Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization.

Indoor environmental quality (IEQ) affects students' health and academic performance who spend substantial time in school environments. This systematic literature review article explores how Artificial intelligence (AI) and Machine learning (ML) can predict and enhance IEQ within educational environments. A systematic review of 20 articles published between 2021 and 2024 was conducted using the PRISMA method, with all articles retrieved from the Scopus database. This article addresses three key research questions: 1) What IEQ factors are measured in university buildings, and why are they studied? 2) Which IoT sensors are employed in university buildings, and how are their data collected and managed? and 3) Which machine learning models are used for occupant comfort prediction in IEQ studies? The findings suggest air quality and thermal comfort are the most studied IEQ factors. These factors are primarily monitored using IoT sensors that enable continuous, real-time monitoring. None of the reviewed studies directly addressed acoustic comfort, highlighting a gap for future research. These IoT sensors are often integrate edge and cloud computing to ease data collection and reduce disruptions. Most articles used supervised learning models, of which 50% used deep learning techniques exclusively, 20% relied on traditional models, and 30% adopted a hybrid strategy combining traditional and deep learning ML. Reinforcement learning methods have also shown promise for dynamic HVAC and lighting control, although their adoption remains less common.