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Computer Assisted Language Learning
《计算机辅助语言学习》是一本SSCI一区及A&HCI双检索期刊，最新影响因子6.0，在语言学期刊中排名第一，(CALL) 致力于发表学术成果，增进我们对以技术为媒介的语言学习过程的理解。语言学习并不是期刊收稿关注点。本期刊接受率14%，投稿至初审约5天左右，录用后待刊周期约为11天左右。
Guest Editors
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Corpus-Based Language Pedagogy in the AI Era
Qing Ma：
She is an A ciate Professor in the Department of Linguistics and Modern Language Studies and currently holds the position of Associate Dean (Research & Postgraduate Studies) in the Faculty of Humanities at The Education University of Hong Kong. Her research primarily focuses on second language vocabulary acquisition, corpus linguistics, and the application of technology in language learning, including corpus-based language pedagogy (CBLP), computer-assisted language learning (CALL), and mobile-assisted language learning (MALL). maqing@eduhk.hk
Peter Crosthwaite：
Associate Professor in the School of Languages and Cultures at UQ (since 2017), formerly assistant professor at the Centre for Applied English Studies (CAES), University of Hong Kong (since 2014). He holds an MA TESOL from the University of London and an M.Phil/Ph.D in applied linguistics from the University of Cambridge, UK.
Special Issue Information
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The use of corpora for language learning, traditionally known as “data-driven learning” (DDL, Johns, 1991), is a now well-established area of research in applied linguistics with over 350 empirical studies charting its efficacy for the acquisition of vocabulary, grammar and discourse (Boulton & Vyatkina, 2021). Attention is now turning from whether DDL “works” to determining the appropriate pedagogical approaches for its implementation, including considerations regarding the design of DDL specific corpora and software (e.g., Crosthwaite & Baisa, 2024; Crosthwaite & Anthony, in review), developing corpus literacy in current and potential DDL practitioners (Mukherjee, 2006; Ma et al., 2023), and the key steps involved in effective classroom implementation have been term as corpus-based language pedagogy (CBLP) (Ma et al., 2022; 2023; 2024a, b).
However, a paradigm shift in the way that we conceptualize and interact with language data is now manifested in the advent of AI and more recently, generative AI and the large language models (LLMs) underpinning this technology. Freely available to the general public, these powerful AI tools have already fundamentally impacted the way that we consider language pedagogy across all educational sectors from primary school to higher education and beyond, with applications including vocabulary and grammar (Baskara & Mukarto, 2023; Bezirha & Davier, 2023), conversational tutors (Ng et al., 2024), automated assessment (Mizumoto & Eguchi, 2023), languages other than English, academic writing (Yuan et al., 2023), and second language writing (Barrot, 2023). Despite its potential in language education, generative AI faces several challenges, including issues regarding integrity, plagiarism, data privacy, copyright, etc.
Where does this situation leave corpus technology and its applications on language education as a field of research? And, how will the advent of AI and LLMs impact our forays into bringing corpus technology into the language classroom? If the value of corpus technology was in providing access to authentic language data at a large scale, has this been negated in the switch to statistical language models comprising billions of permutations trained on trillions of words of language data across all major world languages? What is the ongoing value of corpus literacy if users can now type exactly what they are looking for as a natural language prompt (receiving “the answer” as a natural language output), without having to spend time concordancing? And what is the future of corpus-based language pedagogy as educational boards around the world are racing to implement (or defend against) generative AI in classroom practice? Can existing and emerging powerful AI tools co-work with corpus technology to achieve better language learning or teaching results?
The field of corpus linguistics is already beginning to answer the call for calm and clarity. For example, Crosthwaite and Baisa (2023) discuss currently limitations of generative AI, including issues around safety, user data privacy, AI hallucinations, passive learning, etc.. However, these disadvantages inherent to generative AI are the advantages of corpus technology (e.g., safe, authentic, human-generated data, active learning, etc.). It makes sense to combine AI and corpus technology and the combination may further empower our language teachers and learners. Quick off the mark, Anthony (2024) has already added LLMs support to the latest version of the ever popular AntConc corpus tool, with the potential to overcome several oft-cited complaints about DDL including the complexity of concordance lines and generating appropriate corpus queries. Lin (2023) has already demonstrated how ChatGPT can be used as a concordancer for use with DDL activities, with promising results. Mizumoto (2023) has clarified how generative AI and large language models can be combined with corpus tools and other language related software as part of a framework of Metacognitive Resource Use guided by users’ self-awareness, task specifics and associated strategies. In practice, Ma (2024) has recently explored how generative AI and other AI tools can be wavered into CBLP lesson designs to empower students’ learning in a popular online workshop series.
Themes：
It is now time, however, to bring together corpus technology, language teachers and language learners in discussion regarding the future of corpus-based language pedagogy in the AI era. This proposed special issue of Computer Assisted Language Learning, guest edited by Qing Ma and Peter Crosthwaite, aims to create a platform for leading researchers to explore how language learning and teaching can be enhanced through the integration of corpus technology, AI, and LLMs. Related topics to be covered by this special issue include (but are not limited to):
language teacher education
computer/mobile assisted language learning
second language writing
second language assessment
languages other than English
register/ genre education
language skills (e.g., vocabulary, grammar, reading, speaking, etc.)
collaborative learning
design-based learning
Submission Information
/// WE NEED YOU ///
Contributions should outline the role and value of corpus technology in the teaching and learning of these topics, together with a discussion of the potential impact - either positive or negative - of AI/LLMs on this practice. Empirical, propositional and meta-analysis contributions are acceptable. All submissions must conform with the submission guidelines and the aims and scope of the journal in terms of length of manuscripts, relevance, and research methodology. Please check the relevant journal pages for this information.
Important Dates：
Potential contributors should prepare a 300 word abstract of their proposed contribution, in line with the scope of the call outlined above. This should be submitted via e-mail to the SI Guest Editors Dr. Qing Ma (maqing@eduhk.hk) and Dr. Prof. Peter Crosthwaite (p.cros@uq.edu.au) by October 31, 2024. Full submissions must be submitted through the journal submission system by February 15, 2025, with revisions due by April 30, 2025.
This special issue will comprise an editorial (introduction), 8-10 contributed articles and a final commentary. Papers will be published Online First upon acceptance, expected around June 2025.
Instructions for AuthorsSubmit an Article
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本文编辑：孙雨 同济大学
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