Category: language

GLOTECH plenary 2025

Conference website: https://web.ua.es/es/dl2/glotech-2025

Plenary: Critical AI literacy (CAIL): language education and applied linguistics as central disciplinary areas

Download here the comptencies outlined in the Pérez-Paredes, P. Curry, N. & Ordoñana-Guillamón, C. (2025). Critical AI literacy for applied linguistics and language education students. Journal of China Computer-Assisted Language Learning (JCCALL). Special Issue Theme: Cultivating AI literacy in language education. https://doi.org/10.1515/jccall-2025-0005

Some references

Curry, N., & McEnery, T. (2024). Corpus linguistics for language teaching and learning: A research agenda. Language Teaching.

Curry, N., McEnery, T., & Brookes, G. (2025). A question of alignment–AI, GenAI and applied linguistics. Annual Review of Applied Linguistics.

Curry, N., Mark, G., Lee, H., McEnery, T., Burton, G., Clark, T., & Shin, D. (2024). Applying corpus research indirectly to language teaching materials and assessment development.

Cheung, L., & Crosthwaite, P. (2025). CorpusChat: integrating corpus linguistics and generative AI for academic writing development. Computer Assisted Language Learning, 1-27.

Eaton, S.E. Postplagiarism: transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. Int Journal Education Integrety 19, 23 (2023). https://doi.org/10.1007/s40979-023-00144-1

Galaczi, E., & Pastorino-Campos, C. (2025). Ethical AI for language assessment: Principles, considerations, and emerging tensions. Annual Review of Applied Linguistics. Advance online publication. https://doi.org/10.1017/S0267190525100081

Handley, Z. L. (2024). Has artificial intelligence rendered language teaching obsolete?. The Modern Language Journal.

Kern, R. (2021). Twenty-five years of digital literacies in CALL. Language, Learning & Technology.

Kern, R. (2025). New Literacies: A Historical Perspective. In The Palgrave Encyclopedia of Computer-Assisted Language Learning (pp. 1-5). Springer Nature Switzerland.

Lawrence, N. (2024). The Atomic Human .Understanding Ourselves in the Age of AI. Penguin.

Lévy, P. (2025). Symbolism, digital Culture and Artificial Intelligence. RED. Revista de Educación a Distancia, 25(81). http://dx.doi.org/10.6018/red.630211
Liu, Y., Hu, G. (2024). Research Trends in Applied Linguistics (2017–2021): A Scientometric Review of 42 Journals. In: Meihami, H., Esfandiari, R. (eds) A Scientometrics Research Perspective in Applied Linguistics. Springer, Cham. https://doi.org/10.1007/978-3-031-51726-6_3

Lusta, A., Demirel, Ö., & Mohammadzadeh, B. (2023). Language corpus and data driven learning (DDL) in language classrooms: A systematic review. Heliyon, 9(12).

Ma, Q. (2025). Corpus Literacy and Data-Driven Learning. In The Palgrave Encyclopedia of Computer-Assisted Language Learning (pp. 1-7). Cham: Springer Nature.

Ma, Q., Crosthwaite, P., Sun, D., & Zou, D. (2024). Exploring ChatGPT literacy in language education: A global perspective and comprehensive approach. Computers and education: Artificial intelligence, 7, 100278. https://doi.org/10.1016/j.caeai.2024.100278

McCarthy, M., McEnery, T., Mark, G. and Pérez-Paredes, P. (2021) Looking back on 25 years of TaLC: In conversation with Profs Mike McCarthy and Tony McEnery. In Pérez-Paredes, P. & Mark, G. (Eds.). Beyond concordance lines: applications of corpora in language education. John Benjamins, pp. 57–74. https://doi.org/10.1075/scl.102.03mcc

McEnery, T. (2025). Twenty years of Corpora. Corpora, 20(1), 1-2.

McEnery, T., & Wilson, A. (1997). Teaching and Language Corpora(TALC). ReCALL, 9(1), 5–14. doi:10.1017/S0958344000004572

McInnes, R. (2025, April 11). Resist the gen-AI-driven university: A call for reclaiming thought in learning and teaching. ASCILITE TELall Blog. https://blog.ascilite.org/resist-the-gen-ai-driven-university-a-call-for-reclaiming-thought-in-learning-and-teaching/

Mizumoto, A. (2023). Data-driven learning meets generative AI: Introducing the framework of metacognitive resource use. Applied Corpus Linguistics, 3(3), 100074.
Mohsen, M. A., Althebi, S., Alsagour, R., Alsalem, A., Almudawi, A., & Alshahrani, A. (2024). Forty-two years of computer-assisted language learning research: A scientometric study of hotspot research and trending issues. ReCALL, 36(2), 230–249. doi:10.1017/S0958344023000253

Ohashi, L., & Alm, A. (2025). Conversational AI Literacy. In The Palgrave Encyclopedia of Computer-Assisted Language Learning (pp. 1-6). Cham: Springer Nature.

