Branislav Bédi, The Árni Magnússon Institute for Icelandic Studies, Iceland
Neasa Ní Chiaráin, Trinity College Dublin, Ireland
Manny Rayner, Adelaide University, Australia

Special issue

This special issue aims to explore the implications of Generative AI (GenAI) and their applications for the theory and practice of computer-assisted language learning (CALL) and the field of language education. As GenAI tools open up new possibilities, it is reasonable to expect that we are in the early stages of a major paradigm shift (O’Dea, 2024; Pegrum, 2025). In particular, GenAI services like OpenAI’s Advanced Voice Mode appear now to be close to the point where they can reliably support open-domain spoken dialogues in most widely spoken languages, giving helpful feedback to language learners if requested. Indeed, initial services of this kind are being trialled at some schools. This special issue seeks to gather empirical studies, theoretical analyses, meta-studies, replication studies, and practical applications that investigate how GenAI can be effectively utilised in CALL to enhance pedagogy in language learning and teaching, and teacher training (Bahari et al., 2025).

References
Bahari, A., Han, F., & Strzelecki, A. (2025). Integrating CALL and AIALL for an interactive pedagogical model of language learning. Education and Information Technologies, Advance Online Publication. https://doi.org/10.1007/s10639...

Ní Chiaráin, N., Robinson-Gunning, N., Nolan, O., & Comtois, M. (2023). Filling the SLaTE: Examining the contribution LLMs can make to Irish iCALL content generation. 9th Workshop on Speech and Language Technology in Education (SLaTE). Dublin: Trinity College Dublin. https://doi.org/10.21437/SLaTE...

O’Dea, X. (2024). Generative AI: Is it a paradigm shift for higher education? Studies in Higher Education, 49(5), 811–816. https://doi.org/10.1080/030750...

Pegrum, M. (2025). From revolution to evolution: What generative AI really means for language learning. Language Teaching, Advance Online Publication. https://doi.org/10.1017/S02614...

Call for abstracts

As a first step, we invite expressions of interest. We particularly encourage papers presenting responsible forward-looking projections of GenAI in CALL and/or addressing issues in less commonly spoken languages (Ní Chiaráin et al., 2023).

Please send your one-page expression of interest (preliminary title, 300–350 word abstract plus about 4–6 references) to the Guest Editors (ReCALL2027special@gmail.com) for informal feedback.

Special issue topics

We invite contributions addressing topics including but not limited to:

Assessing the limitations and challenges of implementing GenAI tools and smart chatbots to support individual and individualised language learning
The impact of GenAI on learners and teachers from diverse perspectives
The use of GenAI in less commonly spoken and Indigenous languages
The role of GenAI in promoting digital literacies and computer-mediated communication in language learning and teacher training
Application and integration of existing theories and concepts used in language learning to support the use of GenAI-powered tools in CALL
Ethical considerations in using GenAI-powered tools such as ChatGPT or similar tools for text, voice, and image generation in CALL
Review studies summarising the use of GenAI-powered tools and chatbots integration into language classrooms and online learning environments
Meta-studies with statistical evidence about the success rate of GenAI-powered tools in CALL
Exploring opportunities for effective human-GenAI collaboration in CALL
Responsible projections of likely short-term evolution of GenAI based CALL platforms.

Timeline

June 2025: Call for papers
30 September 2025: Deadline for submitting an expression of interest
31 January 2026: Deadline for submission of papers
31 January 2027: Deadline for finalising complete manuscript
1 May 2027: Publication

Instructions for authors

To submit your expression of interest (preliminary title, 300–350 word abstract plus about 4–6 references) or for any other questions relating specifically to the special issue and its content, please contact ReCALL2027special@gmail.com.

For any other queries, please contact recall.editorial@cambridge.org.

The usual Author instructions for ReCALL submissions apply to abstracts and full papers.