Artificial Intelligence in Mobile-Interactive EFL Learning Environments: A Bibliometric Analysis

Bo Zhou; Seong Pek Lim; Nahdia Kabir; Mohamed Bouteraa; Asna Asna

doi:10.3991/ijim.v20i08.59677

Authors

Bo Zhou INTI International University, Nilai, Malaysia https://orcid.org/0009-0003-4218-8654
Seong Pek Lim INTI International University, Nilai, Malaysia https://orcid.org/0000-0002-0322-7572
Nahdia Kabir American International University – Bangladesh (AIUB), Dhaka, Bangladesh
Mohamed Bouteraa Qatar Foundation–American University of Qatar, Doha, Qatar https://orcid.org/0000-0003-4834-8973
Asna Asna University PGRI Kanjuruhan Malang, Malang, Indonesia https://orcid.org/0009-0006-0085-9968

DOI:

https://doi.org/10.3991/ijim.v20i08.59677

Keywords:

artificial intelligence, EFL learning;, interactive mobile technologies, interactive learning environments, bibliometric analysis

Abstract

In the past five years, artificial intelligence (AI) has become increasingly embedded in technology-enhanced and mobile-interactive English as a foreign language (EFL) learning environments. However, the overall structure of knowledge production in this domain remains poorly understood. This study therefore conducts a bibliometric analysis to map publication patterns on AI-mediated EFL learning environments. Peer-reviewed SSCI-indexed journal articles published between 2021 and 2025 were retrieved from the Web of Science (WoS) Core Collection; 311 eligible articles were analyzed. Descriptive performance indicators, citationbased mapping, and network analysis were applied to identify influential documents, authors, institutions, journals, and countries. The findings show an exponential rise in publications and citations, but this growth is uneven. Knowledge production is overwhelmingly concentrated in East Asia, particularly Mainland China and Hong Kong, while research is dominated by chatbot-mediated and mobile-interactive AI applications published in a small cluster of technology-enhanced and interactive learning journals. The field is expanding rapidly but remains conceptually narrow and geographically concentrated. This study provides a structural baseline for future inquiry and highlights the need to widen AI modalities, strengthen theory-linked constructs, and advance cross-regional comparative designs to support cumulative knowledge development in mobile-interactive EFL learning contexts.

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