Programme

Conference programme

TBA

Keynote speakers

A keynote speaker: Luciana Forti

Luciana Forti

Luciana Forti is Tenure-Track Researcher in Educational Linguistics at the Department of Modern Languages, Literatures and Cultures of the University of Chieti-Pescara, Italy. Her work explores various intersections between second language acquisition and corpus linguistics, with a particular focus on data-driven learning, learner corpus research, and phraseology. Her most recent publications address issues related to the use of learner corpora in the development of language teaching materials, the relationship between learner production and processing of word combinations, and the creation of open educational resources to promote the uptake of corpus use in non-academic teaching contexts. She teaches courses on second language teaching, corpus linguistics, and translation. In 2023, she published the monograph Corpus Use in Italian Language Pedagogy. Exploring the Effects of Data-driven learning (Routledge).

Whole Lotta LOTEs? How target languages other than English are forging the future of LCR
In rock music history, Led Zeppelin’s Whole Lotta Love is known for its iconic and instantly recognisable riff, built predominantly around one single chord. In learner corpus research (LCR) history, English as the target language was the main chord that dominated the initial development of the field, before target languages other than English (LOTEs) began their rise in second-generation LCR (Granger, 2024). While the dominance of English in applied linguistics at large may be an inevitable consequence of the “Englishisation” of education systems (Lanvers, 2024), analysing how LCR on target LOTEs is developing is essential to evaluate the generalisability of LCR methodology.
This keynote will present a systematic review aimed at foregrounding the emerging patterns in LCR focused specifically on target LOTEs. It will consider both scholarly publications and presentations given at the biannual LCR conference. It will show how target LOTEs do not represent merely an extension of the field but rather contain important seeds of innovation, stemming from the nature of language diversity. Facing (and overcoming) challenges concerning the annotation of languages characterised by rich morphology, agglutination, and non-alphabetic writing systems, for example, allows LCR to truly generalise the application of its methods.
In line with this year’s conference theme Forging the Future of Learner Corpus Research, this keynote will ultimately seek to identify how target LOTEs are contributing to forge the future of the field, by mapping the extent to which LCR is evolving from a single-chord song to a symphony with a “whole lotta” diverse voices.

References
Granger, S. (2024). From early to future learner corpus research, International Journal of Learner Corpus Research, 10(2), 247-279.
Lanvers, U. (2024). Language learning beyond English: Learner motivation in the twenty-first century, Cambridge University Press.

Agnieszka Leńko-Szymańska

Agnieszka Leńko-Szymańska is an Associate Professor at the Institute of Applied Linguistics, University of Warsaw. Her research interests lie at the intersection of second language acquisition, language teaching and assessment, and corpus linguistics, with a particular focus on learner corpus research. Her monograph Defining and Assessing Lexical Proficiency (Routledge, 2020) examines the construct of word use in L2 writing. She has published widely on the applications of corpora in language teaching and learning. She is currently Vice-President of the Learner Corpus Association.

Learner Corpus Research in Automated L2 Writing Evaluation: From Feature Engineering to GenAI Validation
The assessment of second language (L2) production is central to language education, yet challenging. The evaluation of learners’ writing and speaking involves complex judgements about linguistic complexity, accuracy, fluency, discourse organisation, task fulfilment and communicative effectiveness. These judgements are time-consuming, difficult to standardise, and prone to rater variability. Automated assessment has therefore been proposed as a way of increasing the efficiency, consistency, and scalability of assessment, in particular in high-stakes examinations.
This talk reviews the changing role of Learner Corpus Research (LCR) in automated L2 writing evaluation. It proposes a U-shaped trajectory in this relationship. In early automated writing evaluation systems, such as ETS’s e-rater, LCR played an important role: it helped to identify features (i.e. linguistic characteristics) that distinguished proficient from less proficient writing (Attali & Burstein, 2006). However, with the development of more recent approaches based on neural networks and deep learning, learner corpora were increasingly reduced to datasets for training and testing systems (Uto, 2021).
The rise of large language models marks a further turning point. GenAI systems can now assign scores, generate feedback, and justify their evaluations even without learner corpora as task-specific training datasets. At the same time, recent studies have begun to examine validity and reliability of GenAI-based scores for assessing English learner writing (Pack 2024). Yet validating such systems primarily against human ratings is insufficient, since human scores are themselves variable, scale-dependent, and shaped by rater and task effects (Gaudeau, 2025).
Drawing on my own recent empirical research, the talk argues that learner corpus research can now assume a renewed role: not simply as a source of features or training data, but as a framework for validating GenAI-generated scores. Corpus evidence can help determine whether AI scores reflect developmentally and linguistically meaningful properties of learner writing, or whether they overvalue surface linguistic features such as length, grammatical complexity or lexical sophistication.

References
Attali, Y. & Burstein, J. (2006). Automated Essay Scoring With e-rater® V.2. Journal of Technology, Learning, and Assessment, 4(3). Available from http://www.jtla.org
Gaudeau, G. (2025). Beyond the Gold Standard in Analytic Automated Essay Scoring. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 18–39, Vienna, Austria. Association for Computational Linguistics.
Pack, A., Barrett, A., & Escalante, J. (2024). Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability. Computers and Education: Artificial Intelligence, 6, 100234. https://doi.org/10.1016/j.caeai.2024.100234
Uto, M. (2021). A review of deep-neural automated essay scoring models. Behaviormetrika, 48(2), 459–484. https://doi.org/10.1007/s41237-021-00142-y

Susan Nacey

Susan Nacey is a professor of linguistics and currently Vice Dean for Research at the Faculty of Education at the University of Inland Norway (INN), and a member of INN’s research group ‘English Language in Use’. She researches metaphor and other features in a wide variety of text types in English and Norwegian, especially in written and spoken learner language. She is the author of ‘Metaphors in learner English’ (John Benjamins, 2013) and the co-editor of ‘Metaphor identification in multiple languages: MIPVU around the world’ (John Benjamins, 2019). Professor Nacey is also an Editor of the journal ‘Metaphor and the Social World’ and Associate Editor of ‘Metaphor and Symbol’. She led a team in the development of the Norwegian version of the Louvain International Database of Spoken English Interlanguage (LINDSEI), and contributed to the development of the TRAWL Corpus – Tracking Written Learner Language. See here for her profile in the Norwegian Research Information Repository and here for her personal webpage.

