| Asia Pacific Journal of Language, Culture, and Education Vol. 1, No. 1, pp. 1-11 |
| Abbreviation: APJLCE |
| ISSN: 3092-362X |
| Publication date: 30 June 2026 |
| Received: 18 April 2026 / Received in Revised Form: 4 June 2026 / Accepted: 23 June 2026 |
| DOI: https://doi.org/10.23403/apjlce.2026.1.1.1
|
Corpus Tools in a World of Generative AI: What Role Should They Play? |
| Laurence Anthony (Waseda University), JAPAN |
| Copyright 2026 APJLCE This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited. |
Abstract |
| Generative AI has dramatically transformed the conditions under which language teaching and learning take place, enabling teachers to rapidly develop materials and evaluate learner outputs, and helping learners to produce fluent and well-structured texts with minimal effort. This development raises important questions about the continuing role of corpus tools and methods, which have traditionally supported the analysis and learning of language through engagement with authentic data, and have also served as an in-class learning approach in the form of data-driven learning (DDL). In this paper, I offer a conceptual framework and design-oriented perspective on how the role of corpus tools needs to be reinterpreted and redesigned in this new world of generative AI. I argue that while AI has democratized the production of language, there is a growing need to validate these outputs, especially in highly specialized domains and under-resourced language contexts. At the same time, learners still need to develop the ability to express ideas, structure arguments, and make appropriate linguistic choices, and teachers still require ways to ground evaluation and feedback in evidence. Corpus tools should therefore evolve as complementary systems to AI, supporting evidence-based learning, pattern detection, and the verification of AI-generated output. As an illustrative example of this direction, the paper describes a recent update to AntConc (version 4.4), which integrates AI-supported exploration with traditional corpus analysis. |
Keywords |
| AntConc, Corpus Tools, Data-Driven Learning, Generative AI, Language Teaching, Verification |
References |
| Anthony, L. (2022). What can corpus software do? In A. O’Keeffe & M. McCarthy (Eds.), The Routledge Handbook of Corpus Linguistics. Routledge Press.
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The Author |
| Laurence Anthony is Professor of Applied Linguistics at the Faculty of Science and Engineering, Waseda University, Japan. He has a BSc degree (Mathematical Physics) from the University of Manchester, UK, and MA (TESL/TEFL) and PhD (Applied Linguistics) degrees from the University of Birmingham, UK. He is a founding member of the Center for English Language Education in Science and Engineering (CELESE), which runs discipline-specific language courses for the 10,000 students of the faculty. His research focuses on language data science, AI, corpus linguistics, educational technology, and English for Specific Purposes (ESP). He received the National Prize of the Japan Association for English Corpus Studies (JAECS) in 2012 for his work in corpus software tools design, including the creation of AntConc. |
The Author’s Address |
| First and Corresponding Author Laurence Anthony Professor Waseda University 3-4-1 Okubo, Shinjuku, Tokyo 169-0072, JAPAN E-mail: anthony@waseda.jp |
