An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training
Researchers have developed an interpretable closed-loop Intelligent Tutoring System (ITS) designed to enhance public speaking skills through multimodal feedback. The system utilizes an XGBoost model to analyze facial, vocal, textual, and oculomotor features from video segments, providing feedback aligned with a seven-dimensional rating scale. Trained on over 10,000 MOOC video segments, the ITS demonstrated scoring accuracy comparable to expert ratings and led to significant skill improvements in adult learners over a 30-day practice period. AI
IMPACT Demonstrates how AI can provide structured, interpretable feedback for skill development, potentially improving educational tools.