Realizing Corrective Feedback in Task-Based Chatbots Engineered for Second Language Learning
- Authors
- Shin, Dongkwang; Lee, Jang Ho; Noh, Wonjun Izac
- Issue Date
- Jan-2024
- Publisher
- SAGE PUBLICATIONS LTD
- Keywords
- chatbots; corrective feedback; Google Dialogflow (TM); task-based chatbot; English-as-a-foreign language
- Citation
- RELC JOURNAL
- Journal Title
- RELC JOURNAL
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72634
- DOI
- 10.1177/00336882231221902
- ISSN
- 0033-6882
1745-526X
- Abstract
- 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 Dialogflow (TM). 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.
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- Appears in
Collections - College of Education > Department of English Education > 1. Journal Articles
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