This research addresses the critical gap in detecting LLM-generated content in Korean language, where traditional English-focused detection methods fall short.

Key Findings:

• Developed specialized detection approach for Korean text based on linguistic features
• Identified unique morphological and syntactic patterns that differentiate AI from human writing
• Demonstrated the importance of language-specific detection methodologies
• Contributes to academic integrity and copyright protection strategies

Security Implications: As LLMs become more sophisticated, language-specific detection tools are essential for identifying AI-generated content that could be used for misinformation, plagiarism, or other deceptive practices in non-English contexts.

Detecting LLM-Generated Korean Text through Linguistic Feature Analysis