Integrating AI technology into corpus-based language learning through ChatAI
Open accessAbstract
This paper proposes a novel solution to the problem of integrating AI technology with traditional corpus methods through the introduction of a ChatAI tool within the AntConc corpus analysis toolkit. ChatAI is designed to simplify both the management and use of large language models (LLMs) in classroom settings. On the management side, the tool allows teachers and learners to easily access and use powerful propriety LLMs, such as OpenAI’s GPT5 models. It also allows them to download and run open access and open weight LLMs, such as Llama models, on their own computers, while keeping their data completely private. On the language learning side, ChatAI offers users a way to directly interact with LLMs both when creating corpus queries and also when analyzing or interpreting the results generated from corpus queries. As a result, teachers and learners can begin a lesson by querying a corpus with natural language using a full range of traditional corpus methods (e.g. Key-Word-In-Context, Dispersion Plots, Clusters, N-Grams, Collocates, Word/Keyword, and Wordclouds), and then pipe the results of their analyses to an LLM for further insights and discussion. Importantly, in contrast to online services like ChatGPT, ChatAI improves transparency by allowing users to see and edit important LLM settings, such as the system and user prompts as well as the temperature setting. These features not only lead to a smooth and intuitive lesson flow, but also improve the authenticity and replicability of results, while at the same time greatly reducing the possibility of LLM hallucinations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI-driven front ends can make corpus insights more accessible to non-specialists while preserving the methodological strengths of corpus analysis.
“This aligns with emerging work on integrating AI with corpus methods, work that argues that AI-driven front ends can make corpus insights more accessible to non-specialists while preserving the methodological strengths of corpus analysis (Anthony, 2025).”
From A New Era with Artificial Intelligence Powered Learner Corpora for Teaching and Learning of English as a Second Language in Primary School Education (2025) · cites this paper as Anthony, 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Learning Vocabulary in Another Language
Nation, I. S. P. (2001) · Cambridge University Press · doi:10.1017/cbo9781139524759 · Cited by 6,608 · see citing works
Corpus Use in Language Learning: A Meta‐Analysis
Boulton, A., & Cobb, T. (2017) · Language Learning · doi:10.1111/lang.12224 · Cited by 635 · see citing works
Ji, H., Han, I., & Ko, Y. (2023) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2142873 · Cited by 394 · see citing works
A Survey on Model Compression for Large Language Models
Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00704 · Cited by 273 · see citing works
Generative AI and the end of corpus-assisted data-driven learning? Not so fast!
Crosthwaite, P. R., & Baisa, V. (2023) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2023.100066 · Cited by 135 · see citing works
Exploring ChatGPT literacy in language education: A global perspective and comprehensive approach
Ma, Q., Crosthwaite, P. R., Sun, D., & Zou, D. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100278 · Cited by 133 · see citing works
Ma, Q., Tang, J., & Lin, S. (2021) · Computer Assisted Language Learning · doi:10.1080/09588221.2021.1895225 · Cited by 87 · see citing works
Does semantic tagging identify cultural change in British and American English?
Potts, A., & Baker, P. (2012) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.17.3.01pot · Cited by 60 · see citing works
Introducing knowledge-based vocabulary lists (KVL)
Schmitt, N., Dunn, K., O’Sullivan, B., Anthony, L., & Kremmel, B. (2021) · TESOL Journal · doi:10.1002/tesj.622 · Cited by 47 · see citing works
Self-citationA user-friendly corpus tool for disciplinary data-driven learning
Crosthwaite, P. R., & Baisa, V. (2024) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.23056.cro · Cited by 41 · see citing works
Visualisation in corpus-based discourse studies
Anthony, L. (2018) · doi:10.4324/9781315179346 · Cited by 20
Self-citationPersonalized Tutoring Through Conversational Agents
Liu, D., Huang, R., Chen, Y., Adarkwah, M. A., Zhang, X., Li, X., Zhang, J., & Da, T. (2024) · Smart computing and intelligence · doi:10.1007/978-981-97-5826-5_4 · Cited by 6 · see citing works
Exploring AI to automate EFL corrective written feedback in the first language
Hirschel, R., & Horai, K. (2025) · Technology in Language Teaching & Learning · doi:10.29140/tltl.v7n1.2208 · Cited by 4 · see citing works
Knowledge-based Vocabulary Lists
Anthony, L., Kremmel, B., Schmitt, N., Dunn, K., & O’Sullivan, B. (2024) · University of Toronto Press eBooks · doi:10.3138/9781800504158 · Cited by 2 · see citing works
Self-citationAnthony, L. (2022) · The Routledge Handbook of Corpus Linguistics · doi:10.4324/9780367076399-9
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Johns, T. (1991). Should you be persuaded: Two samples of data-driven learning materials. ELR Journal, 4, 1-16.
Cited by
Sorted by citations
Corpus linguistics and AI in the reconfiguration of language learning ecologies
Pérez-Paredes, P. (2026) · Language Teaching · doi:10.1017/s0261444825101109 · Cited by 7 · see citing works
Wong, W. L., & Fong, S. (2025) · Applied Language Sciences · doi:10.65553/als.250105 · Cited by 2 · see citing works
The paradox of inclusion: Generative AI and the center-periphery divide in language education
Shi, K. (2026) · System · doi:10.1016/j.system.2026.104103 · Cited by 1 · see citing works
Har, F. T. K. (2026) · Applied Language Sciences · doi:10.65553/als.260104
Rodríguez-Puente, P. (2026) · Springer eBooks · doi:10.1007/978-3-031-87993-7_597-1
LM- vs Corpus-driven translation of a Special Power of Attorney
Giampieri, P. (2026) · LFE. Revista de lenguas para fines específicos/Revista de lenguas para fines específicos · doi:10.20420/rlfe/2026.865
CorpusMate 2.0: Integrating AI-powered language analysis into a data-driven learning corpus tool
Crosthwaite, P. R. (2026) · Artificial Intelligence in Language Education · doi:10.29140/aile.2026.104826
Ma, Q., Mei, F., Crosthwaite, P. R., & Ma, X. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105728
Academic vocabulary use in research publications in the AI era: human-authored vs AI-mediated texts
Laosrirattanachai, P., & Laosrirattanachai, P. (2026) · The JALT CALL Journal · doi:10.29140/jaltcall.2026.103271
Multimodal GenAI-supported personalization: engagement, autonomy, and L2 performance
Liu, S., & Zhi, Y. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2738194
Otile, J., Robinette, R., Johnson, K., Peram, R. R., Ndyomugabe, M., Mujuni, E., Abdulla, I. M., Khalif, A. Y., Muluga, W. U., Batholomae, C., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-120245
Converging GenAI and Data-Driven Learning for ESP Materials Development
Mužar, A. P., & Aleksić-Hajduković, I. (2026) · Komunikacija i kultura online · doi:10.18485/kkonline.2026.17.s2.2
- Authors
- Anthony, L.
- Affiliations
- Waseda University (JP)
- Corresponding author
- Anthony, Laurence
- ORCID
- Anthony, Laurence
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2025-11-21
- Pages
- 1-19
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W4416508416
- Cited by
- 7
- References
- 13
- Citations by year
- 2026: 7
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer Assisted Language Learning (publishedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- AntConc, large language models, corpus-based language learning, LLM hallucination mitigation, open-source LLMs, classroom language teaching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
