Human-Centric Artificial Intelligence of Things–Based Indoor Environment Quality Modeling Framework for Supporting Student Well-Being in Educational Facilities
Min Jae Lee, Ruichuan Zhang
Abstract
Maintaining the quality of indoor environments in educational facilities is crucial for student comfort, health, well-being, and students’ learning performance. Current indoor environment maintenance practices and building systems for educational facility spaces often fail to include feedback from students and exhibit limited adaptability to their needs. To address this problem, this paper introduces a novel artificial intelligence of things (AIoT)-based framework to predict multidimensional indoor environment quality (IEQ) conditions. The proposed framework integrates internet of things (IoT) systems with deep learning algorithms to systematically incorporate multidimensional IEQ data and multimodal feedback from occupants. By collecting, fusing, and analyzing real-time IEQ and occupant feedback data, the proposed framework predicts the future IEQ condition based on current conditions. This framework yields insights into the IEQ conditions and their potential impacts on student well-being, thereby facilitating the future development of climate-adaptive, data-driven, and human-centric educational facilities. This framework was deployed, validated, and tested in selected educational facilities at the Virginia Tech Blacksburg campus, with encouraging results.