[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121499-en":3,"doc-seo-121499-105":30,"detail-sidebar-cat-0-en-105":90},{"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":20,"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},121499,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Deep machine learning for innovative educational management under crisis conditions","Modern education operates under persistent uncertainty driven by crises, pandemics, and rapid technological change. These pressures require a shift from traditional reactive management to adaptive, data-driven strategies. Deep machine learning (DML) based on multi-layer neural networks enables predictive analytics, risk assessment, and intelligent decision-making. In education management, DML supports crisis forecasting, adaptive planning, personalized learning, resource optimization, and automated quality control, improving forecast accuracy and response speed to instability. Future work points to hybrid deep learning with symbolic AI for greater interpretability and effectiveness in complex crises.","UDC 004.94::37 .014.5  \nDeep machine learning for innovative educational management under crisis conditions  \nSvitlana Krasnyuk  \nKyiv National University of Technologies and Design, Kyiv [https://orcid.org/0000-0002-5987-8681](https://orcid.org/0000-0002-5987-8681)  \nAbstract. Modern education faces uncertainty driven by crises, pandemics, and rapid technological shifts. These challenges demand a transition from traditional reactive management to adaptive, datadriven strategies. Deep machine learning (DML), utilizing multi-layer neural networks, provides tools for predictive analytics, risk assessment, and intelligent decision-making. In educational management, DML supports crisis forecasting, adaptive planning, personalized learning, resource optimization, and quality control. Its key strength lies in adaptability and continuous learning, ensuring accurate predictions and quick responses to instability. Integrating DML enables resilience and innovation, shifting education toward proactive, intelligent management. Future developments include hybrid models combining deep learning with symbolic AI for enhanced interpretability and strategic effectiveness during complex crises.  \nKeywords: innovative management, educational management, classical & deep machine learning, artificial intelligence, crisis conditions.  \nIntroduction.  \nModern educational systems are facing unprecedented challenges: economic shocks, pandemics, military conflicts and rapid technological changes. These factors create high uncertainty and require the implementation of innovative approaches to education management. Traditional management models focused on stability are ineffective. In such conditions, deep machine learning comes to the fore as a key tool for the digital transformation of educational management. Deep machine learning is a class of artificial intelligence methods based on multi-layer neural networks that can analyze large volumes of data, identify hidden dependencies and predict complex processes. In educational management, this technology allows: to predict crisis risksand develop adaptive planning scenarios; personalize educational trajectories, taking into account the individual needs of students; optimize the distribution of resources (financial, personnel, infrastructure) in the face of restrictions; automate quality control of educational processes and learning outcomes; support decision-making in conditions of instability based on predictive analytics. The main advantage of deep machine learning is its ability to adapt to changing conditions, forming intelligent systems capable of self-learning and increasing the accuracy of forecasts. This allows: reducing the risks of management errors in conditions of high uncertainty; forming flexible strategies that are quickly adjusted when external factors change; moving from reactive management to proactive, which is especially important during crises.  \nThe Main Part.  \nModern educational systems are facing unprecedented challenges: economic shocks, pandemics, military conflicts and rapid technological changes. These factors create high uncertainty and require the implementation of innovative approaches to education management. Traditional management models focused on stability are ineffective. In such conditions, deep machine learning comes to the fore as a key tool for the digital transformation of educational management. Deep machine learning is a class of artificial intelligence methods based on multi-layer neural networks that can analyze large volumes of data, identify hidden dependencies and predict complex processes. In educational management, this technology allows: to predict crisis risksand develop adaptive planning scenarios; personalize educational trajectories, taking into account the individual needs of students; optimize the distribution of resources (financial, personnel, infrastructure) in the face of restrictions; automate quality control of educational processes and learn","cbCaivjUiEV6Dvk8","https://ap.wps.com/l/cbCaivjUiEV6Dvk8","pdf",358289,1,4,"English","en",105,"# Introduction\n# The Main Part\n# Conclusions\n# Discussion","[{\"question\":\"Why is deep machine learning important for educational management during crises?\",\"answer\":\"Crises create high uncertainty, making stability-focused management ineffective. Deep machine learning can analyze large data sets, predict complex processes, and enable adaptive, proactive decisions.\"},{\"question\":\"How does deep machine learning support educational management tasks?\",\"answer\":\"It can forecast crisis risks and create adaptive planning scenarios, personalize student learning trajectories, optimize resources under restrictions, automate quality control, and support decision-making through predictive analytics.\"},{\"question\":\"What advantages does deep machine learning provide over reactive management?\",\"answer\":\"Its key advantage is adaptability through continuous learning. This reduces management error risks, helps form flexible strategies quickly adjusted to external changes, and moves systems from reactive to proactive during crises.\"}]","Deep machine learning for innovative educational management under crisis conditions | PDF",1785735951,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"deep-machine-learning-for-innovative-educational-management-under-crisis-conditions","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/deep-machine-learning-for-innovative-educational-management-under-crisis-conditions/121499/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is deep machine learning important for educational management during crises?","Question",{"text":74,"@type":75},"Crises create high uncertainty, making stability-focused management ineffective. Deep machine learning can analyze large data sets, predict complex processes, and enable adaptive, proactive decisions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does deep machine learning support educational management tasks?",{"text":79,"@type":75},"It can forecast crisis risks and create adaptive planning scenarios, personalize student learning trajectories, optimize resources under restrictions, automate quality control, and support decision-making through predictive analytics.",{"name":81,"@type":72,"acceptedAnswer":82},"What advantages does deep machine learning provide over reactive management?",{"text":83,"@type":75},"Its key advantage is adaptability through continuous learning. This reduces management error risks, helps form flexible strategies quickly adjusted to external changes, and moves systems from reactive to proactive during crises.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]