A systematic review of research on speech-recognition chatbots for language learning: Implications for future directions in the era of large language models

ABSTRACT

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.

KEYWORDS:

• Chatbot
• large language model
• ChatGPT‌
• artificial intelligence‌
• automatic speech recognition
• intelligent personal assistant
• language learning
• computer-assisted language learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Jaeho Jeon

Jaeho Jeon is a doctoral student in the department of Literacy, Culture, and Language Education at Indiana University Bloomington. His research interests include computer-assisted language learning, dynamic assessment, and teacher education. His articles have been published in Computer Assisted Language Learning, Computers & Education, Interactive Learning Environments, Education and Information Technologies, System, among others.

Seongyong Lee

Seongyong Lee currently serves as an assistant professor in the department of English Education at Hannam University, South Korea. He obtained his PhD in second and foreign language education from the State University of New York at Buffalo. His research interests include computer-assisted language learning, world Englishes, English as a lingua franca (ELF) in academic settings, and English as a second language writing. His articles have appeared in Computer Assisted Language Learning, Computers & Education, Information Technology & People, English Today, Asian Englishes, Applied Linguistics Review, Lingua, System, among others.

Seongyune Choi

Seongyune Choi is a Ph.D. candidate in the Department of Computer Science and Engineering at Korea University in Seoul, South Korea. His research interests include computer science education, educational technologies, and artificial intelligence in education. His articles have appeared in International Journal of Human-Computer Interaction, Education and Information Technologies, Educational Technology & Society, among others.