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The Knowledge Graph and Evolutionary Trajectory of Artificial Intelligence in Educational Robotics: A Bibliometric Analysis (2016- 2025)

Yu-Shen Fang, a, Yu-Xi Liu, b

Abstract

This study uses bibliometric analysis, employing CiteSpace and VOSviewer to examine 313 articles indexed in the Web of Science Core Collection between 2016 and 2025. It systematically maps the knowledge graph, evolutionary trajectory, and emerging trends in the field of artificial intelligence in educational robotics. The findings reveal a paradigm shift from viewing robots as technological tools to viewing them as social partners, characterized by a three-stage evolution: validation of technical feasibility, integration into educational contexts, and reflection on learner- centered values. The knowledge network is fragmented—ten coexisting clusters, three fragmented domains, and a hollow center. A pronounced structural hole exists among the three major domains of technical implementation, educational application, and value-based reflection, with no effective mechanism for translating knowledge between them. Frontier trends are dominated by a technological-supply logic, while terms related to ethical reflection are entirely absent from burst- detection analysis, reflecting an institutional lag in which technological maturity outpaces normative development. Based on these findings, the study proposes integrated recommendations for academic communities, policymakers, and technology developers to advance the field toward a fair, humanistic, and sustainable educational future.

Keywords

bibliometric analysiseducational roboticsethical governancehuman-robot collaborationknowledge graphparadigm shift 1 Introduction

References

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