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

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Human-Computer Interaction

Background:

• Medical education requires effective instructional strategies and timely feedback.
• Current methods for analyzing teaching interactions and providing feedback are often limited in scope and adaptability.
• The integration of advanced AI, specifically attention mechanisms, offers potential for enhanced analysis and feedback in complex learning environments.

Purpose of the Study:

• To propose an integrated framework for enhancing interactive behavior recognition and feedback optimization in medical teaching.
• To develop an Attention-Driven Interactive Behavior Recognition Model for capturing multimodal instructional interactions.
• To implement an Adaptive Feedback Optimization Strategy for real-time refinement of educator feedback.

Main Methods:

• Utilized a multimodal encoder with an attention-enhanced neural architecture to process audio, video, and textual cues.
• Developed an iterative feedback refinement process integrating attention-guided behavior assessments with pedagogical knowledge.
• Employed weighted behavior evaluation and continuous parameter updating for adaptive feedback generation.

Main Results:

• Demonstrated substantial improvements in recognition accuracy and feedback effectiveness compared to state-of-the-art methods on medical education datasets.
• The integrated system provides real-time interpretability of teaching interactions and enhances learner engagement.
• Achieved contextually precise, adaptive feedback aligned with evolving learning needs across diverse teaching scenarios.

Conclusions:

• The attention-driven framework significantly advances personalized instruction in medical education.
• The system offers a scalable solution for intelligent medical education support and timely pedagogical interventions.
• This work paves the way for further exploration of adaptive, data-driven teaching technologies.