We present BadgeX, a novel system integrating lightweight wearable IoT devices (smart badges/smartphones) with Large Language Models (LLMs) to enable real-time collaborative learning analytics. The system captures multimodal sensor data (e.g., audio, image, motion, depth) from learners, processes it into structured features, and employs an LLM-driven framework to interpret these features, generating high-level insights grounded in learning theory. A pilot study demonstrated the system's capability to capture rich collaboration traces and for an LLM to produce plausible, theoretically coherent narrative analyses from sensor-derived features. BadgeX aims to lower deployment barriers, making complex collaborative dynamics visible and offering a pathway for real-time support in educational settings.
@article{arxiv.2604.04093,
title = {BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning},
author = {Zaibei Li and Shunpei Yamaguchi and Qiuchi Li and Daniel Spikol},
journal= {arXiv preprint arXiv:2604.04093},
year = {2026}
}