AEED: Agentic Edge-AI Engagement Detection for K-12 Classrooms

Abstract

Monitoring classroom participation requires dependable, low-latency, private solutions that work within edge hardware constraints while maintaining meaningful learning connections. On-device inference is conceivable with edge-AI systems, but they rarely analyze input signal importance in real-world classroom scenarios and employ face orientation or gaze as crude proxies. The embodied cognition theory predicts that engagement should be represented in whole-body sensorimotor activity on specific tasks, not only in face configuration, but agentic classroom pipelines have not operationalized this requirement. In this article, we present agentic edge-AI engagement detection (AEED), a cross-disciplinary pipeline that leverages classroom deployment constraints to inform model design, data collection, and system development. In particular, AEED consists of BodyPosture (BP), a model trained on traditional camera-facing body-orientation cues, and FaceReaction (FR), a face model with labels derived from synchronized EEG, ECG, and task performance. In order to examine the impact of input design alongside architectural and platform characteristics, both models operate in parallel under the same tracking, hardware, and logging settings. The pipeline uses commodity edge hardware for interaction recording, dual-model inference, and anonymous student tracking. When camera geometry changes, BP's orientation-based signals invert, whereas FR's physiologically grounded signals correlate with learning outcomes in contexts similar to its training conditions. These results suggest that input design and label construction may matter more than architectural choice for deployment validity, although training-domain differences between the two models remain a partial confound. For agentic AI systems in authentic classrooms, these findings offer a reproducible framework for building embodied, per-student engagement trajectories.