Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study

Abstract

OpenAI o3 can generate radiological licensure items that align with national standards in terms of difficulty, factual correctness, and blueprint coverage. However, wording clarity and the pedagogical specificity of explanations were weaker and did not meet an adoptable threshold without further editorial refinement. These findings support a practical workflow in which LLMs draft syllabus-aligned items at scale, while faculty perform targeted edits to ensure clarity and formative feedback. Future studies should evaluate image-inclusive generation, use Application Programming Interface (API)-pinned model snapshots to increase reproducibility, and develop guidance to improve explanation quality for learner remediation.

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