BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning

TL;DR

BadgeX is a system that combines wearable IoT devices with large language models to analyze and interpret collaborative learning activities in real-time, providing insights grounded in learning theory.

Contribution

It introduces a novel integration of IoT sensors and LLMs for real-time educational analytics, enabling rich collaboration tracing and interpretive insights.

Findings

Successfully captured multimodal sensor data from learners.

LLMs generated plausible, theory-coherent narrative analyses.

Demonstrated potential for real-time support in educational settings.

Abstract

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.