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AI incident #793 ·

AllHere's Chatbot 'Ed' Fails and Costs Los Angeles Unified School District $6 Million

Open on the AI Incident Database 5 news reports Snapshot 7 Sep 2026

What happened

The Los Angeles Unified School District invested up to $6 million in developing the AI chatbot "Ed," meant to provide academic and mental health support for students. The chatbot allegedly failed to meet expectations, and the project collapsed when AllHere, the contracted start-up, faced financial difficulties. Prince George's County Public Schools in Maryland was also affected. Founder Joanna Smith-Griffin was removed as CEO in June 2024 and later arrested on fraud charges.

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 (5)

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

  1. A.I. ‘Friend’ for Public School Students Falls Flat
    nytimes.com · Dana Goldstein · AIID #4078
  2. LAUSD shelves its hyped AI chatbot to help students after collapse of firm that made it
    latimes.com · Howard Blume · AIID #4610
  3. CEO Of Artificial Intelligence Startup Company Charged With Defrauding Investors
    justice.gov · U.S. Attorney's Office Southern District of New York, United States Department of Justice · AIID #4611
  4. Start-Up Founder Who Sold A.I. Chatbot to Schools Is Charged With Fraud
    nytimes.com · Claire Fahy · AIID #4298
  5. Founder of AI education chatbot charged with defrauding investors of $10 million
    usatoday.com · Greta Cross · AIID #4609

Who was involved

Alleged developer
Allhere
Alleged harmed party
Taxpayers, Prince George'S County Students, Prince George'S County Public Schools, Prince George'S County Parents, Los Angeles Unified School District (Lausd), Los Angeles Students, Los Angeles Parents, Educational Communities, Students, Teachers, Educators

Classification (MIT AI Risk Repository taxonomy)

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

  • Misinformation and Manipulation

    "Recent studies have demonstrated that LLMs can be exploited to craft deceptive narratives with levels of persuasiveness similar to human-generated content (Pan et al., 2023b; Spitale et al., 2023), to fabri- cate fake n...

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

Incidents in the same risk subdomain

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