Shin, Lee, & Noh (2024). Realizing corrective feedback in ...chatbot...
작성자Dongkwang Shin작성시간24.01.18조회수63 목록 댓글 0Shin, D., Lee, J. H., & Noh, I. W. (2024). Realizing corrective feedback in task-based chatbots engineered for second language learning. RELC Journal, 0(0). https://doi.org/10.1177/00336882231221902
2024. 01. 17 https://journals.sagepub.com/eprint/3ZWW3VZRDNFGHFAV2VM2/full
Building on the work of customized chatbots for language teaching and learning and the second-language acquisition literature on corrective feedback (CF), this article showcases an innovative practice for building a tailored and task-based chatbot to provide CF. Given that extant chatbots are generally not sensitive to learners’ grammatical errors, we illustrate a way to install a CF function by using ‘action and parameters’ and ‘define prompts’ options in the chatbot-building platform known as Google DialogflowTM. Our study, which included upper-grade English-as-a-foreign language learners in South Korea, demonstrated that customized chatbots could offer CF when students made non-target utterances and elicit learner uptake successfully. Based on our innovation, we then provide directions for pedagogy on chatbot-based language learning.