BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910105052643328 |
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| author | Li, Zaibei Yamaguchi, Shunpei Li, Qiuchi Spikol, Daniel |
| author_facet | Li, Zaibei Yamaguchi, Shunpei Li, Qiuchi Spikol, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning Li, Zaibei Yamaguchi, Shunpei Li, Qiuchi Spikol, Daniel Human-Computer Interaction 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. |
| title | BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.04093 |