Chapter 1: The End of Second Language Acquisition?
Robert Swier
Abstract
This chapter critically examines the field of Second Language Acquisition (SLA) in light of recent advances in artificial intelligence. After briefly tracing the development of SLA from its emergence in the 1960s, I argue that despite decades of research, the field has not yet produced a method that meaningfully reduces the time, effort, or motivation required for attaining proficiency in a second language. Dedicated learners today remain largely subject to the same basic conditions faced by earlier generations: extensive study, sustained practice, uncertain outcomes, and often limited proficiency. This limited progress is then contrasted with the history of artificial intelligence (AI), which developed in parallel with SLA and similarly endured long periods of disappointment, including the so-called “AI winters.” Unlike SLA, however, AI research eventually produced transformative results. Reviewing the developments that led to contemporary large language models (LLMs), I consider whether AI has now begun to solve or bypass the practical communication problem that second-language learning traditionally seeks to address. If AI systems can provide fast, inexpensive, and increasingly reliable translation, interpretation, writing assistance, and multilingual dialogue, then for many, communication in a second language may be better achieved through native-language use mediated by AI than through years of formal study. The chapter concludes by contemplating the future of SLA, questioning what relevance the field retains if AI offers faster, cheaper, and more effective solutions to the communicative problems second-language learning has long promised to address.
About the Contributor
Robert Swier is a faculty member in the Faculty of Literature, Arts, and Cultural Studies at Kindai University in Osaka, Japan. He holds a Ph.D. from Kyoto University, where his doctoral research examined the use of 3D virtual environments for task-based language learning. He also holds undergraduate and master’s degrees in computer science from the University of Rochester, with specializations in artificial intelligence and natural language dialogue systems, and a master’s degree in computer science from the University of Toronto, specializing in computational linguistics. His research interests have included corpus-based statistical methods for semantic role labeling, as well as computer-assisted language learning. His current focus is on the implications of advanced AI systems for second language learning and multilingual communication.
Citation
Swier, R. (2026). The end of second language acquisition? In R. Dykes, O. Edwards, D. Bollen, & T. S. W. Lin (Eds.), Artificial intelligence in Japan’s language learning classrooms (pp. 12–31). Candlin & Mynard. https://doi.org/10.47908/45/1
This chapter critically examines the field of Second Language Acquisition (SLA) in light of recent advances in artificial intelligence. After briefly tracing the development of SLA from its emergence in the 1960s, I argue that despite decades of research, the field has not yet produced a method that meaningfully reduces the time, effort, or motivation required for attaining proficiency in a second language. Dedicated learners today remain largely subject to the same basic conditions faced by earlier generations: extensive study, sustained practice, uncertain outcomes, and often limited proficiency. This limited progress is then contrasted with the history of artificial intelligence (AI), which developed in parallel with SLA and similarly endured long periods of disappointment, including the so-called “AI winters.” Unlike SLA, however, AI research eventually produced transformative results. Reviewing the developments that led to contemporary large language models (LLMs), I consider whether AI has now begun to solve or bypass the practical communication problem that second-language learning traditionally seeks to address. If AI systems can provide fast, inexpensive, and increasingly reliable translation, interpretation, writing assistance, and multilingual dialogue, then for many, communication in a second language may be better achieved through native-language use mediated by AI than through years of formal study. The chapter concludes by contemplating the future of SLA, questioning what relevance the field retains if AI offers faster, cheaper, and more effective solutions to the communicative problems second-language learning has long promised to address.
About the Contributor
Robert Swier is a faculty member in the Faculty of Literature, Arts, and Cultural Studies at Kindai University in Osaka, Japan. He holds a Ph.D. from Kyoto University, where his doctoral research examined the use of 3D virtual environments for task-based language learning. He also holds undergraduate and master’s degrees in computer science from the University of Rochester, with specializations in artificial intelligence and natural language dialogue systems, and a master’s degree in computer science from the University of Toronto, specializing in computational linguistics. His research interests have included corpus-based statistical methods for semantic role labeling, as well as computer-assisted language learning. His current focus is on the implications of advanced AI systems for second language learning and multilingual communication.
Citation
Swier, R. (2026). The end of second language acquisition? In R. Dykes, O. Edwards, D. Bollen, & T. S. W. Lin (Eds.), Artificial intelligence in Japan’s language learning classrooms (pp. 12–31). Candlin & Mynard. https://doi.org/10.47908/45/1
Information About the Book
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Title: Artificial Intelligence in Japan’s Language Learning Classroom
Editors: Robert Dykes, Oliver Edwards, Dave Bollen, and Tina Shu-wen Lin Publication date: June 2026 Read more... |