Modern war demands more of special operations medics. AI is helping the Army cram in more of what they need most in training. 27%

By Kelsey Baker40%

7/24/2026, 4:41:01 AM

BS Summary: This article contains 22 faulty reasoning types, including Overconfidence Bias, Post Hoc (False Cause), and Anecdotal, with Availability Heuristic as the most egregious example at 14.8% saturation with 117 hits. Analysis detected 962 faulty-reasoning hits from 788 analyzed words, generating a BS Score of 38.4% and a BS Rank of 27% (16,051 of 21,887 articles). This article is better (less manipulative) than 73.30% of the article peer group.

A soldier with the 1st Special Forces Group adjusts a leg splint during medical training at Joint Base Lewis-McChord, Washington. 
Sgt. 
Jose Camacho/US Army 
Army instructors are using AI to analyze student performance and optimize medic training. 
The goal is to make room for more battlefield skills without extending a nearly nine-month course. 
Lessons from Ukraine and drone warfare are influencing what special operations medics learn. 
As military medical personnel prepare to face complex battlefields where wounded troops may wait hours or even days for evacuation , the Army school tasked with readying special operations medics for future fights is turning to AI to pack in more lessons into their training. 
The military's preparations for large-scale combat operations and new injuries , such as those caused by drones, are forcing military medical personnel to sharpen skills that received less attention during the wars in Iraq and Afghanistan. 
Instructors at the Joint Special Operations Medical Training Center, however, can't simply extend their already grueling training course, a premier pipeline for new military medics headed to special operations units, to accommodate more material. 
As is, it's nearly nine months long. 
So, instructors are leaning on AI to optimize the experience, decide what to teach more or less of, and determine where students are struggling. 
The number one thing the school needs "is a way for students to learn more and learn faster," Col. 
Ken Dwyer, the school's commander, told Business Insider. 
"We only have so much time with them before we're required to get them onto the force." 
US military special operation operations personnel conduct combat casualty care training at Hurlburt Field, Florida. 
Airman 1st Class Alexa Hunt/US Air Force 
Course instructors face a unique challenge: taking students who usually have no prior medical experience and turning them into highly capable paramedics . 
Graduates must be able to stop catastrophic hemorrhaging, manage wounds that cannot be controlled with a tourniquet , provide a level of basic care for local civilians, and manage the health needs of potentially hundreds of troops off the battlefield, too. 
(The school trains senior medics too, in its four-month Special Forces Medical Sergeant Course, where troops build on skills to master a higher degree of clinical care.) 
On top of all those medical skills, students  Army medics who will serve with Rangers and Green Berets and Navy corpsmen headed to Marine reconnaissance and special operations units  must possess the tactical combat skills expected of special operators, making for a school calendar with little wiggle room. 
To make better use of that time, course leaders are analyzing student performance data, including how long students take to master specific skills and how many repetitions they require, using AI-assisted analytics to pinpoint how they can make the most of their curriculum. 
The goal isn't to replace instructors, but to help leaders identify how they can shift precious classroom hours toward the skills students need most. 
Feeding that granular information, requiring tedious data entry from leaders, into a calculator "gives us back a, 'this is where our blind spots are,'" said the course chief, who spoke on condition of anonymity because of privacy concerns. 
Such data compiled over multiple iterations of the intense course can make a big training difference, he said. 
"We can shift resources and time to put the emphasis where the students need it and where their areas of weakness are," he said. 
Tourniquet training offers a simple example. 
The skill was a central part of battlefield medicine during the Global War on Terror, so students may arrive with more familiarity or master application faster than other forms of care, such as tourniquet removal in the field, something Ukraine is showing is necessary for prolonged casualty care when evacuation isn't an option . 
Such removals, called conversions, must be carefully tracked and timed to avoid serious organ damage. 
Training could focus more on conversions, or other areas of concern for medics, like infection and medication management, part of a growing field called prolonged casualty care , meant to keep wounded troops alive when advanced trauma care is out of reach. 
Refining training in such minute ways allows the curriculum to evolve more quickly, the course chief explained. 
