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

Introduction This study proposes an integrated framework that enhances interactive behavior recognition and feedback optimization in medical teaching through the application of attention mechanisms. The approach centers on two core components: the Attention-Driven Interactive Behavior Recognition Model, which captures multimodal instructional interactions, and the Adaptive Feedback Optimization Strategy, which refines educator feedback in real time. Methods The behavior recognition model employs a multimodal encoder and attention-enhanced neural architecture to selectively prioritize salient audio, video, and textual cues within instructional sequences. By focusing on the most informative features and temporal patterns, it significantly improves the accuracy of recognizing learner engagement and instructional behaviors in complex teaching environments. Results and Discussion 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.