Abstract In knowledge structure theory (KST) framework, this study evaluates the reliability of knowledge state estimation by introducing two key measures: the expected accuracy rate and the expected discrepancy. The accuracy rate quantifies the likelihood that the estimated knowledge state aligns with the true state, while the expected discrepancy measures the average deviation when misclassification occurs. To support the theoretical framework, we provide an in‐depth discussion of these indices, supplemented by two simulation studies and an empirical example. The simulation results reveal a trade‐off between the number of items and the size of the knowledge structure. Specifically, smaller structures exhibit consistent accuracy across different error levels, while larger structures show increasing discrepancies as error rates rise. Nevertheless, accuracy improves with a greater number of items in larger structures, mitigating the impact of errors. Additionally, the expected discrepancy analysis shows that when misclassification occurs, the estimated state is generally close to the true one, minimizing the effect of errors in the assessment. Finally, an empirical application using real assessment data demonstrates the practical relevance of the proposed measures. This suggests that KST‐based assessments provide reliable and meaningful diagnostic information, highlighting their potential for use in educational and psychological testing.