{"id":82431,"date":"2026-03-24T22:16:26","date_gmt":"2026-03-24T22:16:26","guid":{"rendered":"https:\/\/one.sightlinemg.com\/navytimes\/uncategorized\/2026\/03\/24\/deadly-iran-school-strike-casts-shadow-over-pentagons-ai-targeting-push\/"},"modified":"2026-08-08T17:16:51","modified_gmt":"2026-08-08T17:16:51","slug":"deadly-iran-school-strike-casts-shadow-over-pentagons-ai-targeting-push","status":"publish","type":"post","link":"https:\/\/one.sightlinemg.com\/navytimes\/news\/your-navy\/2026\/03\/24\/deadly-iran-school-strike-casts-shadow-over-pentagons-ai-targeting-push\/","title":{"rendered":"Deadly Iran school strike casts shadow over Pentagon\u2019s AI targeting push"},"content":{"rendered":"\n

KYIV, Ukraine \u2014 On the first day of the U.S.-Iran war, a Tomahawk cruise missile struck Shajareh Tayyebeh elementary school in Minab, southern Iran. At least 168 people were killed \u2014 more than 100 of them under the age of 12, according to UN and Iranian officials.<\/p>\n\n\n\n

The school building sat fewer than 100 yards from a long-time Islamic Revolutionary Guard Corps naval installation and was previously located within the IRGC compound perimeter until a wall appeared between 2013 and 2016, according to an analysis of satellite imagery by Amnesty International<\/a>.<\/p>\n\n\n\n

By the time the U.S. and Israel launched their first strikes on Feb. 28, the school had been established several years prior. It was active on social media and had its own website, a Reuters<\/a> investigation found.<\/p>\n\n\n\n

So what went wrong?<\/p>\n\n\n\n

\u201cWas artificial intelligence, including the use of the Maven Smart System, used to identify the Shajareh Tayyebeh school as a target?\u201d more than 120 House Democrats asked in a March 12 letter<\/a> to the Pentagon, just days after 46 Senate Democrats sent a similar request<\/a> demanding clarity on the deadly hit.<\/p>\n\n\n\n

The Maven Smart System<\/a>, a targeting and intelligence platform built by data analytics company Palantir Technologies under a $1.3 billion Pentagon contract, was built to solve a problem that has grown exponentially in recent years: information overload \u2014 with artificial intelligence as its secret weapon.<\/p>\n\n\n\n

Maven fuses satellite imagery, drone feeds, radar data and signals intelligence into a single interface, then classifies targets, recommends weapons systems and generates strike packages in near real time, compressing kill-chain reasoning and decision making into the fastest timelines ever seen on the battlefield.<\/p>\n\n\n\n

And it uses Anthropic\u2019s Claude AI model<\/a>, embedded in its system, to semi-autonomously rank targets by strategic importance, drafting automated legal justifications for each strike along the way.<\/p>\n\n\n\n

The software generated hundreds of strike coordinates in the first 24 hours of the Iran campaign, enabling the U.S. to hit more than 1,000 targets in the first 24 hours of the war, according to The Washington Post<\/a>.<\/p>\n\n\n\n

After sources briefed on preliminary findings told CNN<\/a> that U.S. Central Command had created targeting coordinates using outdated intelligence provided by the Defense Intelligence Agency that had not been updated to reflect the school\u2019s presence, one question became central to the inquiries: \u201cIf so, did a human verify the accuracy of this target?\u201d they asked. <\/p>\n\n\n\n

They are still waiting for an official explanation.<\/p>\n\n\n\n

Ukrainian drone operators who build and deploy semi-autonomous targeting systems on the front line told Military Times they recognized the likely culprit immediately. <\/p>\n\n\n\n

Ihor Matviyuk, the director of Aero Center<\/a>, a Ukrainian drone company that builds and deploys semi-autonomous drones on the front lines of the war with Russia, said he can imagine exactly how the failure happened. <\/p>\n\n\n\n

Although he has no inside knowledge of the Minab strike specifically, earlier this month he said that it bears the hallmarks of a targeting failure \u2014 not an AI malfunction.<\/p>\n\n\n\n

\u201cIt was almost definitely a strike on the [given] coordinates,\u201d Matviyuk told Military Times. \u201cThe main problem was not the AI \u2014 it was how close the military object was to the school.\u201d<\/p>\n\n\n\n

Last week, former military officials speaking to Semafor<\/a> confirmed Matviyuk\u2019s early assessment: \u201cHumans \u2014 not AI \u2014 are to blame” for the school strike, they said, pointing to stale human-curated data fed to the Pentagon\u2019s Maven targeting platform.<\/p>\n\n\n\n

Matviyuk recognized the pattern because he\u2019s had to decide how much AI to use in his own semi-autonomous weapon systems again and again as drone warfare and software capabilities have rapidly evolved on Ukraine\u2019s battlefield.<\/p>\n\n\n\n

\u201cAutomatic targeting allows us to capture less than half of the targets, not more,\u201d Matviyuk said. \u201cBecause they are all still camouflaged.\u201d<\/p>\n\n\n\n

\"\"
Ukrainian soldiers train with drones at an undisclosed location in the Donetsk Oblast, Ukraine, September 2025. (Diego Herrera Carcedo\/Anadolu via Getty Images)<\/figcaption><\/figure>\n\n\n\n

The Defense Department\u2019s own data bears that out. Maven can correctly identify objects at roughly 60% accuracy overall \u2014 compared with 84% for human analysts.<\/p>\n\n\n\n

But that rate drops below 30% in adverse conditions, such as bad weather or poor visibility, according to Pentagon data published in a 2024 Bloomberg<\/a> report.<\/p>\n\n\n\n

The risk of \u201ccollateral damage,\u201d as the strike on the Minab school might be categorized in military terminology, is too high \u2014 that is why Aero Center and every other Ukrainian drone company that spoke with Military Times says they always leave the final strike decision to a human operator.<\/p>\n\n\n\n

\u201cThe direct impact is always carried out by the operator\u2019s command,\u201d Matviyuk said, \u201cto prevent civilians from getting under the blow.\u201d<\/p>\n\n\n\n

In 2021, an experimental U.S. Air Force targeting AI scored roughly 25% accuracy in real conditions, despite rating its own confidence at 90%, then-Maj. Gen. Daniel Simpson, the Air Force\u2019s assistant deputy chief of staff for intelligence, surveillance, and reconnaissance, told Defense One<\/a>.<\/p>\n\n\n\n

\u201cIt was confidently wrong,\u201d Simpson said, summing up the program\u2019s problems. \u201cAnd that\u2019s not the algorithm\u2019s fault. It\u2019s because we fed it the wrong training data.\u201d<\/p>\n\n\n\n

The situation is not expected to improve. Last month, Hegseth slashed the Civilian Protection Center of Excellence workforce by approximately 90% and cut CENTCOM\u2019s civilian casualty assessment team from 10 to one, Politico<\/a> reported.<\/p>\n\n\n\n

Then, after leaving a skeleton staff to oversee the guardrails of the biggest expansion of AI in the military, Deputy Secretary of Defense Steve Feinberg signed a memo earlier this month formalizing AI\u2019s role in military decision making \u2014 designating Maven an official program of record and pushing adoption across all U.S. military branches by September, Reuters<\/a> reported on Friday.<\/p>\n\n\n\t