A MIXED METHODS MAPPING OF AI AND LLM ENHANCED LANGUAGE LEARNING RESEARCH IN HIGHER EDUCATION FROM 2008 TO 2025

Wang Feng, Shah Said Hussain, Hassan Esraa Abdelnaser Mohamed

Abstract


Artificial intelligence (AI), especially large language models (LLMs), is increasingly transforming higher education, offering opportunities for personalized learning, adaptive assessment, and innovative instructional design. This study maps research on AI- and LLM-Enhanced Language Learning in higher education (AIHEd) from 2008 to 2025, using bibliometrix, structural topic modeling (STM), and VOSviewer network analyses. With bibliometric analyses, publication results show modest production before 2015, steady growth from 2016 to 2022, and a sharp surge after 2023, peaking at 1,078 articles, reflecting accelerating interest in AI and LLMs in higher education. Research articles are mainly published in journals such as Education and Information Technologies, BMC Medical Education, and Interactive Learning Environments, with institutions including Monash University and the University of Hong Kong serving as major collaboration hubs. China leads in total output, while the US and UK demonstrate higher per-article impact. Highly cited studies published in 2023–2024 cluster around generative AI, learning analytics, and educational technology. Keyword analyses reveal a clear thematic progression from foundational AI (2008–2013), to data-driven and personalized learning (2014–2019), and to generative AI applications (2020–2025). STM identifies eight latent topics spanning technical development and learner-centered pedagogy. VOSviewer co-occurrence networks further reveal four major clusters, namely, technological innovation, pedagogical integration, ethical and policy issues, and AI literacy and adoption, highlighting the growing prominence of ethical and responsible AI use alongside technological advancement. By mapping publication trends, thematic structures, and collaboration networks, the findings provide actionable guidance for policymakers, institutions, and educators in curriculum development, research planning, and ethical decision-making in AIHEd.

Keywords


AI, higher education, bibliometrix, STM, VOSviewer

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DOI: http://dx.doi.org/10.46827/ejes.v13i8.6901

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