AI Scammers Exploit Student Loans
By Greg Collier
A troubling new report from the Associated Press has shed light on a growing form of fraud that exposes how vulnerable and broken the U.S. student loan system truly is. In what has become an increasingly common scheme, criminals are using stolen identities and artificial intelligence to enroll in community college courses, trigger federal student aid disbursements, and disappear with the money. Real people are left with debt, damaged credit, and a grueling bureaucratic fight to clear their names.
The scams often begin with unsuspecting victims learning they are “enrolled” at colleges they’ve never heard of, with student aid already distributed in their name. Some only discover the fraud after police or school officials question suspicious applications. Others only find out when checking their credit reports or receiving overdue payment notices. Victims have included people who never attended college at all.
Criminals are exploiting weaknesses in the verification process, especially at community colleges, where tuition is lower and more of the financial aid is returned directly to the “student.” Scammers target asynchronous online classes, where AI-generated bots can enroll, submit generic homework assignments, and claim aid with minimal human oversight. Some colleges have reported entire classes populated by bots. Real students then struggle to register for needed courses, which fill up quickly because of fake enrollments.
The problem is not limited to one region. In California alone, over a million fraudulent applications were filed in 2024, leading to hundreds of thousands of suspected fake enrollments. The state’s community college system, with its extensive online offerings and large number of campuses, has become a prime target. At least $11.1 million in aid was stolen from California schools in just one year, with no realistic chance of recovery.
The federal government has acknowledged the scale of the problem. A new temporary rule requires first-time student aid applicants to provide government-issued identification, impacting roughly 125,000 students during the summer term. More permanent and advanced verification systems are said to be in development for future terms. But some worry these steps are too late, and possibly too little.
Meanwhile, the system intended to help people access education continues to be manipulated. Criminal networks have used names of prison inmates and dead individuals, sometimes coordinating scams across multiple states. Convictions in Texas and New York have revealed fraud rings pursuing millions of dollars. Victims must navigate a slow and confusing process involving schools, loan servicers, and federal agencies, often without clear answers.
Adding to the concern, the federal office charged with investigating aid fraud has been weakened. Hundreds of staffers were recently laid off or retired from the Federal Student Aid office and the Inspector General’s division. As federal oversight thins, fraudsters may find it even easier to exploit the system.
The human cost goes beyond financial loss. Some victims, after years of effort, have only just had their fraudulent loans removed. Others are still trapped in the appeals process or seeing their credit scores drop. Some simply wanted to return to school to better their lives, only to find themselves blocked by full classrooms occupied by bots.
The emergence of artificial intelligence and the increase in online education have opened new doors for opportunity, but also for abuse. What this crisis reveals is not just a failure of cybersecurity or oversight, but a fundamental question about the system itself. If fake students can apply, enroll, and receive aid undetected, how secure or fair is the student loan infrastructure? And if identity theft can leave people burdened with years of debt for schools they never attended, who is the system really serving?
These scams are not just exploiting financial aid. They are exposing just how fragile the scaffolding of higher education financing has become.
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