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LLM-Assisted Iterative Topic Modeling with Multi-dimensional Metadata and Human-in-the-Loop Validation: A Case Study of Metaverse Education

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(6), pp.99~114
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 13, 2026
  • Accepted : May 26, 2026
  • Published : June 30, 2026

Wan-Je Gil 1 In-Soo Shin 1

1동국대학교

Accredited

ABSTRACT

This study proposes an LLM-assisted iterative topic modeling framework that integrates multi-dimensional metadata structuring, multi-model consolidation, and human-in-the-loop validation. Research subjects, fields, and methods were extracted from academic documents, and LDA and BERTopic were applied in parallel to construct an initial topic set aligned with prior topic structures. The topic system was refined through selective cross-validation with heterogeneous LLMs, progressive sampling, and expert review. The finalized topic set was applied to the full corpus for distribution and trend analysis. The results show that metaverse education research has expanded around stable core themes rather than abrupt paradigm shifts. This study reconceptualizes topic modeling as an iterative process of semantic convergence and offers a structured empirical classification of metaverse education research.

Citation status

* References for papers published after 2024 are currently being built.

This paper was written with support from the National Research Foundation of Korea.