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Can LLMs Reason About Attention? Towards Zero-Shot Analysis of Multimodal Classroom Behavior
arXiv – CS AI|Nolan Platt, Sehrish Nizamani, Alp Tural, Elif Tural, Saad Nizamani, Andrew Katz, Yoonje Lee, Nada Basit|
🤖AI Summary
Researchers developed a privacy-preserving AI system that analyzes classroom videos to understand student engagement using pose detection and gaze tracking, with data processed by the QwQ-32B-Reasoning LLM. The system deletes original video frames and retains only geometric coordinates to comply with FERPA privacy regulations.
Key Takeaways
- →The system uses OpenPose and Gaze-LLE to extract skeletal and attention data from classroom videos while immediately deleting original footage.
- →QwQ-32B-Reasoning LLM performs zero-shot analysis of student behavior patterns across lecture segments.
- →The pipeline ensures FERPA compliance by storing only geometric coordinates as JSON data rather than identifiable video content.
- →Preliminary results show promise for LLM-based multimodal behavior analysis but reveal limitations in spatial reasoning about classroom layouts.
- →Instructors can access insights through a web dashboard featuring attention heatmaps and behavioral summaries.
Read Original →via arXiv – CS AI
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