# ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

arXiv: https://arxiv.org/abs/2303.04292 · HTML: https://mlsanity.com/paper/2303.04292/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 106 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT](https://mlsanity.com/paper/2103.16033/) (2021)
- [Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity](https://mlsanity.com/paper/1805.00789/) (2018)
- [VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT](https://mlsanity.com/paper/2202.01348/) (2022)
- [Future of Smart Classroom in the Era of Wearable Neurotechnology](https://mlsanity.com/paper/2110.11475/) (2021)

## Built on by (most cited first, 3 of 3)

- [PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning](https://mlsanity.com/paper/2403.05864/) (2024)
- [Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy](https://mlsanity.com/paper/2502.07404/) (2025)
- [Harnessing IoT and Generative AI for Weather-Adaptive Learning in Climate Resilience Education](https://mlsanity.com/paper/2508.21666/) (2025)

## Abstract (arXiv)

Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals. Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across 15 participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants' learning performance increased on average by 26%. Furthermore, we showed that ERUDITE can be deployed on an edge-based prototype to evaluate its practicality and scalability.