An Analysis of Korean Error-Handling Patterns in Rule- and Statistics-Based and LLM-Based Spell Checkers and Their Pedagogical Implications

This study empirically analyzed the error-processing patterns of five Korean grammar correction tools (Naver, Daum, Bareun-Hangeul, Gemini 3, and ChatGPT 5.2) currently utilized in the writing environment as of 2026. The findings revealed that while conventional rule- and statistics-based systems were effective for orthographic corrections, they exhibited a technical plateau in deep sentence-level error processing. In contrast, Gemini 3, a Generative Large Language Model (LLM), demonstrated superior contextual understanding and sentence restructuring capabilities, achieving an accuracy rate exceeding 90% across all domains. However, beneath this performance leap, a new form of “technical deceptiveness”—characterized by grammatical hallucinations and post-hoc rationalizations where incorrect corrections are fluently justified—was identified. Based on these results, this study first proposes “Critical AI Literacy” education, which enables learners to understand the probabilistic nature of generative AI and question the authenticity of its suggestions. Second, it suggests a “Writer-led Verification Process” as a core direction for literacy education in the AI era, empowering writers to make final judgments by cross-referencing AI-generated texts with linguistic norms rather than being subordinated to technical convenience. This research holds pedagogical significance by providing an empirical foundation for learner-centered critical writing education through a comprehensive examination of the possibilities and limitations of LLMs.