Call for Papers for Special Issue on Generative AI and Listening Skills

2026-06-06

Theme: 

Generative AI and Listening Skills

Guest Editors:

Joshua Matthews (University of New England)
Glenn Stockwell (The Education University of Hong Kong)

Overview:

The emergence of generative artificial intelligence (GenAI) has been a transformative force in L2 language education since late 2022, attracting widespread attention for its potential to reshape language teaching and learning. While this surge has led to a rapid expansion of research in AI-assisted language learning, the focus has been disproportionately oriented toward L2 writing. In contrast, L2 listening, despite its fundamental role in language development, has received comparatively limited attention (Crompton et al., 2024; Mohebbi, 2024).

This imbalance is not entirely unexpected. Historically, listening has been underemphasised within the broader field of language learning, famously characterised as the “Cinderella” of the macro-skills (Vandergrift, 1997). This marginalisation is closely tied to the inherent complexity of the listening construct and the challenges associated with operationalising it in pedagogical and assessment contexts. Listening involves the active construction of meaning through the dynamic integration of linguistic (bottom-up) and non-linguistic (top-down) knowledge sources within socioculturally situated contexts (Aryadoust & Luo, 2022). Critically, much of this processing occurs internally within the learner’s cognitive system, making it difficult to directly observe and measure with precision.

Against this backdrop, the potential of GenAI to support L2 listening development is particularly compelling, and emerging research suggests that such tools can play a valuable role. Recent studies indicate that GenAI-supported environments may foster immersive listening experiences, support comprehension development, reduce listening-related anxiety (Xiao, 2025), and improve L2 listening comprehension through interaction with intelligent personal assistants (Tai & Chen, 2024). GenAI has also shown promise in assessment approaches designed to support listening development (Abdellatif, 2024). Despite these positive indications, significant gaps remain, with the role of GenAI in L2 listening development comparatively under-theorised and under-examined (Crompton et al., 2024).

Addressing this gap requires research that explicitly engages with the L2 listening construct and establishes principled links between AI-mediated interventions and the cognitive and metacognitive processes underpinning successful comprehension. This research agenda is particularly pertinent for listening, given its enduring pedagogical challenges, historical marginalisation, and continued underrepresentation in AI-focused research (Goh & Aryadoust, 2025).

This special issue seeks to address these gaps by bringing together research at the intersection of generative AI and L2 listening. The aim is to advance a more coherent and evidence-based understanding of how GenAI can support listening development across diverse contexts, learner populations, and instructional approaches. In doing so, the issue aims to contribute to both theoretical advancement and practical innovation in AI-mediated language learning.

Topics of Interest:

We invite submissions on, but not limited to, the following areas:

  • Integration of GenAI tools in L2 listening instruction and feedback
  • Conversational AI and spoken interaction systems as environments for listening development
  • Cognitive, metacognitive, and affective processes in AI-mediated L2 listening
  • The role of GenAI in reducing listening anxiety and enhancing learner engagement
  • Assessment of L2 listening and aural vocabulary knowledge using AI-supported approaches
  • Formative and adaptive assessment of listening using GenAI technologies
  • AI-supported feedback mechanisms and their impact on listening development
  • Comparative studies of GenAI-based and traditional listening instruction
  • Longitudinal research on GenAI and L2 listening development
  • Teacher practices, perceptions, and professional development in AI-supported listening pedagogy
  • Cross-linguistic and cross-cultural perspectives on AI-mediated listening
  • AI-generated listening materials and their impact on L2 listening

We welcome diverse manuscript types, including empirical research articles, systematic reviews, case studies, and theoretical papers. We particularly encourage submissions that provide robust empirical evidence, offer transferable frameworks applicable across diverse contexts, include learner perspectives, address ethical considerations, and contribute to our theoretical understanding of AI-mediated language learning.

Timeline:

  • Abstract Submission Deadline: July 15, 2026
  • Notification of Acceptance: August 15, 2026
  • Full Manuscript Deadline: February 28, 2027
  • Expected Publication: Mid 2027 [Continuous online publication]

Journal:

This special issue will be published by the Australian Journal of Applied Linguistics (AJAL). AJAL is a peer-reviewed international open access journal focussing on all areas of applied linguistics, indexed in Scopus, DOAJ, and ERIC. For additional information regarding the journal, please visit:
https://www.castledown.com/journals/ajal

Submission and Inquiries:

We invite you to submit a proposal/abstract of no more than 500 words using APA 7th style in MS Word format (.doc/.docx). Proposals/abstracts should detail the area of focus, the research gap being addressed, the research design and methodologies used, and key findings or expected contributions related to the central theme of the special issue. Identifying information, including name of author(s), affiliation(s), contact information for all author(s), and a 100-word biographical statement for each author, should be included in the proposal. Based on the review of the proposals, authors will be invited to submit complete manuscripts for possible inclusion in the special issue. Authors’ guidelines will be included in the invitation letters.

For this special issue, please submit your proposals and inquiries directly to Joshua Matthews (jmatth28@une.edu.au).

References

Abdellatif, M. S., Alshehri, M. A., Alshehri, H. A., Hafez, W. E., Gafar, M. G., & Lamouchi, A. (2024). I am all ears: Listening exams with AI and its traces on foreign language learners’ mindsets, self-competence, resilience, and listening improvement. Language Testing in Asia, 14, Article 54. https://doi.org/10.1186/s40468-024-00329-6

Aryadoust, V., & Luo, L. (2022). The typology of second language listening constructs: A systematic review. Language Testing, 40(2), 375–409. https://doi.org/10.1177/02655322221126604

Crompton, H., Edmett, A., Ichaporia, N., & Burke, D. (2024). AI and English language teaching: Affordances and challenges. British Journal of Educational Technology, 55(6), 2503–2529. https://doi.org/10.1111/bjet.13460

Goh, C. C. M., & Aryadoust, V. (2025). Developing and assessing second language listening and speaking: Does AI make it better? Annual Review of Applied Linguistics, 45, 179–199. https://doi.org/10.1017/S0267190525100111

Mohebbi, A. (2024). Enabling learner independence and self-regulation in language education using AI tools: A systematic review. Cogent Education, 12(1), 2433814. https://doi.org/10.1080/2331186X.2024.2433814

Tai, T. Y., & Chen, H. H. (2024). The impact of intelligent personal assistants on adolescent EFL learners’ listening comprehension. Computer Assisted Language Learning, 37(3), 433–460. https://doi.org/10.1080/09588221.2022.2040536

Vandergrift, L. (1997). The Cinderella of communication strategies: Reception strategies in interactive listening. The Modern Language Journal, 81(4), 494–505. https://doi.org/10.1111/j.1540-4781.1997.tb05517.x

Xiao, Y. (2025). The impact of AI-driven speech recognition on EFL listening comprehension, flow experience, and anxiety: A randomized controlled trial. Humanities and Social Sciences Communications, 12, Article 4672. https://doi.org/10.1057/s41599-025-04672-8