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Journals / Anadolu Üniversitesi Eğitim Fakültesi Dergisi / 2026 / Volume: 10 Issue: 2

AI-assisted speaking in ELT: a bibliometric mapping of research gaps and a future research agenda (2023–2026)

Mustafa Özdere(Nigde Ömer Halisdemir Üniversitesi)0000-0001-7535-9024
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Publication Date
2026-09-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study provides researchers, practitioners, and policymakers with an actionable roadmap for advancing AI-assisted speaking instruction in ELT.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The rapid integration of artificial intelligence (AI) into English Language Teaching (ELT) has generated a fragmented research landscape regarding AI-assisted speaking instruction.
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Key Findings & Data:This study addresses this gap through a bibliometric analysis of 251 Scopus-indexed articles (2023–2026), complemented by a supplementary review of 42 ERIC-indexed studies.
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Conclusion & Impact:This study provides researchers, practitioners, and policymakers with an actionable roadmap for advancing AI-assisted speaking instruction in ELT.
🔬Design:Review & Systematic Survey
📍Scope:2023–2026 Verileri
⚙️Variables:AI-assisted speaking · Bibliometric analysis · TPACK

Abstract

The rapid integration of artificial intelligence (AI) into English Language Teaching (ELT) has generated a fragmented research landscape regarding AI-assisted speaking instruction. While individual studies report promising outcomes for chatbots and automatic speech recognition (ASR) systems, the field lacks a systematic mapping of trends, gaps, and future directions specific to ELT speaking contexts. This study addresses this gap through a bibliometric analysis of 251 Scopus-indexed articles (2023–2026), complemented by a supplementary review of 42 ERIC-indexed studies. Using VOSviewer for keyword co-occurrence analysis, we determined four research clusters: technical infrastructure (ASR, pronunciation, EFL), AI-ELT core, chatbot-mediated affective mediation (chatbot, anxiety, WTC), and pedagogical-affective factors (speaking, motivation, feedback). Overlay visualization revealed a temporal shift from technology-centric toward learner-centered, affect-sensitive research. Building on these findings, we propose a future research agenda comprising 15 research questions from the durability of AI-mediated affective gains to the effectiveness of ASR tools across diverse L1 backgrounds. To address the neglect of affective and ethical dimensions in AI-ELT teacher preparation, we introduce TPACK-A, an extension of the TPACK framework incorporating Affective Knowledge (AK) as a distinct teacher competency. This study provides researchers, practitioners, and policymakers with an actionable roadmap for advancing AI-assisted speaking instruction in ELT.

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Cite This Article

Özdere, M. (2026). AI-assisted speaking in ELT: a bibliometric mapping of research gaps and a future research agenda (2023–2026). Anadolu Üniversitesi Eğitim Fakültesi Dergisi, Volume: 10 Issue: 2. https://doi.org/10.34056/aujef.1942022

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