Learner corpus research and its pedagogical promise: From insight to action?
This keynote lecture traces recent developments in learner corpus research, with particular attention to the last few years, and highlights emerging trends in its pedagogical applications. It uses these trends to reflect on the evolving relationship between learner corpus research and classroom practice, and to ask how far LCR has thus far delivered on its pedagogical promise.
Learner corpus research has long been associated with pedagogical potential: it can deepen our understanding of learner language, support materials development and assessment practices, and inform classroom practice. Yet a persistent question remains about how far the field has moved from generating knowledge about learner language to making that knowledge meaningfully usable in educational contexts (see e.g. Callies, 2019; Chambers, 2015; Gilquin, 2023; Granger, 2009, 2015; Granger & Lefer, 2023; Götz, 2022; Götz & Granger, 2024; Meunier, 2002, 2016; Seidlhofer, 2002).
This keynote builds on a recently completed systematic review of LCR publications, which covered the decade up to 31 July 2023 and was presented at the 2024 LCR conference in Tartu (Karlsen & Nacey, 2026). This work focused on the characteristics and trends of LCR over the last decade with respect to pedagogical applications. Extending that work, the talk asks what has happened since, updating the review with publications from August 2023 to December 2025 to revisit the relationship between learner corpus research and pedagogical applications.
Pedagogical relevance is approached along a continuum running from 1) knowledge production to better understand learner language production and development, through 2) pedagogical implications extending theoretical contributions towards practical significance, to 3) indirect applications where learner corpora are used to create pedagogical resources, and finally to 4) direct applications of learner corpora in educational contexts, where teachers and/or learners interact hands-on with learner corpus data. In addition to application type, the analysis considers the types of learner corpora accessed, the L1s represented, the target languages involved, and the general availability of the corpora, making it possible to ask not only what learner corpora have been used for, but also how far work in the field has advanced along the path from research insight to pedagogical action.

References
Callies, M. (2019). Integrating corpus literacy into language teacher education: The case of learner corpora. In S. Götz & J. Mukherjee (Eds.), Learner corpora and language teaching (pp. 245–263). John Benjamins.
Chambers, A. (2015). The learner corpus as a pedagogic corpus. Cambridge University Press. https://doi.org/10.1017/CBO9781139649414.020
Gilquin, G. (2023). Written Learner Corpora to Inform Teaching. In E. Csomay & R. R. Jablonkai (Eds.), The Routledge Handbook of Corpora and English Language Teaching and Learning (1 ed., Vol. 1, pp. 281–295). Routledge. https://doi.org/10.4324/9781003002901-23
Granger, S. (2009). The contribution of learner corpora to second language acquisition and foreign language teaching: A critical evaluation. In K. Aijmer (Ed.), Corpora and Language Teaching (Vol. 33, pp. 13–32). John Benjamins. https://doi.org/10.1075/scl.33.04gra
Granger, S. (2015). The contribution of learner corpora to reference and instructional materials design. Cambridge University Press. https://doi.org/10.1017/CBO9781139649414.022
Granger, S., & Lefer, M.-A. (2023). Learner translation corpora: Bridging the gap between learner corpus research and corpus-based translation studies. International Journal of Learner Corpus Research, 9(1), 1–28. https://doi.org/https://doi.org/10.1075/ijlcr.00032.gra
Götz, S. (2022). Learner Corpora to Inform Testing and Assessment. In E. Csomay & R. R. Jablonkai (Eds.), The Routledge Handbook of Corpora and English Language Teaching and Learning (1 ed., Vol. 1, pp. 311–326). Routledge. https://doi.org/10.4324/9781003002901-25
Götz, S., & Granger, S. (2024). Learner corpus research for pedagogical purposes: An overview and some research perspectives. International Journal of Learner Corpus Research, 19(1), 1–38.
Karlsen, P. H., & Nacey, S. (2026). Pedagogical applications of learner corpora: A decade in review. In F. Farr & L. Murray (Eds.), The Routledge Handbook of Language Learning and Technology. Routledge.
Meunier, F. (2002). The pedagogical value of native and learner corpora in EFL grammar teaching. In S. Granger, J. Hung, & S. Petch-Tyson (Eds.), Computer Learner Corpora, Second Language Acquistion and Foreign Language Teaching (pp. 119–141). John Benjamins.
Meunier, F. (2016). Learner corpora and pedagogical applications. In The Routledge Handbook of Language Learning and Technology (pp. 376–387). https://doi.org/10.4324/9781315657899
Seidlhofer, B. (2002). Pedagogy and local learner corpora: Working with learning-driven data. In S. Granger, J. Hung, & S. Petch-Tyson (Eds.), Computer Learner Corpora, Second Language Acquistion and Foreign Language Teaching (pp. 213–234). John Benjamins.