AI-Integrated Intelligent Evaluation Method for Mental Health Education Teaching Effectiveness

Abstract

Evaluating the teaching effectiveness of mental health education is essential for improving instructional quality and enabling timely, targeted support for learners. Conventional evaluation approaches are often subjective, rely on a narrow set of quantitative indicators, and suffer from a time lag that limits their ability to support routine and personalized instruction. This paper proposes an AI-integrated intelligent evaluation method that leverages multi-source sensing to collect classroom behavioral signals, standardized psychometrics, student feedback text, and interaction trajectories. By combining natural language processing, machine learning, and affective computing, the method builds a unified quantitative assessment and intelligent inference model to dynamically estimate goal attainment, cognitive gains, and affective changes. The framework also generates actionable recommendations for teaching optimization and early intervention, while highlighting practical challenges such as data privacy, sample heterogeneity, and model transferability. We discuss privacy-preserving learning, multi-context training, and indicator-system design as key directions for robust deployment.