Can LLMs Reason About Attention? Towards Zero-Shot Analysis of Multimodal Classroom Behavior
Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We present a privacy-preserving pipeline that analyzes classroom videos to extract insights about student attention, without storing any identifiable footage. Our system runs on a single GPU, using OpenPose for skeletal extraction and Gaze-LLE for visual attention estimation. Original video frames are deleted immediately after pose extraction, thus only geometric coordinates (stored as JSON) are retained, ensuring compliance with FERPA. The extracted pose and gaze data is processed by QwQ-32B-Reasoning, which performs zero-shot analysis of student behavio...
| Authors | Nolan Platt, Sehrish Nizamani, Alp Tural et al. |
|---|---|
| Primary category | cs.HC |
| Published | 3 April 2026 |
| Last updated at source | 26 June 2026 |
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