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The research aims to systematically analyze AI liberal arts education research in Korea using text-mining techniques.

Structural Analysis of AI Liberal Arts Education Research in Korea: A Text-Mining Meta-Analysis of AI Liberal Arts Education Research in Korea

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The research aims to systematically analyze AI liberal arts education research in Korea using text-mining techniques.
Meta-analysis of 137 KCI-indexed journal articles on AI general education published from 2018 to 2025.
Utilized natural language processing-based text mining methods.
Applied TF-IDF keyword analysis, Sentence-BERT embedding, and K-means clustering for thematic structure identification.
Identified six thematic clusters: instructional design using generative AI, AI literacy, teacher competencies, effect studies, technology acceptance, and AI ethics.
Generative AI-based instructional design accounted for the largest research proportion, while ethics-related studies were limited.
Inter-cluster relationships revealed coherent cycles between instructional design and effect studies.

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Meta-analysis maps thematic structures in AI liberal arts education research, suggesting curriculum design and professional development implications.

Ilbo Seo (2025) studied this question.

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Meta-analysis maps thematic structures in AI liberal arts education research, suggesting curriculum design and professional development implications.