<p>Complete indicator weights.</p>

This study developed an evaluation system comprising five criterion-level dimensions, 12 sub-criteria, and 26 indicators. Interpretive Structural Modeling (ISM) was used to identify the hierarchical relationships among the indicators, and an expert-informed Bayesian network was subsequently established for probabilistic evaluation.

The results showed that the target node had a positive-state probability of 47%, indicating an overall medium level of development. Curriculum quality exhibited the highest positive-state probability among the sub-criteria. When the target node was set to the positive state, the core curriculum renewal cycle, supervision frequency in practical training, and supervisors’ technological updating capability showed comparatively high posterior probabilities, whereas research platform support emerged as a relatively weak node requiring further attention.