AI evidence extraction

Artificial Intelligence in English-Speaking Education: A WoS-Based Bibliometric Analysis of Technology, Pedagogy, and Ethics (2021-2025)

Bo Zhou, Lim Seong Pek, Nahdia Kabir, Jiaying Yang, Mohamed Bouteraa, Xizi He · International Journal of Learning Teaching and Educational Research · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.26803/ijlter.25.3.37

Methodology & findings

Study design

Bibliometric analysis using co-citation, co-occurrence, and temporal analyses of 248 Web of Science (WoS)-indexed articles published between 2021 and 2025 (2,046 citations; H-index = 24).

Sample

N = 248, 1 group

Primary method

Co-citation analysis, co-occurrence analysis, temporal analysis. Bibliometric metrics reported include H-index = 24 and 2,046 total citations across the 248 articles.

Main result

The study identifies that research "shift from an early emphasis on speech technologies to learner-centered, pedagogically grounded, and affect-sensitive approaches." Additionally, "Ethics-related terms do not appear among high-frequency or high-centrality keywords, despite their growing relevance in AI-enhanced learning," revealing a critical gap in the literature on AI-assisted English-speaking education.

Reports effect sizes.

Research paradigm

Positivist/Bibliometric

Author conclusions

The authors conclude that "the study advances an integrative perspective linking technology, pedagogy, and learner experience, with implications for teachers, researchers, and policymakers," and that "the findings align with UNESCO's SDG 4, highlighting the need for inclusive and equitable AI-supported speaking education."

Risk of bias

Database selection bias: Study limited to WoS-indexed articles, potentially excluding relevant research from other databases (Scopus, ERIC, etc.); Language bias: Focus on 'English-speaking education' may exclude non-English literature; Temporal bias: Narrow publication window (2021-2025) may not capture long-term research trends; Indexing bias: WoS indexing criteria may systematically exclude certain types of research or journals; Database selection bias (WoS-indexed articles only; may exclude relevant publications in other databases); Language bias (English-speaking education may skew toward Anglo-American perspectives); Publication bias (peer-reviewed articles may overrepresent successful or positive findings); Temporal bias (2021-2025 window may miss foundational work)

Limitations

The study reveals "a fragmented use of theoretical models" and notes that "Frameworks such as the Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Self-Determination Theory (SDT) are typically applied in isolation rather than being integratively linked to instructional design." Additionally, the paper identifies that "ethical and governance concerns remain insufficiently addressed" in the reviewed literature.

Open questions raised

Under-theorization of ethics and governance in AI-enhanced learning contexts
Insufficient attention to ethical and governance concerns despite their growing relevance
Fragmented use of theoretical models—frameworks such as TAM, SCT, and SDT are applied in isolation rather than being integratively linked to instructional design
Limited integrative linking between technology, pedagogy, and learner-related dimensions
The study identifies under-theorization of ethics and governance in AI-enhanced learning, fragmented use of theoretical models (TAM, SCT, SDT applied in isolation rather than integratively linked to instructional design), and the need for more ethically grounded approaches to AI-assisted English-speaking education.
Insufficient attention to ethics and governance in AI-enhanced learning

Data: not_statedCode: not_statedExtracted from: pdfAgreement 68%

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