An AI-driven framework integrating predictive modeling and intervention strategies to enhance psychological health education among college students

The increasing incidence of psychological health challenges among college students highlights the limitations of traditional education and intervention methods. This study proposes and validates an AI-driven framework integrating predictive modeling and personalized interventions to enhance psychological health education. The framework employs a hybrid model combining LSTM, BiLSTM, and LightGBM to analyze multimodal academic, physiological, and speech-text data for predicting psychological health risk levels. An intelligent intervention system delivers real-time, tailored support through sentiment analysis and recommendation algorithms. The evaluation of 12,543 anonymous student records showed high predictive performance, with a recall rate of 0.93 for high-risk students, an F1-score of 0.91, and a macro average F1-score of 0.85. The results showed that the average academic performance of high-risk and low-risk students was 65.2 and 82.4 points, respectively. AI-driven interventions significantly improved academic results, particularly for moderate-risk students, showing an average gain of 5.1 points. These findings confirm the framework's efficacy in early risk identification and timely intervention, offering a scalable paradigm to improve mental well-being and academic success in higher education.