RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education
This provides a foundational dataset for analyzing real-world student-ChatGPT interactions in EFL education, though it is incremental as it focuses on data collection and baseline tasks rather than novel methods.
The authors tackled the lack of large-scale empirical data on student-AI interactions in education by presenting RECIPE4U, a dataset from a semester-long experiment with 212 college students using ChatGPT for EFL writing revision, which includes annotated dialogues and baseline results for intent detection and satisfaction estimation.
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/.