An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training

1. 1Design a seven-dimensional Behaviorally Anchored Rating Scale to operationalize the assessment of on-camera oral presentation skills
2. 2Implement a three-layer interpretable feedback architecture to connect rubric-aligned multimodal scoring, audience-perceived expressive diagnostics, and retrieval-augmented feedback
3. 3Develop a multimodal scoring system to evaluate student presentations based on the BARS
4. 4Integrate audience-perceived expressive diagnostics to provide more nuanced feedback on student presentations
5. 5Evaluate the effectiveness of the Intelligent Tutoring System using metrics such as student engagement, learning outcomes, and user satisfaction

Researchers and developers in AI, education, and human-computer interaction can benefit from this system to create more effective and personalized learning experiences

💡 An interpretable closed-loop Intelligent Tutoring System can provide personalized and effective feedback to students developing on-camera oral presentation skills

🤖💡 Build an interpretable closed-loop ITS for multimodal affective feedback in async presentation training! 📚💻