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AI incident #1270 ·

Multiple Purported AI-Assisted Cheating Incidents Reported Across South Korea's SKY Universities During October 2025 Midterms

Open on the AI Incident Database 3 news reports Snapshot 7 Sep 2026

What happened

Reports from South Korea's SKY universities indicate multiple AI-assisted cheating incidents during the October 2025 midterms. At Yonsei, dozens reportedly used ChatGPT and other tools despite bans in an Oct. 15 online exam. Korea University and Seoul National reportedly found students using AI or sharing answers via group chats, prompting punitive sanctions across the institutions.

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 (3)

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

  1. Cheating scandals at top universities prompt rethink of education in digital era
    koreatimes.co.kr · Jung Da-hyun · AIID #7599
  2. A.I. Cheating Rattles Top Universities in South Korea
    nytimes.com · Jin Yu Young · AIID #6364
  3. Top universities react to AI cheating scandals, yet concrete disciplinary steps remain elusive
    koreatimes.co.kr · Jung Da-hyun · AIID #7598

Who was involved

Alleged harmed party
Yonsei University, Students At Yonsei University, Students At Seoul National University, Students At Korea University, Seoul National University, Korea University, General Public Of Korea, Faculty And Staff At Yonsei University, Faculty And Staff At Seoul National University, Faculty And Staff At Korea University, Epistemic Integrity, Students, University Students, Educational Communities

Classification (MIT AI Risk Repository taxonomy)

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

  • 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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