생성형 AI와 텍스트 마이닝을 활용한 기업교육 연구 동향 분석

초록

This study investigates the research trends and key topics in the academic journal "Corporate Education and Talent Research" by utilizing generative AI and text mining techniques. An analysis of papers published since the 2000s reveals that early research predominantly focused on learners and program development, while post-2010 studies have increasingly concentrated on organizational performance and job evaluation. Generative AI was employed to summarize research topics, methods, and subjects, demonstrating high accuracy and effectiveness, often comparable to or exceeding expert evaluations. The study also tracks changes in key topics over time, showing a shift from "learning" and "programs" in the early 2000s to "performance," "organization," and "evaluation" in the 2010s, with recent years focusing more on factors influencing educational outcomes. Furthermore, a comparison of LDA and Bertopic topic modeling techniques indicates that Bertopic, with its context-aware embeddings, provides more consistent and refined topic definitions. This research highlights the efficiency and reliability of AI-based analysis in academic studies, though it also notes the importance of careful prompt design and data quality in influencing AI performance. The findings suggest that integrating AI analysis with human expertise could enhance the accuracy and depth of research in corporate education.

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