35.2582, Calls: Applied Linguistics, Computational Linguistics/ CALICO - "Artificial Intelligence in Language Learning and Assessment" (Jrnl)

[The LINGUIST List]

LINGUIST List: Vol-35-2582. Mon Sep 23 2024. ISSN: 1069 - 4875.

Subject: 35.2582, Calls: Applied Linguistics, Computational Linguistics/ CALICO - "Artificial Intelligence in Language Learning and Assessment" (Jrnl)

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Date: 21-Sep-2024
From: Bryan Smith [bryansmith at asu.edu]
Subject: Applied Linguistics, Computational Linguistics/ CALICO - "Artificial Intelligence in Language Learning and Assessment" (Jrnl)


Call for Papers
Thematic Issue on Artificial Intelligence in Language Learning and
Assessment
CALICO Journal 43.3, October 2026

This thematic issue seeks to deepen our understanding of how AI
technologies, particularly machine learning and natural language
processing (NLP), can enhance language education and impact learner
outcomes. We invite original, methodologically rigorous, and
empirically sound studies that focus on learner outcomes and
demonstrate how AI tools, especially NLP tools powered by large
language models, can be strategically employed to meet the evolving
demands of language education.

While much of the current research on AI in education has focused on
the capabilities and perceptions of these tools, this issue aims to
shift the focus toward their tangible effects on learning outcomes and
pedagogical practices. With the rise of LLM technologies, language
educators have an unprecedented opportunity to reshape instructional
methods. We are seeking studies that provide evidence-based insights
into the use of such AI applications for achieving language learning
goals and improving learner performance.

We welcome contributions that examine the use of NLP tools across
various skill areas, including learner engagement, comprehension,
proficiency (both written and spoken), and pragmatic development.
Studies that specifically explore how LLM-powered NLP applications can
be used and combined most effectively (also with other resources) to
serve the established learning goals are particularly encouraged (see
Chun, Kern, and Smith’s (2016) heuristic #3). Additionally, we seek
research on the efficacy of NLP applications in supporting autonomous
language learning and how learners interact with these tools.
Submissions employing qualitative, quantitative, or mixed methods
approaches are welcome. Please note that studies based solely on
surveys or questionnaires will not be considered.

Potential areas of focus include, but are not limited to:
●       AI for accessibility and support for learners with special
needs
●       AI tools in collaborative language learning environments
●       AI-assisted learning in multilingual contexts
●       Autonomous or extramural language learning through AI
●       Construction and use of small-language model (SLM) tools in
language education
●       Development of AI-augmented teaching materials
●       Enhancing instructional practices with LLMs
●       Learner agency in using AI for language development
●       Redefining the role of the teacher in AI-supported classrooms
●       Teacher training and professional development in AI
integration
●       The role of LLMs in L2 assessment
The editors invite expressions of interest for potential inclusion in
the thematic issue by October 30th, 2024. Invitations for full
manuscripts will be sent to authors by November 15th, 2024. Full
manuscripts will be due to the editors on October 15th, 2025. All
manuscripts will be double-blind peer-reviewed. Please send your
expression of interest to bryansmith at asu.edu.

Submission Guidelines for Expressions of Interest:
Title: A provisional title for the proposed manuscript.
Abstract: A brief abstract (250–300 words) outlining the scope, aims,
methodology, and potential contribution of the research.
Key Contributions: A statement (1–2 sentences) summarizing the unique
contributions the manuscript is expected to make to the field of CALL.
Keywords: Include up to five keywords.
Author Information: Names, institutional affiliations, and contact
details of the author(s).

Chun, D. M., Kern, R., & Smith, B. (2016). Technology in language use,
language teaching, and language learning. The Modern Language Journal,
100(S1), 64–80. https://doi.org/10.1111/modl.12302



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