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April 23, 2026Journal of Korea Multimedia Society0 citationsOpen Access

Development of an LLM-based TOPIK Item Generation Model and Its Validity Verification

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HKHyunyoung KimSHSeunghoon HanJPJoo-Hyun Park

Key Points

  • This research aims to create an effective model for generating test items for the Test of Proficiency in Korean (TOPIK).
  • Developed an automatic item generation (AIG) model tailored to TOPIK standards.
  • Conducted evaluations with Korean education experts to verify the model's performance.
  • Automated the creation of passages, answer options, and commentary for exam items.
  • The AIG model successfully generates mock exam items aligned with learning objectives.
  • The system improves quality by addressing common grammatical and structural issues in item generation.
  • Evaluation indicates high rates of alignment with TOPIK standards as confirmed by education experts.

Abstract

The Hallyu (Korean Wave) content industry has become a core axis of the global cultural market, spurring increased interest in Hangeul and the Test of Proficiency in Korean (TOPIK). However, following the 2020 Korean Standard Education Curriculum, the supply of official and private practice items remains limited, hindering the ability to secure sufficient mock exam materials. To solve this, this study successfully developed an automatic item generation (AIG) model and system that adheres to specialized TOPIK standards, including item-specific learning objectives. The system’s performance was rigorously verified through evaluations conducted by Korean education experts, confirming the alignment of generated items with their respective learning objectives. Critically, the model addresses grammatical and sentence structure errors resulting from the data sparsity of Hangeul in commercially available foreign LLMs. By automating the creation of all item components (passages, options, commentary, and attractive distractors) and ensuring they meet TOPIK’s inherent level-specific objectives, the AIG system significantly enhances the overall quality and completeness of mock exams.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69e9b62685696592c86eaee4https://doi.org/10.9717/kmms.2026.29.3.581
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