AI-driven dynamic psychological measurement: correcting university student mental health scales using daily behavioral and cognitive data

AI-driven dynamic assessment provides superior accuracy for anxiety (SAS) and depression (SDS) scales over static methods by effectively capturing psychological fluctuations. The significant reductions in clinically-rated anxiety and depression suggest the system may function as an integrated assessment-intervention loop, fostering self-awareness through continuous feedback. High user engagement confirms the method's feasibility. However, the model's diminished specificity for the complex SCL-90 scale over time highlights challenges in handling intricate, long-term symptom patterns. This research supports a shift towards continuous "digital phenotyping" and underscores the need for rigorous validation, multimodal data integration, and robust ethical considerations.