This paper evaluates an AI-based simulated administration approach for estimating item difficulty before empirical calibration. AI-generated option probabilities for synthetic examinees were converted into item difficulty estimates and compared with operational Rasch difficulty statistics. Results showed meaningful alignment, highlighting simulated administration as a potential tool for earlier item performance evidence.