ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education

TL;DR

This paper introduces ChEDDAR, a comprehensive dataset of student-ChatGPT dialogues in EFL writing courses, analyzing usage patterns, perceptions, and establishing baseline results for intent detection and satisfaction estimation in educational dialogue systems.

Contribution

The paper presents the first large-scale, longitudinal dataset of student-ChatGPT interactions in EFL writing education, including detailed annotations and baseline task results.

Findings

Students' usage patterns and perceptions analyzed

Baseline results for intent detection established

Insights into AI integration in EFL education provided

Abstract

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.