The Open University. (2025). A framework for the learning and teaching of critical AI literacy skills. https://www.open.ac.uk/blogs/learning-design/wp-content/uploads/2025/01/OU-Critical-AI-Literacy-framework-2025-external-sharing.pdf

O’Sullivan, Í. (2007). Enhancing a process-oriented approach to literacy and language learning: The role of corpus consultation literacy. ReCALL, 19(3), 269-286.

Oshchepkova, T., Tolstykh, O. M., Panasenko, E. V., Nazarova, N. A., & Petrova, N. V. (2024). Examining changes in foreign language educators’ attitudes towards the use of computer-assisted learning. Studies in English Language and Education, 11(2), 630-649.

Pérez-Paredes, P. (2025). Corpus linguistics and Computer Assisted Language Learning. In Hilary Nesi and Petar Milin (Eds.) International Encyclopedia of Language and Linguistics, 3rd Edition. Elsevier.

Pérez-Paredes, P., Mark, G. & O’Keeffe, A. (2025). Corpus linguistics for language learning research. Research Methods in Applied Linguistics (RMAL). John Benjamins. URL.

Pérez-Paredes, P. Curry, N. & Ordoñana-Guillamón, C. (2025). Critical AI literacy for applied linguistics and language education students. Journal of China Computer-Assisted Language Learning (JCCALL). Special Issue Theme: Cultivating AI literacy in language education. https://doi.org/10.1515/jccall-2025-0005

Pérez-Paredes, P. & Boulton, A. (2025). Data-driven Learning in and out of the Language Classroom. Cambridge University Press. https://doi.org/10.1017/9781009511384

Pérez-Paredes, P. & Curry, N. (2025). Corpus linguistics in Languages for Specific Purposes (LSP). In Thorsten Roelcke, Ruth Breeze and Jan Engberg (Eds.), Handbook of Specialized Communication, pp. 407-432. De Gruyter. https://doi.org/10.1515/9783110672633-020

Pérez-Paredes, P. & Mark, G. (Eds.) (2021). Beyond concordance lines: applications of corpora in language education. John Benjamins.

Pérez-Paredes, P. & Ordoñana-Guillamón, C. (2025). Future challenges and opportunities for data-driven learning. In McCallum, L. & Tafazoli, D. (Eds.) The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer. https://doi.org/10.1007/978-3-031-51447-0_350-1

Pérez-Paredes, P. Curry, N. & Aguado Jiménez, P. (forthcoming). Enriching AI literacy with corpus-based pedagogy.

Seargeant, P. (2023). The future of language. Bloomsbury.

Stockwell, G. (2024). ChatGPT in language teaching and learning: Exploring the road we’re travelling. Technology in Language Teaching & Learning, 6(1), 2273. https://doi.org/10.29140/tltl.v6n1.2273

Son, J. B., Ružić, N. K., & Philpott, A. (2025). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning, 5(1), 94-112

Sun, W., & Park, E. (2023). EFL learners’ collocation acquisition and learning in corpus-based instruction: A systematic review. Sustainability, 15(17), 13242.

Sutoris, P. (2025, May 21). Our future may depend on the humanities. Wonkhe. https://wonkhe.com/blogs/our-future-may-depend-on-the-humanities/

Mike McCarthy on discovery our histories as language educators

Read the piece here.

Usage based and the emergence of L1

The following quotes are from Lieven, E. (2016). Usage-based approaches to language development: Where do we go from here? Language and Cognition,8(3), 346-368. doi:10.1017/langcog.2016.16

Young children show differential and restricted competence in comprehension and production early on; second, that children’s linguistic productivity is tied closely to their linguistic experience, but this interacts with processing capacity, the developing linguistic system, and children’s communicative goals; and, finally, that the development of more abstract grammar is protracted, and that differing levels of abstraction will give the ability to do different tasks

Children are exposed to many meaningful usage events which they can now begin to interpret in the context of this newly developing understanding of shared intentionality. Grammar is learned through a continuous process of abstraction. Constituency and more complex syntax emerge through this process.

In the usage-based approach, linguistic categories such as noun, verb, noun phrase, subject, and object are not pre-given but emerge as the child constructs language by connecting what they already know in terms of the cognitive and intention-reading developments of the first year to the language that they hear. 

The development of word categories is tied to children starting to develop low-scope slot-and-frames patterns based on the frequencies in the input. Examples from English are It’s X-ing, I want a Y, That’s a Z. The slots in these patterns are the basis of emergent categories, initially of low-semantic scope such as THING or ACTION but showing increasing evidence of abstraction.

