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Scientific Mapping of GenAI in English as a Foreign Language (EFL) Context

Qiyong Luo & Feifei Chen

International Journal of Technology and Human Interaction2025https://doi.org/10.4018/ijthi.384377article
ABDC C
Weight
0.37

What the paper says

This bibliometric analysis of 239 articles from 2016 to 2025 maps the trends, gaps, and challenges of generative artificial intelligence (GenAI) in English-as-a-foreign-language education. Since 2024, research has surged due to technological progress and student-centered learning trends. Asian countries, notably China, lead in output, driven by high English proficiency demands and proactive technology integration. Leading journals such as Education and Information Technologies, System, and Computer Assisted Language Learning dominate the scholarly landscape. Research themes highlight GenAI tools' effectiveness in language acquisition, while challenges such as academic integrity and the digital divide persist. Scholars advocate for teacher training in technology use and ethics. Future research should explore GenAI's role in cross-cultural interactions, marginalized engagement, and long-term impacts and acceptance among teachers and students. Promoting collaboration, ethical use, and pedagogical innovation would enhance GenAI's potential to meet the evolving needs of English-as-a-foreign-language learners.

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https://doi.org/https://doi.org/10.4018/ijthi.384377

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@article{qiyong2025,
  title        = {{Scientific Mapping of GenAI in English as a Foreign Language (EFL) Context}},
  author       = {Qiyong Luo & Feifei Chen},
  journal      = {International Journal of Technology and Human Interaction},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijthi.384377},
}

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Scientific Mapping of GenAI in English as a Foreign Language (EFL) Context

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.