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

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.

Figures (1)

• Figure 1: LLM-Enhanced IoT Analytical Framework of BadgeX: ubiquitous sensors capture multimodal signals, IoT pipelines extract and aggregate features, and LLMs interpret these features to generate high-level analytics on learning construct.