Founder
February 28, 2026
19 min read
The integration of artificial intelligence (AI) technologies into the education ecosystem is a transformation that is fundamentally changing pedagogical processes while at the same time creating new threat vectors of unprecedented complexity in the fields of corporate liability and risk management. From predicting student performance to securing examinations, from detecting plagiarism to personalising curricula—across these processes, the shift of the decision-making mechanism from human to machine or to human–machine hybrid structures is straining traditional legal doctrines and insurance policy structures.
This research report examines in depth the compensation, insurability and legal liability regime for harm caused by errors of AI algorithms used in the education sector—for example, a student being wrongly graded, unjustly referred for discipline by a biased algorithm, or their academic future blocked by “black box” decisions. The report details the grey areas between Educators Legal Liability (ELL), Cyber Liability and Tech E&O insurance; the Silent AI risk emerging in the insurance market; and indemnification mechanisms between education institutions and EdTech providers.
Analysis shows that existing insurance products contain structural gaps in covering AI-related “decision-making errors.” Cyber policies, traditionally focused on data breaches, fall short of covering reputational losses and opportunity costs (lost opportunity) arising not from data being “stolen” but from its “misprocessing.” Likewise, ELL policies built on defence strategies grounded in the human educator’s “professional judgment” can become ineffective in the face of systematic errors of deterministic or probabilistic algorithms.
The report comparatively addresses current case law and legislation in the United States (Section 1983, Title VI), the European Union (EU AI Act) and Turkey (KVKK, administrative law), showing how the Educational Malpractice doctrine is evolving into “product liability” or “service defect” in the AI age. Precedent cases such as Ogletree v. Cleveland State University and Doe v. Yale University, together with the 2020 IB/Ofqual grading crisis, are used as case analyses illustrating the concrete legal and financial consequences of these theoretical debates.
Education institutions have for centuries been run under a risk-management understanding based on the assumption of “human error.” A teacher misreading an exam script, an administrator misapplying discipline regulations, or a guidance service giving wrong direction—these are defined, bounded risks in the world of law and insurance. These risks have generally been met by courts with a certain tolerance owing to the subjective nature of human judgment and to education being a “public service” or “field of expertise,” and in insurance policies they have been covered under the heading of educators’ professional liability.
With the advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies, however—especially Generative AI and Predictive Analytics—this paradigm has been shaken. Decisions are now made not by the interaction of biological neurons but by probabilistic computations of artificial neural networks (ANN) with billions of parameters. This shifts the concept of “error” from “negligence” onto a technological and statistical ground: “system failure,” “algorithmic bias” or “data poisoning.”
Categorising the areas in which AI is used in education and the specific liability risks each creates is essential to grounding the insurance and compensation debate correctly.
AI applications in education bring with them various risks alongside their different functions. The first of these, Algorithmic Grading and Assessment, aims at the automatic scoring of student assignments, exams or overall achievement. Such systems can, however, lead to potential errors and risks—for example, algorithms amplifying biases in training data and systematically giving lower grades to certain demographic groups; the best-known example is the IB/Ofqual Crisis. The legal and insurance counterpart of such situations appears as discrimination claims (Title VI), class actions and reputational harm.
The second major area, Proctoring and Exam Security, performs eye-tracking, face recognition and environment scanning to prevent cheating in remote exams. The main potential risks here are: face-recognition systems failing to identify darker-skinned students; innocent behaviour being flagged as “cheating” (false positive); and privacy violations. These risks give rise to legal consequences under constitutional rights (Fourth Amendment / privacy) and biometric data laws (BIPA/KVKK).
Third, Plagiarism and AI Detection applications serve to determine whether student texts were written by AI (e.g. Turnitin, GPTZero). The risk in this area is an original work being wrongly labelled as AI-generated, especially when texts of non-native speakers are perceived as “AI-written” due to linguistic bias. Such errors can lead to legal and insurance claims: defamation, Due Process violations and obstruction of the right to education.
The fourth application is Student Success Prediction and Intervention, whose functions are identifying “at-risk” students, admission decisions and scholarship allocation. The potential danger here is historical inequalities in data (race, gender, socio-economic status) being carried forward through algorithms. This can result in discrimination claims, equal opportunity violations, annulment of administrative decisions and compensation claims.
