[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-586158-105":3,"detail-sidebar-cat-0-en-105":73,"doc-detail-586158-en":122},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":66,"head_meta":68,"extra_data":70,"updated_unix":72},105,"en","transforming-combat-casualty-care-training-generative-ai-enabled-adaptive-learning-in-forward-medical-settings","Transforming Combat Casualty Care Training: Generative AI-Enabled Adaptive Learning in Forward Medical Settings","","Rapid training for far-forward combat casualty care demands speed and reliability in austere, resource-limited environments. This white paper addresses how variability in baseline medical knowledge can be mitigated through adaptive learning without the heavy cost of manually mapping question-answer (QA) sets to curricular objectives. It presents an AI-assisted, human-in-the-loop workflow that generates QA sets aligned with NBME standards, validates quality using LLM judges, and compares performance to human rater outcomes.",{"@graph":14,"@context":65},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/transforming-combat-casualty-care-training-generative-ai-enabled-adaptive-learning-in-forward-medical-settings/586158/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/transforming-combat-casualty-care-training-generative-ai-enabled-adaptive-learning-in-forward-medical-settings/586158.png","ImageObject",300,407,{"name":42,"@type":43},"Taylor Morgan","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-02","2026-10-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59],{"name":60,"@type":61,"acceptedAnswer":62},"What time and cost reductions were reported compared with human SMEs?","Question",{"text":63,"@type":64},"A human SME took about 24 minutes to author one high-quality QA set at an estimated cost of $100. In contrast, each LLM generated a QA set in roughly 20 seconds at a fraction of the price, with large reductions reported in the paper’s results and discussed in the appendix.","Answer","https://schema.org",{"og:url":32,"og:type":67,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":69,"canonical":32},"index,follow",{"doc_id":71,"site_id":7},586158,1792171154,{"code":4,"msg":74,"data":75},"success",[76,80,84,88,93,98,102,106,111,114,118],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":77,"show_sort_weight":78,"slug":79},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":81,"show_sort_weight":82,"slug":83},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Exam",70,"exam",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},5,"Comic",60,"comic",{"id":94,"doc_module":4,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},6,"Technology",50,"technology",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":99,"show_sort_weight":100,"slug":101},"Healthcare",40,"healthcare",{"id":103,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":104,"slug":105},8,30,"research-report",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},9,"Religion & Spirituality",20,"religion-spirituality",{"id":109,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":109,"slug":113},"World Cup","world-cup",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":115,"slug":117},10,"Lifestyle","lifestyle",{"id":119,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":89,"slug":121},19,"General","general",{"code":4,"msg":74,"data":123},{"doc_id":71,"user_id":124,"nickname":42,"user_avatar":125,"doc_module":4,"category_id":103,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":109,"language":131,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":12,"update_tm":135,"read_time":96,"preview_page_count":4},1099523885336,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","TRANSFORMING COMBAT CASUALTY CARE TRAINING: GENERATIVE AIENABLED ADAPTIVE LEARNING IN FORWARD MEDICAL SETTINGS  \nAlan D . Koenig, John J . Lee, Eric Savitsky, Gabriele Nataneli, Karson Lindstrom, David L. Schriger, and Tyler Savitsky  \nCopyright © 2025 The Regents of the University of California.  \nTo cite from this report, please use the following as your APA 7th edition reference: Koenig, A. D., Lee, J. J., Savitsky, E., Nataneli, G., Lindstrom, K., Schriger, D. L., and Savitsky, T. (2025) . Transforming combat casualty care training: Generative AI-enabled adaptive learning in forward medical settings. [White paper] . UCLA/CRESST.  \nTable of Contents  \nExecutive Summary ............................................................................................................... iv  \nBackground............................................................................................................................. 1  \nMethod .......................................................................................................................................4  \nResults ........................................................................................................................................4  \nInterrater Reliability ................................................................................................................... 5  \nDiscussion ............................................................................................................................... 7  \nStudy Limitations and Methodological Implications ..................................................................8  \nConclusion ............................................................................................................................ 10  \nReferences ............................................................................................................................ 11  \nAppendix A: Time and Cost Advantages of LLMs in Medical QA Set Creation......................... 13  \nAppendix B: Author and Judge Prompt Key Components ...................................................... 14  \nTransforming Combat Casualty Care Training: Generative AI-Enabled Adaptive Learning in Forward Medical Settings  \nAlan D. Koenig,a John J. Lee,a Eric Savitsky,b,c Gabriele Nataneli,c Karson Lindstrom,d David L. Schriger,e and Tyler Savitskyc  \na CRESST/University of California, Los Angeles b Emergency Medicine/University of California, Los Angeles c SonoSim, Inc.  \nd Caylent  \ne Emergency Medicine/University of California, Los Angeles  \nExecutive Summary  \nIntroduction  \nRapid training of military and civilian first responders for far-forward combat casualty care in austere, resource-limited environments is a NATO priority during Large-Scale Combat Operations (LSCOs) . Variability in baseline medical knowledge complicates this training. Although adaptive learning can bridge these gaps, creating and mapping traditional questionanswer (QA) sets to curricular objectives is a resource-intensive process. Recent advancementsin generative artificial intelligence (AI) have the potential to overcome these barriers. Here, we describe how we leveraged AI-driven techniques to generate QA sets consistent with National Board of Medical Examiners (NBME) standards, validated their quality using large language models (LLMs) as a judge, and compared their performance against that of a human rater. This study presents a process that can be integrated into online medical training platforms, utilizing a methodology that is generalizable to other educational domains. It features a human-in-theloop framework, where expert supervision is applied to AI-assisted educational assessment.  \nMethod  \nA medical subject matter expert (SME) rated four LLMs’ (OpenAI’s GPT-4, GPT-4o, o1-mini, and o1) performance generating 320 medical QA sets using a three-tier scale: “High” for complete task coverage,“Medium” for minor omissions, and “Low” for critical omissions. Subse","cbCaivymAssb76tl","https://ap.wps.com/l/cbCaivymAssb76tl","pdf",1583341,"English","# Executive Summary\n## Background\n## Method\n## Results\n## Interrater Reliability\n## Discussion\n## Study Limitations and Methodological Implications\n## Conclusion\n## References\n## Appendix A: Time and Cost Advantages of LLMs in Medical QA Set Creation\n## Appendix B: Author and Judge Prompt Key Components","[{\"question\":\"What time and cost reductions were reported compared with human SMEs?\",\"answer\":\"A human SME took about 24 minutes to author one high-quality QA set at an estimated cost of $100. In contrast, each LLM generated a QA set in roughly 20 seconds at a fraction of the price, with large reductions reported in the paper’s results and discussed in the appendix.\"}]","Transforming Combat Casualty Care Training: Generative AI-Enabled Adaptive Learning in Forward Medical Settings | PDF",1792057106]