ISSN : 1225-598X
Using KCI bibliographic data, this study bibliometrically investigates the diffusion patterns and knowledge structures of Artificial Intelligence (AI) research across academic disciplines in South Korea. Key terms were extracted via TF-IDF, and intellectual distances and content overlaps were measured using Cosine and Jaccard similarities along with Multidimensional Scaling (MDS). Knowledge openness was further evaluated through the E-I Index. Results indicate that domestic AI research entered a “quantitative expansion phase” post-2022, driven by interdisciplinary studies and the arts. Regarding knowledge structure, Social Sciences and Interdisciplinary Studies formed a “Dual-Hub.” Across all disciplines, a “Path-dependent Hybridity” emerged, where “instrumental universality” and “content specificity” coexist. Additionally, fields like Social Sciences and Engineering showed “convergence-led” structures, whereas Medical and Natural Sciences remained “specialized-depth.” These findings imply that Library and Information Science (LIS) should act as an architect of intelligent knowledge ecosystems by leading data governance. Consequently, the government should implement a “Two-track Strategy” that differentiates support based on these discipline-specific structural characteristics.