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，然而关于学生与人工智能系统之间大规模、真实世界交互的实证分析仍然有限。在本研究中，我们提出了ChEDDAR，即ChatGPT与英语作为外语学习者在论文修改过程中的对话数据集，该数据集收集自一项长达一学期的纵向实验，涉及212名参与英语作为外语（EFL）写作课程的大学生。学生被要求通过与ChatGPT的对话来修改他们的论文。ChEDDAR包含对话记录、话语层面的论文编辑历史、自评满意度以及学生意图，此外还有记录其目标和整体体验的会话层面前后调查。我们分析了学生关于生成式人工智能的使用模式及其感知，涉及意图和满意度。作为基础步骤，我们在教育背景下为面向任务的对话系统中的两个关键任务建立了基线结果：意图检测和满意度估计。我们最后提出了进一步研究建议，以优化生成式人工智能在教育环境中的整合，并概述了利用ChEDDAR的潜在场景。ChEDDAR公开可访问于https://github.com/zeunie/ChEDDAR。