We compare the traditional three-correct-in-a-row mastery rule with an adaptive, measurement-informed mastery model using ASSISTments Skill Builder data. The adaptive model produced more consistent decisions overall, especially early in learning, while rule-based mastery varied across skills, templates, and hint use, suggesting a need for context-sensitive formative decision frameworks.

Using Multi-Facet Rasch Modeling on 36,400 safety ratings of AI-generated conversations, we reveal significant racial disparities (Asian 39.1%, White 28.7% detection rates) and content-specific bias patterns. Simulations show that diverse teams of 8-10 members achieve 70%+ reliability versus 62% for smaller homogeneous teams, providing evidence-based guidelines for AI-generated content moderation.