This session explores how AI tools and expert review can work together to improve distractor quality in licensing exams. It highlights how item performance data and regulatory requirements informed an iterative model-tuning process that produced increasingly plausible, fair, and functional distractors. Comparative item performance data across iterations will be shared, along with practical lessons for effective human–AI collaboration in regulated, high-stakes assessment.