Large numbers of studies, not only for English, have found that frequency in the input is closely associated with what children learn.

If something is very frequent in the input, but does not occur in the child’s speech, this suggests that there is something about the form in terms of complexity or meaning that is slowing learning.

An example comes from my study of six children’s learning of English auxiliaries (Lieven, 2008). There was a strong rank order correlation between the frequency of these in the input and the order in which they were found in the children’s speech, but there were a number of exceptions. Frames with could, would, and should were relatively frequent in the input, but in the period studied these emerged either late or not at all in the children’s speech. This is probably because these modals require a subtle semantics which the children did not yet control. Modals are a set of verbs that diverge from simple declarative sentences and questions about factuality, signalling a range of speaker stances towards the information being conveyed. Moreover, they are polysemous (being used to convey both speech acts and logical prediction), and in each usage they signal a slightly different range of speaker stances.

Although children start with rote-learned strings and low-scope schemas and may retain these into adulthood, they clearly also develop the capacity to produce and comprehend at a more abstract level. 

The evidence is that the youngest children can only correctly identify the agents and patients of transitive causatives if they are presented with a prototypical coalition of cues.

From the point of view of a usage-based account, one can see these results arising from two competing processes: the deep entrenchment of SVO word order (initially with low-scope pronoun schemas) which competes with the much less frequently encountered and highly specific pragmatic contexts in which OVS word order (even with case marking) is used. This latter usage requires a coalition of contextualizing cues for its interpretation…

there is evidence for the storage of ‘big words’. Bannard and Matthews (2008) showed that children did better on production of 4-word sequences that were frequent in the input than identical sequences in which the last word is changed. Second, there is good evidence for the importance of low-scope, pronoun-based schemas particularly in the early stages of sentence production (Ambridge & Lieven, 2014). We know that children are significantly more likely to correct non-grammatical word orders to canonical word order as they get older (Akhtar, 1999). When presented with novel verbs in non-canonical word order, younger children tend to use the same word order when asked to produce the sentence with different nouns. However, when children do change to the correct canonical order, they are very likely to use schemas based on pronouns (e.g., He’s meeking it; Abbot-Smith, Lieven, & Tomasello, 2001; Matthews, Lieven, Theakston, & Tomasello, 2004, 2007).

On the usage-based assumption that young children learn language in order to communicate, the relationship of form to meaning is obviously a crucial area for research. However, in research on the learning of syntax, there has tended to be more of a focus on structure than on meaning. I think this has been in reaction to the emphasis on abstract structure in generativist theory and the claim that children could not learn this structure from what they hear. Usage-based researchers have been concerned to show how children can indeed abstract a grammar from the language that they hear, and to argue that generativist theories are not able to solve the ‘linking problem’ of how the hypothesized Universal Grammar interacts with the input to produce the grammar of the specific language (Ambridge, Pine, & Lieven, 2014).

A great deal of empirical evidence has shown: (1) the strong relationships between the language that children hear and the course of their language development; and (2) that children’s language builds up from low-scope patterns and heuristics to an increasingly schematic and abstract network of constructions. To build a comprehensive and psychologically realistic account of children’s language development we now need to concentrate on identifying the processing mechanisms that are involved; to seriously address the relationship between meaning and form; to account for individual differences in learning; and to extend our research to languages that provide specific challenges to the present state of our theories.

Some extracts from Jean Coussins’ “English is not enough”

Source: (URL)

A NATIONAL RECOVERY PROGRAMME FOR LANGUAGES. A framework proposal from the All-Party Parliamentary Group on Modern Languages, published 4 March 2019

Some extracts

Britain, and its government, must remember that only six per cent of the world’s population are native English speakers and 75 per cent speak no English at all.

The amount of online content in English is declining, from over half in 2000 to about a quarter now. Over the same period, Mandarin content has increased from 5 per cent to well over 20 per cent and rising.

The decline in language learning in UK schools can now be felt right up the chain, through universities and into the business community.

Just last week, the BBC reported that language learning is at its lowest level in UK secondary schools since the turn of the millennium, with German and French falling the most.

In 1999, 342,227 took GCSE French while last year the number was just 126,750. Languages at A-level are in freefall: the year-on-year drop last year alone was 5.4 per cent.

Secondary school pupils in the UK spend less time studying languages than anywhere else in the developed world.

Over 70 per cent of UK employers say they’re unhappy with the foreign language skills of British school leavers and graduates and are forced to recruit from overseas to meet their needs.

Smart businesses who make use of languages report 43 per cent higher export/turnovers ratios.