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Dartmouth's Geisel School of Medicine Reportedly Used Canvas Activity Logs to Accuse Students of Cheating

1 news report Synced from source · record last edited 3 Sep 2026

In brief

An AI system built by Geisel School of Medicine's Technology staff and Canvas and deployed by Geisel School of Medicine allegedly harmed University students, Students and 7 others.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
1 reportMay 2021

What happened

Dartmouth's Geisel School of Medicine reportedly used Canvas learning-management activity logs and an internal analysis process to investigate remote-exam cheating during the 2020–2021 academic year. Seventeen medical students were charged after the school inferred that they accessed course materials during exams, but students and outside technical reviewers said automated Canvas activity may have been misread as intentional misconduct. Dartmouth later dropped the charges.

Editor's notes

This record is retained as an incident because it describes a specific academic-misconduct investigation in which Geisel School of Medicine reportedly used Canvas learning-management activity logs and an internal analysis process to accuse students of cheating. The record is AIID-borderline because the implicated system appears to be a semi-opaque activity-log analysis rather than a conventional AI model; retention depends on treating the log-analysis process as an algorithmic decision-support system used in a high-stakes disciplinary context.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. Online Cheating Charges Upend Dartmouth Medical School (Published 2021)
    nytimes.com · Natasha Singer, Aaron Krolik

Who was involved

Alleged deployer
Geisel School of Medicine
Alleged harmed party
University students Students Sirey Zhang Medical students Geisel School of Medicine's students Geisel School of Medicine's professors Geisel School of Medicine's accused students Epistemic integrity Educational communities

AI systems implicated

Learning management system activity logsCanvasAutomated proctoring systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #302 on the AI Incident Database · all 1 report