🤖 AI Summary
This study addresses depression and anxiety among university students stemming from career-related stress by proposing a privacy-preserving, culturally sensitive framework for early mental health risk detection. The approach integrates structured behavioral data with facial emotion cues extracted from interview videos, employing an attention-augmented intermediate fusion neural network for modeling. To enable collaborative training across institutions without sharing raw data, the framework leverages federated learning. Model interpretability is enhanced through Integrated Gradients and SHAP analyses, revealing key behavioral markers—such as gaze avoidance and reduced facial expressivity—that align with established psychological theories. Evaluated on a dataset of Pakistani university students, the model achieves 92.08% accuracy and an F1 score of 89.12%, demonstrating its effectiveness in identifying early signs of psychological distress.
📝 Abstract
Career anxiety and depression among university students present a growing challenge to mental health and academic achievement. This study proposes an Explainable AI (XAI) framework using multimodal data and Federated Learning (FL) to identify early indicators of career-related mental health problems in a privacy-preserving and culturally responsive manner. The framework combines structured behavioral data and facial emotion features from interview videos via an intermediate fusion neural network with attention mechanisms. Label smoothing was applied to improve model generalizability. FL was used across institutions to enable collaborative training without raw data sharing. Evaluation was conducted using the Student Mental Health Survey dataset from university students across Pakistan. Our model attained an F1-score of 89.12%, recall of 86.54%, accuracy of 92.08%, and precision of 91.88%. Using Integrated Gradients and SHAP, the model identified key behavioral markers of depression including avoidance of direct gaze, lower facial expressiveness, and social withdrawal, consistent with psychological theory. This research presents an interpretable, scalable, and context-sensitive AI system for mental health pre-diagnosis with potential integration into student support services globally.