TY - JOUR
T1 - A systematic review of research on speech-recognition chatbots for language learning
T2 - Implications for future directions in the era of large language models
AU - Jeon, Jaeho
AU - Lee, Seongyong
AU - Choi, Seongyune
N1 - Publisher Copyright:
© 2023 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - Chatbot research has received growing attention due to the rapid diversification of chatbot technology, as demonstrated by the emergence of large language models (LLMs) and their integration with automatic speech recognition. However, among various chatbot types, speech-recognition chatbots have received limited attention in relevant research reviews, despite their increasing potential for language learning. To fill this gap, 32 empirical studies on speech-recognition chatbots for language learning were reviewed. The following information was reviewed for each study: basic publication information, research focus, location of chatbot use, methodology, group design format, participant information, intervention duration, target language, device type adopted, and chatbot role. An upward trend in research quantity starting in 2020 was identified, which accelerated exponentially in 2022. College students were more likely than other groups to be involved in research, and English as a second or foreign language was the most common target language. Most studies focused on participants’ perceptions of chatbots and the degree to which using chatbots helped them develop their speaking or listening proficiency. Methodologically, single-chatbot design using mixed methods was the most common design format, and most studies were conducted for more than one month in laboratory or classroom settings. Conventional mobile devices, such as smartphones, tablet PCs, and smart speakers without a screen, were the most frequently adopted device types. The chatbots’ most common role was as conversational partner. A detailed discussion of these results and their implications for future research on speech-recognition chatbots, particularly regarding the use of LLM-powered chatbots, is provided.
AB - Chatbot research has received growing attention due to the rapid diversification of chatbot technology, as demonstrated by the emergence of large language models (LLMs) and their integration with automatic speech recognition. However, among various chatbot types, speech-recognition chatbots have received limited attention in relevant research reviews, despite their increasing potential for language learning. To fill this gap, 32 empirical studies on speech-recognition chatbots for language learning were reviewed. The following information was reviewed for each study: basic publication information, research focus, location of chatbot use, methodology, group design format, participant information, intervention duration, target language, device type adopted, and chatbot role. An upward trend in research quantity starting in 2020 was identified, which accelerated exponentially in 2022. College students were more likely than other groups to be involved in research, and English as a second or foreign language was the most common target language. Most studies focused on participants’ perceptions of chatbots and the degree to which using chatbots helped them develop their speaking or listening proficiency. Methodologically, single-chatbot design using mixed methods was the most common design format, and most studies were conducted for more than one month in laboratory or classroom settings. Conventional mobile devices, such as smartphones, tablet PCs, and smart speakers without a screen, were the most frequently adopted device types. The chatbots’ most common role was as conversational partner. A detailed discussion of these results and their implications for future research on speech-recognition chatbots, particularly regarding the use of LLM-powered chatbots, is provided.
KW - ChatGPT
KW - Chatbot
KW - artificial intelligence
KW - automatic speech recognition
KW - computer-assisted language learning
KW - intelligent personal assistant
KW - language learning
KW - large language model
UR - http://www.scopus.com/inward/record.url?scp=85156161566&partnerID=8YFLogxK
U2 - 10.1080/10494820.2023.2204343
DO - 10.1080/10494820.2023.2204343
M3 - Article
AN - SCOPUS:85156161566
SN - 1049-4820
JO - Interactive learning environments
JF - Interactive learning environments
ER -