Artificial Intelligence for Enhancing Indoor Air Quality in Educational Environments: A Review and Future Perspectives

Alexandros Romaios, Petros Sfikas, Athanasios Giannadakis, Thrassos Panidis, John A. Paravantis, Eugene D. Skouras and Giouli Mihalakakou ()
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Alexandros Romaios: Department of Mechanical Engineering and Aeronautics, University of Patras, University Campus, 26504 Rio, Greece
Petros Sfikas: Department of Mechanical Engineering and Aeronautics, University of Patras, University Campus, 26504 Rio, Greece
Athanasios Giannadakis: Department of Mechanical Engineering, University of Peloponnese, M. Alexandrou 1, 26334 Patras, Greece
Thrassos Panidis: Department of Mechanical Engineering and Aeronautics, University of Patras, University Campus, 26504 Rio, Greece
John A. Paravantis: Department of International and European Studies, University of Piraeus, 80 Karaoli and Dimitriou Street, 18534 Piraeus, Greece
Eugene D. Skouras: Department of Mechanical Engineering, University of Peloponnese, M. Alexandrou 1, 26334 Patras, Greece
Giouli Mihalakakou: Department of Mechanical Engineering and Aeronautics, University of Patras, University Campus, 26504 Rio, Greece

Sustainability, 2025, vol. 17, issue 22, 1-35

Abstract: Indoor Air Quality (IAQ) in educational environments is a critical determinant of students’ health, well-being, and learning performance, with inadequate ventilation and pollutant accumulation consistently associated with respiratory symptoms, fatigue, and impaired cognitive outcomes. Conventional monitoring approaches—based on periodic inspections or subjective perception—provide only fragmented insights and often underestimate exposure risks. Artificial intelligence (AI) offers a transformative framework to overcome these limitations through sensor calibration, anomaly detection, pollutant forecasting, and the adaptive control of ventilation systems. This review critically synthesizes the state of AI applications for IAQ management in educational environments, drawing on twenty real-world case studies from North America, Europe, Asia, and Oceania. The evidence highlights methodological innovations ranging from decision tree models integrated into large-scale sensor networks in Boston to hybrid deep learning architectures in New Zealand, and regression-based calibration techniques applied in Greece. Collectively, these studies demonstrate that AI can substantially improve predictive accuracy, reduce pollutant exposure, and enable proactive, data-driven ventilation management. At the same time, cross-case comparisons reveal systemic challenges—including sensor reliability and calibration drift, high installation and maintenance costs, limited interoperability with legacy building management systems, and enduring concerns over privacy and trust. Addressing these barriers will be essential for moving beyond localized pilots. The review concludes that AI holds transformative potential to shift school IAQ management from reactive practices toward continuous, adaptive, and health-oriented strategies. Realizing this potential will require transparent, equitable, and cost-effective deployment, positioning AI not only as a technological solution but also as a public health and educational priority.

Keywords: indoor air quality; machine learning; deep learning; educational buildings; sustainable buildings; healthy buildings (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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