Lastly, Adaptive Learning aims at curriculum and content delivery tailored to the student. The main risk in such systems is the algorithm misdirecting the student so that they receive inadequate education (“educational neglect”). This can create legal liability under Educational Malpractice and product liability.
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In the insurance sector, the “Silent Cyber” problem that arose when cyber risks first emerged applies to AI today. “Silent AI” refers to the situation where traditional insurance policies (General Liability, Professional Liability, Directors & Officers) neither explicitly include nor explicitly exclude AI-related risks. This ambiguity leads to serious disputes between insurer and insured (the school) when a loss occurs.
For example, when an AI chatbot used by a school gives a student the wrong registration date and the student loses a term:
General Liability (CGL): May deny on the ground that there is no bodily injury or property damage.
Cyber Insurance: May deny: “No data breach or network security vulnerability; the system worked as designed but gave the wrong answer.”
Educators Legal Liability (ELL): May deny on the defence that “this is not a teacher’s error but a software error.”
This report will analyse how to fill these gaps and which mechanisms should come into play in compensating loss.
Liability of education institutions for harm caused by AI is shaped by the legal system of the jurisdiction in which they operate. The US, EU and Turkey examples represent different approaches.
The US legal system seeks to address AI liability not through a dedicated “AI Act” but by interpreting existing constitutional and federal law.
Title VI of the Civil Rights Act of 1964 prohibits discrimination on the basis of race, colour or national origin in any programme receiving federal funding (almost all universities and public schools).
If an AI algorithm, without any discriminatory intent in its design, by its outcomes places a particular race at a disadvantage (e.g. face recognition misidentifying Black students more often, or plagiarism-detection software accusing international students more often), this may be treated as a Title VI violation.
This case is a turning point for AI liability. The student alleged that the AI detection tool GPTZero used by Yale University wrongly marked their original paper as “written by AI.” The plaintiff argued that such software tends to treat non-native speakers’ writing as AI-generated (linguistic bias) and that they were therefore discriminated against within the meaning of Title VI. The institution’s use of a tool whose reliability was not scientifically established to impose disciplinary sanction laid the ground for claims of both discrimination and breach of contract.
42 U.S.C. § 1983 gives individuals the right to sue when constitutional rights are violated by state actors (public universities and schools).
Fourth Amendment (Privacy). In Ogletree v. Cleveland State University, the federal court held that the university’s requirement that the student’s room be scanned by webcam (room scan) before a remote exam violated the prohibition on “unreasonable search and seizure” protected by the Fourth Amendment. The court stated that the convenience afforded by technology (exam security) does not eliminate the student’s expectation of privacy in their own home. This ruling created significant liability risk and compensation exposure for all institutions using proctoring technology.
Public schools must afford students the right to be heard before expelling them or cancelling their grades. The “black box” nature of AI systems—the inability to explain how the decision was reached—violates procedural due process. If the student receives no answer to “Why did the algorithm accuse me of cheating?” other than “the system said so,” that is unlawful.
A comprehensive guide on data privacy, algorithmic bias, and institutional responsibilities for AI use in Turkish education.
The EU has introduced the world’s most comprehensive and stringent AI liability regime with the “Artificial Intelligence Act” (EU AI Act).
The Act places certain AI systems used in education and vocational training in the “high-risk” category (Annex III). These include:
Admission or placement of students in education institutions.
Assessment of learning outcomes (grading).
Determination of education level.
Monitoring of behaviour during exams and detection of cheating.
Education institutions that deploy high-risk AI systems (deployers) must: ensure the system does not decide autonomously and that human confirmation is required; inform students that they are being assessed by AI; ensure representativeness of data used and minimise discrimination risk.
Breach of these obligations leads not only to administrative fines (percentage of turnover) but also to students claiming compensation under GDPR and national law. In addition, the proposed AI Liability Directive aims to reverse the burden of proof (presumption of causality) for AI-related harm, requiring the institution to prove absence of fault.
Turkey does not yet have a standalone AI law, but the existing legal system provides a strong liability framework.
