR包含对话记录、话语级别的作文编辑历史、自评满意度以及学生意图，此外还有会话级别的前后调查问卷，记录他们的目标与整体体验。我们分析了学生使用生成式AI的模式及其在意图和满意度方面的感知。作为基础性工作，我们在教育语境下的任务导向对话系统中，为两个关键任务建立了基线结果：意图检测与满意度估计。最后，我们提出进一步研究以优化生成式AI在教育环境中的整合，并概述了利用ChEDDAR的潜在场景。ChEDDAR已在https://github.com/zeunie/ChEDDAR公开提供。