If data shows students are mastering one skill faster than expected, instructors can reclaim that time and devote it to newer priorities shaped by lessons emerging from Ukraine . 
"We owe it to them to make sure that we're creating systems" that show instructors "where we should be going," the chief said. 
"Those are educational strides that three years ago just wouldn't have been possible." 
Article reasoning-pattern comparisonThis article: 4.7%Kelsey Baker: 1.9%Business Insider: 2.5%Confirmation Bias4.7%This article: 0.9%Kelsey Baker: 1.1%Business Insider: 0.7%Anchoring Bias0.9%This article: 14.8%Kelsey Baker: 9.4%Business Insider: 3.4%Availability Heuristic14.8%This article: 6.3%Kelsey Baker: 1.9%Business Insider: 0.7%Representativeness Heuristic6.3%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.9%Hindsight Bias0.0%This article: 12.2%Kelsey Baker: 3.9%Business Insider: 1.9%Overconfidence Bias12.2%This article: 4.3%Kelsey Baker: 2.4%Business Insider: 5.0%Framing Effect4.3%This article: 2.2%Kelsey Baker: 0.5%Business Insider: 0.8%Loss Aversion2.2%This article: 4.3%Kelsey Baker: 1.7%Business Insider: 0.8%Status Quo Bias4.3%This article: 4.3%Kelsey Baker: 0.9%Business Insider: 0.3%Sunk Cost Effect4.3%This article: 3.0%Kelsey Baker: 6.9%Business Insider: 3.1%Optimism Bias3.0%This article: 5.7%Kelsey Baker: 1.2%Business Insider: 1.6%Pessimism Bias5.7%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 4.6%Negativity Bias0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 2.0%Self-Serving Bias0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.2%Actor-Observer Bias0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.8%In-Group Bias0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.2%Out-Group Homogeneity Bias0.0%This article: 5.2%Kelsey Baker: 1.1%Business Insider: 3.0%Halo Effect5.2%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Horn Effect0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.0%Dunning-Kruger Effect0.0%This article: 6.2%Kelsey Baker: 7.2%Business Insider: 1.3%Recency Bias6.2%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.4%Primacy Effect0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Blind-Spot Bias0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.3%Ad Hominem0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Straw Man0.0%This article: 1.6%Kelsey Baker: 3.0%Business Insider: 3.3%Appeal to Authority1.6%This article: 4.9%Kelsey Baker: 1.1%Business Insider: 1.2%False Dilemma4.9%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.6%Slippery Slope0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Circular Reasoning0.0%This article: 6.3%Kelsey Baker: 2.1%Business Insider: 4.0%Hasty Generalization6.3%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Red Herring0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.7%Bandwagon0.0%This article: 5.7%Kelsey Baker: 1.2%Business Insider: 3.1%Appeal to Emotion5.7%This article: 2.4%Kelsey Baker: 0.5%Business Insider: 0.7%Begging the Question2.4%This article: 12.2%Kelsey Baker: 3.3%Business Insider: 2.3%Post Hoc (False Cause)12.2%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Tu Quoque0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.2%Burden of Proof0.0%This article: 5.3%Kelsey Baker: 1.1%Business Insider: 0.1%Appeal to Nature5.3%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.2%Composition/Division0.0%This article: 6.9%Kelsey Baker: 1.5%Business Insider: 3.6%Anecdotal6.9%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%No True Scotsman0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 1.4%Ambiguity (Equivocation)0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Middle Ground0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.0%Personal Incredulity0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Special Pleading0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.0%Genetic Fallacy0.0%This article: 2.4%Kelsey Baker: 2.1%Business Insider: 1.2%Unattributed Quote2.4%This article: 0.0%Kelsey Baker: 0.5%Business Insider: 0.7%Quote-first Misdirection0.0%This article: 0.0%Kelsey Baker: 1.5%Business Insider: 3.3%Biased Writer Voice0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 1.3%Indoctrination0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Kelsey Baker: 0.0%Business Insider: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Kelsey Baker: 0.9%Business Insider: 1.5%Attempt to Sell a Product or S…0.0%

788 words analyzed.

Speakers

1speaker3.2%attributed speech763writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 45 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 19 words • 100.0% coverageKen Dwyer • 8 words • 0.0% coverageKen Dwyer • 17 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 41 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 50 words • 0.0% coverageWriter's voice • 43 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 54 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 42 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverage
Selected voice

Ken Dwyer

68%flagged-word coverage
25 attributed words100% of attributed speech74% writer coverage
0%2.5%5.0%Unattributed Quote-2.5 ptsWriter: 2.5%Ken Dwyer: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.