This study employs Generative AI and computational linguistics techniques to analyze the correspondence between modern Korean and Chinese HAN-character sounds. The research focuses on 5,978 characters categorized by difficulty levels, aiming to confirm systematic phonological correspondence patterns. Method: The study utilizes advanced computational methods to examine the phonological relationships between Korean and Chinese characters. It categorizes the characters based on difficulty levels and analyzes their sound patterns. Results: The research confirms high-consistency patterns in Korean onset-Chinese initial and Korean coda-Chinese final mappings. It also identifies complex relationships between Korean vowels and Chinese vowels. The study reveals that Chinese exhibits greater syllable type diversity compared to Korean. Additionally, it finds slightly higher correspondence rates for ‘basic’ characters compared to ‘advanced’ ones, though the overall difference is not substantial. Conclusions: Based on these findings, the study proposes language learning strategies that prioritize high-consistency patterns for foundational phonological correspondence. It recommends adopting gradual approaches for complex correspondences and incorporating phonological knowledge into education. This approach aims to help learners understand commonalities and differences between the two language systems. The research offers insights for Korean language education and HAN-character vocabulary learning. It suggests that consistent learning strategies can be developed regardless of character difficulty. Future research directions include developing AI-based personalized learning systems and conducting longitudinal studies on learners' acquisition of correspondence rules. This study introduces an innovative methodology integrating Generative AI with computational linguistics for phonological analysis. It potentially enhances HAN-character vocabulary education and represents a new paradigm for language education research.

1. 引言
2. 韓中現代漢字音對應研究與生成型 AI 技術 研究現狀 
    2.1 韓語和漢語的音節結構及韻體系 概觀
    2.2 韓中現代漢字音對應研究動 向
    2.3 生成型 AI 在語言 研究中的應用
3. 利用生成型AI 的韓中漢字音對應分析
    3.1 利用生成型 AI 的韓中漢字音對應方法論
    3.2 音節的構成要素對應結果
4. 結論與建議

• 林玄烈(경성대학교) | 임현열