Lessons from the post occupancy evaluation of the indoor learning environments: A review

¹ Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ, USA
² School of Property, Construction and Project management, RMIT University, Melbourne, Australia
³ Department of Construction, Shahid Beheshti University, Tehran, Iran

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Indoor Environmental Quality (IEQ) plays a critical role in ensuring healthy and productive learning environments in university classrooms, where high occupant density and prolonged exposure can amplify environmental deficiencies. This study presents a systematic review of peer-reviewed literature published since 2015, synthesizing current evidence on indoor air quality (IAQ) in higher-education classrooms. The review examines methodological approaches, environmental parameters, and ventilation strategies reported across different climatic contexts. Carbon dioxide (CO₂) concentration emerges as the most frequently used indicator of indoor air quality, with many studies reporting average values exceeding 1000 ppm during occupied periods, indicating inadequate ventilation in numerous classroom environments. Additionally, only a limited number of studies integrated thermal comfort and IAQ data, and several reported acceptable indoor temperatures while simultaneously observing high CO₂ concentrations, indicating that temperature-based control strategies alone may mask ventilation deficiencies. Overall, the review highlights a persistent mismatch between standard-based IAQ criteria and real classroom conditions, emphasizing the need for integrated, occupant-centric assessment frameworks that jointly consider ventilation performance, air quality, and occupant health. The study also identifies several research gaps, including limited investigations in cold and arid climates, insufficient integration of IAQ assessments, and the underutilization of advanced data-driven modeling approaches.