Theme:
Generative AI and Speaking Skills
Guest Editors:
Joshua Matthews (University of New England)
Glenn Stockwell (The Education University of Hong Kong)
Overview:
The emergence of generative artificial intelligence has fundamentally shifted the landscape of language education. Since the public release of ChatGPT in late 2022, these technologies have captured widespread attention for their potential to transform how we teach and learn languages. While writing has dominated much of the research into GenAI in language learning contexts (Su & Lin, 2024; Yilmaz, 2024), speaking skills have received considerably less attention despite their critical importance in developing communicative competence.
Recent studies have shown positive impacts of voice-based AI chatbots on language learning, particularly in developing speaking and listening skills, vocabulary knowledge, and oral communicative competence (Patil et al., 2024). Research indicates that GenAI can enhance students’ oral proficiency while reducing speaking anxiety and increasing willingness to take linguistic risks (Chen et al., 2025). Studies examining AI-based instruction have demonstrated significant improvements in speaking fluency, pronunciation accuracy, and overall oral performance (Cui & Zhang, 2025; Hsu et al., 2021). These tools function as always-available conversation partners, capable of providing immediate feedback and fostering dialogic learning through real-time interaction (Liu, 2025).
However, significant gaps remain in our understanding of how GenAI can be effectively integrated into speaking pedagogy. The field needs more empirical research to assess the actual impact of these tools on speaking development, clearer guidelines for ethical use, and better understanding of how different GenAI applications can target specific aspects of oral communication (Crompton & Burke, 2024). Automatic speech recognition (ASR) technologies show promise for pronunciation training, though more research is needed on their effectiveness across different learning contexts (Bashori et al., 2024). There is also a need for research that goes beyond perceptions to examine measurable learning outcomes, as much current research still focuses on attitudes rather than actual proficiency gains (Stockwell, 2024).
This special issue seeks to address these gaps by bringing together research on the intersection of generative AI and speaking skills development. We aim to provide a clearer understanding of how GenAI technologies can enhance oral communication competencies across diverse educational contexts, learner populations, and pedagogical approaches. By examining both the opportunities and challenges, this collection will offer evidence-based insights for educators, researchers, and policymakers.We invite submissions on, but not limited to, the following topics:
- GenAI applications in specific contexts (academic, professional, multilingual settings)
- Integrating GenAI tools in pronunciation instruction and automated feedback systems
- Conversational AI as speaking partners for fluency development and interaction practice
- GenAI applications for developing pragmatic competence and sociolinguistic awareness
- Comparative studies examining GenAI versus traditional speaking instruction methods
- Longitudinal investigations of GenAI’s impact on oral proficiency development
- GenAI’s effectiveness in reducing speaking anxiety and building learner confidence
- User experience research on speaking-focused GenAI interfaces and applications
- Teacher perceptions, attitudes, and experiences with GenAI speaking tools
- Ethical considerations including privacy, bias, and authentic assessment in AI-mediated speaking practice
- Professional development models for integrating GenAI in oral communication pedagogy
- Cross-linguistic and cross-cultural perspectives on AI-mediated speaking instruction
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: October 15, 2025
- Notification of Acceptance: November 15, 2025
- Full Manuscript Deadline: May 31, 2026
- Expected Publication: Late 2026 [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:
Bashori, M., Sutrisno, A., van Hout, R., & Strik, H. (2024). I can speak: Improving English pronunciation through automatic speech recognition-based language learning systems. Innovation in Language Learning and Teaching, 18(2), 443-461. https://doi.org/10.1080/17501229.2024.2315101
Chen, Y., Ke, N., Huang, L., & Luo, R. (2025). The role of GenAI in EFL speaking: Effects on oral proficiency, anxiety and risk-taking. RELC Journal. https://doi.org/10.1177/00336882251341072
Crompton, H., & Burke, D. (2024). AI and English language teaching: Affordances and challenges. British Journal of Educational Technology, 55(3), 1142-1159. https://doi.org/10.1111/bjet.13460
Cui, W., & Zhang, J. (2025). AI applications in language learning: Improving speaking fluency and pronunciation using chatbots and speech feedback. Interactive Learning Environments. https://doi.org/10.1080/10494820.2025.2546634
Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254. https://doi.org/10.1016/j.system.2024.103254
Hsu, T. C., Tsai, S. C., & Yu, P. T. (2021). Developing a chatbot for English conversation practice: A preliminary study on partner selection preference. Computer Assisted Language Learning, 36(4), 449-474. https://doi.org/10.1080/09588221.2021.1915283
Liu, W. (2025). Language teacher AI literacy: Insights from collaborations with ChatGPT. Journal of China Computer-Assisted Language Learning. https://doi.org/10.1515/jccall-2024-0030
Patil, A. G., Kazemian, S., & Shah, A. (2024). The use of artificially intelligent chatbots in English language learning: A systematic meta-synthesis study of articles published between 2010 and 2024. ReCALL, 37(1), 42-62. https://doi.org/10.1017/S0958344024000119
Stockwell, G. (2024). ChatGPT in language teaching and learning: Exploring the road we’re travelling. Technology in Language Teaching & Learning, 6(1), 2273. https://doi.org/10.29140/tltl.v6n1.2273
Su, Y., & Lin, C. (2024). Generative AI (GenAI) in the language classroom: A systematic review. Educational Technology & Society, 28(1), 156-172. https://doi.org/10.1080/10494820.2025.2498537
Yilmaz, R. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Heliyon, 10(6), e28516. https://doi.org/10.1016/j.heliyon.2024.e28516
Please visit the journal website to view the articles (open access) or to make a submission:
https://www.castledown.com/journals/ajal/
