Interactive behavior recognition and feedback optimization strategy for medical teaching based on attention mechanism

Experimental evaluations across multiple medical education datasets demonstrate substantial improvements in recognition accuracy and feedback effectiveness compared with state-of-the-art methods. Building upon these recognition insights, the feedback optimization strategy dynamically adapts instructional responses through an iterative refinement process. It integrates attention-guided behavior assessments with domain-specific pedagogical knowledge to generate feedback that is contextually precise, adaptive, and aligned with evolving learning needs. Through weighted behavior evaluation and continuous parameter updating, the strategy ensures that feedback remains effective across diverse teaching scenarios. The integrated system improves real-time interpretability of teaching interactions, enhances learner engagement, and provides a scalable solution for intelligent medical education support. These advances contribute to more personalized instructional delivery, support timely pedagogical interventions, and promote better alignment between teaching strategies and learner progress. This work highlights the potential of attention-driven architectures to advance personalized instruction and sets the stage for further exploration of adaptive, data-driven teaching technologies.