[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126289-en":3,"doc-seo-126289-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126289,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Predicting and Enhancing Indoor Environmental Quality in Educational Environments - Latest Trends in AI and Machine Learning Applications","Indoor environmental quality (IEQ) strongly influences students’ health and academic performance due to extensive time spent in schools. This systematic literature review examines how artificial intelligence (AI) and machine learning (ML) can predict and enhance IEQ in educational settings. Using the PRISMA framework, it reviews 20 studies published from 2021 to 2024 sourced from Scopus, addressing measured IEQ factors, deployed IoT sensing approaches, and ML models for occupant comfort. Results identify air quality and thermal comfort as dominant targets, with supervised learning and deep or hybrid strategies most common. Acoustic comfort remains a notable research gap.","CIB Conferences  \n\n| Volume 1 | Article 93 |\n| --- | --- |\n| 2025\u003Cbr>Predicting and Enhancing Indoor Environmental Quality in Educational Environments: Latest Trends in AI and Machine Learning Applications\u003Cbr>Abdurrahman Baru\u003Cbr>Georgia Institute of Technology, [abaru8@gatech.edu](abaru8@gatech.edu)\u003Cbr>Marwan Shagar\u003Cbr>Georgia Institute of Technology, [mshagar@gatech.edu](mshagar@gatech.edu)\u003Cbr>Anshi Vajpayee\u003Cbr>Georgia Institute of Technology, [avajpayee7@gatech.edu](avajpayee7@gatech.edu)\u003Cbr>Jin Lee\u003Cbr>Georgia Institute of Technology, [yl@gatech.edu](yl@gatech.edu)\u003Cbr>[Graham J. Moore](Graham J. Moore)\u003Cbr>Georgia Institute of Technology, [gmoore75@gatech.edu](gmoore75@gatech.edu)\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://docs.lib.purdue.edu/cib-conferences](https://docs.lib.purdue.edu/cib-conferences) |  |\n\nRecommended Citation  \nBaru, Abdurrahman; Shagar, Marwan; Vajpayee, Anshi; Lee, Jin; Moore, Graham J.; and Yang, Eunhwa (2025) \"Predicting and Enhancing Indoor Environmental Quality in Educational Environments: Latest Trends in AI and Machine Learning Applications,\" CIB Conferences: Vol. 1 Article 93.  \nDOI: [https://doi.org/10.7771/3067-4883.2020](https://doi.org/10.7771/3067-4883.2020)  \nThis document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. [Please contact epubs@purdue.edu](Please contact epubs@purdue.edu) for additional information.  \nPredicting and Enhancing Indoor Environmental Quality in Educational Environments: Latest Trends in AI and Machine Learning Applications  \nAuthors  \nAbdurrahman Baru, Marwan Shagar, Anshi Vajpayee, Jin Lee, Graham J. Moore, and Eunhwa Yang  \nThis w116-smart and sustainable built environments is available in CIB Conferences: [https://docs.lib.purdue.edu/](https://docs.lib.purdue.edu/)[ ](https://docs.lib.purdue.edu/)cib-conferences/vol1/iss1/93  \nPredicting and Enhancing Indoor Environmental Quality in Educational Environments: Latest Trends in AI and Machine Learning Applications  \nAbdurrahman Baru, [abaru8@gatech.edu](abaru8@gatech.edu)  \nGeorgia Institute of Technology, United States of America  \nMarwan M Shagar, [mshagar@gatech.edu](mshagar@gatech.edu)  \nGeorgia Institute of Technology, United States of America  \nAnshi Vajpayee, [avajpayee7@gatech.edu](avajpayee7@gatech.edu)  \nGeorgia Institute of Technology, United States of America  \nGraham J Moore, [gmoore75@gatech.edu](gmoore75@gatech.edu)  \nGeorgia Institute of Technology, United States of America  \nJin Lee, [yl@gatech.edu](yl@gatech.edu)  \nGeorgia Institute of Technology, United States of America  \nEunhwa Yang, [eunhwa.yang@design.gatech.edu](eunhwa.yang@design.gatech.edu)  \nGeorgia Institute of Technology, United States of America  \nAbstract  \nIndoor 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. M","cbCaitsUvlk2FM77","https://ap.wps.com/l/cbCaitsUvlk2FM77","pdf",1058520,1,13,"English","en",105,"# Abstract\n# 1 Introduction\n## IEQ relevance in educational settings\n## Research objectives and scope\n# Systematic Literature Review Method\n## PRISMA-based article selection\n## Data source and study range\n# Key Research Questions and Findings\n## IEQ factors measured in university buildings\n## IoT sensors and data collection practices\n## ML models for occupant comfort prediction\n# Research Gaps and Future Directions\n## Underrepresented acoustic comfort","[{\"question\":\"How does the document evaluate AI and ML for indoor environmental quality in schools?\",\"answer\":\"It performs a systematic literature review using the PRISMA method, analyzing 20 studies from 2021 to 2024 retrieved from the Scopus database.\"},{\"question\":\"Which IEQ factors are most frequently studied in the reviewed university-building research?\",\"answer\":\"Air quality and thermal comfort are identified as the most studied IEQ factors.\"},{\"question\":\"What types of machine learning approaches are commonly used for occupant comfort prediction?\",\"answer\":\"Most studies use supervised learning; about half rely exclusively on deep learning, 20% use traditional models, and 30% adopt hybrid strategies combining traditional and deep learning.\"}]","Predicting and Enhancing Indoor Environmental Quality in Educational Environments - Latest Trends in AI and Machine Learning Applications | PDF",1785904277,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-and-enhancing-indoor-environmental-quality-in-educational-environments-latest-trends-in-ai-and-machine-learning-applications","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-and-enhancing-indoor-environmental-quality-in-educational-environments-latest-trends-in-ai-and-machine-learning-applications/126289/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the document evaluate AI and ML for indoor environmental quality in schools?","Question",{"text":76,"@type":77},"It performs a systematic literature review using the PRISMA method, analyzing 20 studies from 2021 to 2024 retrieved from the Scopus database.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which IEQ factors are most frequently studied in the reviewed university-building research?",{"text":81,"@type":77},"Air quality and thermal comfort are identified as the most studied IEQ factors.",{"name":83,"@type":74,"acceptedAnswer":84},"What types of machine learning approaches are commonly used for occupant comfort prediction?",{"text":85,"@type":77},"Most studies use supervised learning; 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