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The Rise of Digital Tutors: A Bibliometric Exploration of Intelligent Agent-Supported Language Learning from 2005 to 2025

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Title: The Rise of Digital Tutors: A Bibliometric Exploration of Intelligent Agent-Supported Language Learning from 2005 to 2025
Language: English
Authors: Lujie Huang (ORCID 0009-0000-2703-7442), Ming Liu
Source: European Journal of Education. 2026 61(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 24
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Descriptors: Tutors, Electronic Learning, Intelligent Tutoring Systems, English (Second Language), Second Language Learning, Educational Research, Technology Uses in Education
DOI: 10.1111/ejed.70529
ISSN: 0141-8211
1465-3435
Abstract: The integration of intelligent agents into language education has fundamentally reshaped the landscape of English language learning, enabling personalised, adaptive and emotionally responsive instruction across diverse learning contexts. While the proliferation of agent-based technologies, such as chatbots, virtual tutors and embodied pedagogical agents, has generated substantial academic interest, a systematic mapping of this evolving research domain remains lacking. This study conducts a longitudinal bibliometric analysis of 774 peer-reviewed publications from 2005 to 2025, with a specific focus on intelligent agent-supported English language learning. Drawing on data from the Web of Science Core Collection and employing CiteSpace, VOSviewer and Bibliometrix, the study identifies major contributors, collaboration patterns, influential journals, thematic clusters and citation bursts. The analysis reveals three distinct developmental phases--techno-adaptive (2005-2010), algorithmic (2011-2019) and learner-centric (2020-2025)--and uncovers persistent geopolitical asymmetries, with China and the United States dominating scholarly output. Notably, research has increasingly shifted from technical feasibility studies to investigations centred on learner affect, intercultural competence and automated assessment in English as a foreign language settings. Despite these advances, ethical concerns, linguistic inclusivity and applications for low-resource learners remain underexplored. This study provides a comprehensive, data-driven synthesis of intelligent agent-supported language learning and offers critical insights to inform future research, pedagogical design and policy development in AI-enhanced English language education.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497974
Database: ERIC
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  Data: The Rise of Digital Tutors: A Bibliometric Exploration of Intelligent Agent-Supported Language Learning from 2005 to 2025
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  Data: <searchLink fieldCode="AR" term="%22Lujie+Huang%22">Lujie Huang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-2703-7442">0009-0000-2703-7442</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ming+Liu%22">Ming Liu</searchLink>
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: The integration of intelligent agents into language education has fundamentally reshaped the landscape of English language learning, enabling personalised, adaptive and emotionally responsive instruction across diverse learning contexts. While the proliferation of agent-based technologies, such as chatbots, virtual tutors and embodied pedagogical agents, has generated substantial academic interest, a systematic mapping of this evolving research domain remains lacking. This study conducts a longitudinal bibliometric analysis of 774 peer-reviewed publications from 2005 to 2025, with a specific focus on intelligent agent-supported English language learning. Drawing on data from the Web of Science Core Collection and employing CiteSpace, VOSviewer and Bibliometrix, the study identifies major contributors, collaboration patterns, influential journals, thematic clusters and citation bursts. The analysis reveals three distinct developmental phases--techno-adaptive (2005-2010), algorithmic (2011-2019) and learner-centric (2020-2025)--and uncovers persistent geopolitical asymmetries, with China and the United States dominating scholarly output. Notably, research has increasingly shifted from technical feasibility studies to investigations centred on learner affect, intercultural competence and automated assessment in English as a foreign language settings. Despite these advances, ethical concerns, linguistic inclusivity and applications for low-resource learners remain underexplored. This study provides a comprehensive, data-driven synthesis of intelligent agent-supported language learning and offers critical insights to inform future research, pedagogical design and policy development in AI-enhanced English language education.
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