This study aims to analyze research trends in Korean language education by systematically reviewing academic journal articles focusing on learner corpora. A total of 176 papers were examined to identify temporal, keyword, and categorical trends. Temporally, research emerged in the early 2000s, showed gradual growth from 2013 to 2017, and experienced a sharp increase from 2018 to 2023. Post-2023, the field transitioned from quantitative expansion to qualitative depth. Keyword analysis revealed “error analysis” as the most dominant theme. Categorically, studies on error and usage patterns evolved from classifying error types to emphasizing semantic and cognitive approaches, with growing attention to AI-driven error correction systems. Interlanguage development analysis primarily focused on grammatical category progression, predominantly employing longitudinal methodologies. Corpus-based dictionary compilation shifted from error documentation to practical dictionary development using large-scale error corpora. Finally, implications for each research category were synthesized based on the findings. This study provides a comprehensive overview of the evolving scholarly landscape in Korean learner corpus research, highlighting methodological advancements and future directions. (Changchun University)