@inproceedings{han-etal-2024-recipe4u,
    title = "{RECIPE}4{U}: Student-{C}hat{GPT} Interaction Dataset in {EFL} Writing Education",
    author = "Han, Jieun  and
      Yoo, Haneul  and
      Myung, Junho  and
      Kim, Minsun  and
      Lee, Tak Yeon  and
      Ahn, So-Yeon  and
      Oh, Alice",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1193",
    pages = "13666--13676",
    abstract = "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/.",
}

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/.
Han, Jieun Yoo, Haneul Myung, Junho Kim, Minsun Lee, Tak Yeon Ahn, So-Yeon Oh, Alice
RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education