Autonomous Agents and Agentic AI in Education: A Bibliometric Analysis of Thematic Evolution and Emerging Learning Ecosystems
DOI:
https://doi.org/0.65222/VIRAL.2026.7.42.62Keywords:
Abstract
Artificial intelligence has become an increasingly influential force in educational transformation, moving beyond automation and decision support toward systems capable of autonomous action, adaptation, and collaboration with human actors. The rapid emergence of agentic artificial intelligence and autonomous educational agents has generated a fragmented and rapidly expanding body of literature spanning educational technology, learning analytics, intelligent tutoring systems, human-computer interaction, and AI governance. Despite this growth, a comprehensive understanding of the intellectual foundations, thematic evolution, and emerging research frontiers of autonomous agents in education remains limited.
This study presents a bibliometric analysis of research on autonomous agents in education published between 2020 and May 2027. Bibliographic records were retrieved from the Scopus database using a structured search strategy covering autonomous agents, agentic AI, intelligent agents, pedagogical agents, and AI tutors in educational contexts. The dataset was analysed using Biblioshiny and VOSviewer through a combination of performance analysis and science mapping techniques, including annual scientific production, country and source analyses, co-citation networks, keyword co-occurrence mapping, thematic evolution analysis, and trend topic identification.
The findings reveal an accelerated growth trajectory after 2023, corresponding with the diffusion of generative AI and increasingly autonomous educational systems. Three major evolutionary phases are identified: technological experimentation, educational integration, and the emergence of agentic learning ecosystems. The intellectual structure of the field is anchored in intelligent tutoring systems, learning analytics, and human-computer interaction, while recent research increasingly concentrates on explainable AI, learner agency, human-AI collaboration, ethical governance, and multi-agent educational environments.
The study proposes an evolutionary framework that conceptualises the transition from educational technologies to collaborative educational actors and identifies future research priorities related to human-AI co-learning systems, educational governance, autonomous assessment, and lifelong personalised learning ecosystems. By integrating bibliometric evidence with conceptual synthesis, this study contributes a comprehensive understanding of how autonomous agents are reshaping contemporary educational research and practice.
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