ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education
This work addresses the limited empirical analysis of real-world student-AI interactions in EFL education, providing a foundational dataset for researchers, though it is incremental as it focuses on data collection and baseline tasks.
The study introduced ChEDDAR, a dataset from a semester-long experiment with 212 college students in EFL writing courses, where students revised essays through dialogues with ChatGPT, and analyzed usage patterns and perceptions to establish baselines for intent detection and satisfaction estimation in educational AI systems.
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.