Technology-Mediated Language Education in the Era of Artificial Intelligence: Bridging Language Teaching/Learning and Computational Linguistic Research Using LLMs

Dear Colleagues,

The rapid advancement of artificial intelligence— in particular, large language models (LLMs)—is profoundly transforming technology-mediated language education. Across educational contexts, LLMs are reshaping how languages are taught, learned, assessed, and researched, while also redefining the role of digital technologies in language education more broadly. As a central component of generative artificial intelligence, LLMs combine interactive language generation with the large-scale modelling of linguistic data, enabling both pedagogical applications and advanced forms of linguistic analysis.

In language education, LLMs are increasingly adopted to support instructional practices such as content creation for language learning, automated assessment, personalised feedback, and interactive conversational practice that fosters learner engagement and autonomy. At the same time, they are being explored for core linguistic processing tasks relevant to educational contexts, including grammatical error detection and correction; lexical, syntactic, and discourse-level complexity modelling; language proficiency estimation; and the automatic annotation and analysis of learner corpora.

These pedagogical and computational dimensions may be explored either independently or in combination. Accordingly, this Special Issue welcomes contributions that focus on pedagogical uses of LLMs, linguistic processing and analysis of learner data, or bringing both perspectives together, paying particular attention to multilingual and second language contexts and to the responsible and ethical use of LLMs in education.

This Special Issue will advance research on the use of large language models in language learning, teaching, and language technology, addressing both opportunities and challenges associated with their educational application. It brings together pedagogical, linguistic, and computational perspectives, highlighting how LLMs contribute to instructional design, language development, feedback, assessment, and data-driven analysis of learner language, while also considering issues of evaluation, bias, fairness, and trust.

Contributions to this Special Issue may address one or more of the following themes:

• AI-mediated pedagogical design for language learning and teaching;
• Prompt-based interaction with LLMs in language education;
• Hybrid human–AI approaches to feedback and assessment for language learning;
• Use of LLMs for grammatical error detection and correction;
• LLM-based readability, linguistic complexity, and language proficiency modelling;
• AI-supported language assessment (AI-assisted and AI-resilient);
• LLMs for corpus analysis and learner language research;
• Multilingual and low-resource language processing using LLMs;
• Evaluation methods, metrics, benchmarks, and datasets for LLM-based language applications;
• Bias, reliability, transparency, and trust in LLM-based language systems;
• Teachers’ perspectives and professional practices in AI-supported language education;
• Critical AI literacy in language education.

We welcome empirical studies, conceptual and theoretical contributions, design-based research, and methodological papers related to language learning and teaching, including native, second, and foreign language learning, multilingual education, academic language development, and teacher education.

Dr. Sílvia Araújo
Dr. Micaela Aguiar
Prof. Dr. Elena Volodina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• large language models in language education
• AI-mediated language pedagogy
• hybrid human–AI approaches to feedback and assessment
• learner language corpus analysis using LLMs
• technology-mediated language learning
• critical AI literacy in language education