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AI incident #1038 ·

Scammers Reportedly Use AI Tools to Impersonate Students and Obtain Federal Aid

Open on the AI Incident Database 4 news reports Snapshot 7 Sep 2026

What happened

Between 2021 and 2025, California community colleges faced a surge in fraudulent applications—now estimated at 34% of all submissions. Reports indicate that scammers used generative AI tools, including ChatGPT, to produce identity-verifying responses that enabled them to impersonate students and obtain financial aid. The fraud allegedly resulted in over $13 million in losses over the past year alone, straining administrative and instructional systems and displacing legitimate applicants.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (4)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. CheatGPT — California community colleges engage in a cyber battle with scammers who are stealing learning opportunities and millions of dollars in financial aid meant for legitimate students
    moneywise.com · Christy Bieber · AIID #5081
  2. AI Bots in California Steal Over $10 Million in Federal Financial Aid
    reason.com · Autumn Billings · AIID #5141
  3. How scammers are using AI to steal college financial aid
    apnews.com · Sharon Lurye · AIID #5461
  4. TOUGH LESSON Woman finds mystery $9k loan taken out in her name as ‘ghost’ enrolls her in college – she found out from knock on door
    the-sun.com · Cheyenne R. Ubiera · AIID #5474

Who was involved

Alleged harmed party
U.S. Taxpayers, Financial Aid Recipients, California Community Colleges, California Community College Students, California Community College Faculty, Students, Community College Students, Educational Communities

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Domain-Specific Misuses

    "Improvements in LLMs may exert greater pressure to apply LLMs to various domains, such as health and education (Eloundou et al., 2023). Crude efforts to use LLMs in such domains, however, may incur harm and should be di...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Incidents in the same risk subdomain

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