Timely state diagnosis is essential for supporting operators during abnormal situations in nuclear power plants. Although artificial intelligence–based diagnostic models have achieved strong performance, discussion on how their results should be provided to operators remains limited. This study focuses on the design of an operator-centered state diagnosis module that determines what diagnostic information should be presented and how it should be structured to support operator decision-making. Based on an analysis of operator cognitive activities during abnormal situations, the required diagnostic information is identified and organized in a sequential manner reflecting the operator reasoning process. Diagnostic results, diagnostic reasons, abnormal operating procedure entry condition consistency, and time margin information are hierarchically structured to reduce cognitive burden while supporting situation assessment. The proposed module illustrates how AI-based diagnostic outputs can be presented to assist operators in judging plant conditions and determining appropriate abnormal operating procedure entry.