In Turkey, education is a public service carried out under state supervision. Under Article 125 of the Constitution, “The administration is liable to compensate damage arising from its own acts and decisions.”
Erroneous output from an AI algorithm used by a public school or university (wrong placement, wrong grade, unjust discipline) is characterised in administrative law as “malperformance of the service.” The administration’s failure to exercise due care in selecting, testing or deploying this technology is a “service defect.”
In some cases the administration may be held liable for risks inherent in the nature of the technology even without fault (risk principle).
Law No. 6698 on the Protection of Personal Data (KVKK) directly affects AI use.
Article 11/1-g: The data subject has the right to object to “a result unfavourable to the data subject arising from the analysis of processed data exclusively by automated systems.”
If a university cuts a student’s scholarship or rejects their application based solely on an algorithm’s analysis, the student may object to that outcome. If the institution cannot prove that the decision went through “human review,” it faces both KVKK administrative fines and having to compensate the student’s loss.
The “AI in Education Policy Document and Action Plan” and ethics guidance published by the Ministry of National Education (MEB) make a “human-centred” approach to AI use mandatory. These policy documents will be used by courts as reference in determining the “standard of care” in any litigation. Thus, a school that does not follow MEB’s ethics guidance will have its liability established more easily.
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To understand how theoretical risks materialise in practice, past incidents must be examined from a forensic perspective.
Following the cancellation of exams due to the COVID-19 pandemic, the International Baccalaureate Organisation (IBO) and the UK exam regulator (Ofqual) used algorithms to predict students’ grades.
The algorithm weighted school historical performance (School Context) and cohort past data more heavily than individual student performance (teacher estimate).
Grades of students in small or private schools were raised, while grades of high-achieving students in crowded state schools or schools with low historical performance were dramatically downgraded.
The regulator found IB’s grading model “unfair” and held it to be contrary to GDPR. It ordered students’ grades to be corrected.
Thousands of students lost university offers. Institutions faced loss from “erroneous processing” and massive reputational harm. Had these institutions not had insurance covering Reputational Harm, the financial fallout could have been far worse.
The algorithm’s “discriminatory” output was viewed not as a “product defect” but as a “management error.”
Tools such as Turnitin and GPTZero have been shown to have high error rates despite 99% accuracy claims, especially on complex academic texts and non-native writers.
School acquires and uses the tool → Tool gives a faulty report → School punishes the student → Student sues.
Who is liable? The company that produced the faulty tool, or the school leadership that blindly trusted it? Courts tend to hold the authority that made the final decision (the school) liable. This triggers the school’s ELL coverage.
The most critical financial tool for education institutions to manage AI risks is insurance. Yet current policy structures address AI risks in a fragmented way.
This insurance covers errors by school boards, administrators, teachers and staff arising from their professional (teaching) activities.
Covered: neglect of duty, inadequate supervision, discrimination (within limits), verbal harassment, unjust discipline.
ELL policies typically define Negligence by reference to human conduct. Is a software error “professional negligence”? Insurers may classify AI use not as an “educational activity” but as “technology use” or “administrative decision” and push it outside ELL scope. In particular, Non-bodily injury—e.g. a student’s lost future earnings—is often subject to lower limits or excluded in policies.
Although cyber insurance is seen as the main coverage for digital risks, it has significant limitations regarding AI.
Insuring Agreement A (Privacy Liability): Data privacy breach. Privacy violations such as in Ogletree (room scan) can fall here.
Insuring Agreement B (Network Security): Hacking, malware. Triggers if the AI system is hacked.
Missing piece: Algorithmic Errors. Most standard cyber policies do not cover the case where the system, without any security breach, simply “miscalculates” or produces a “discriminatory result.” That is not a “security failure” but a “performance failure.” Performance failures are typically a Tech E&O matter.
This policy covers financial loss to third parties resulting from technology products or services failing to perform as expected.
Normally Tech E&O is for technology companies (vendors). But if a university uses an algorithm it developed in-house or fine-tunes an open-source model on its own data, that institution is acting as a “tech provider.” Standard ELL and cyber policies do not cover this “developer” risk. The institution needs a form of Tech E&O coverage for its own operations.
Some insurers (e.g. Munich Re, Armilla AI), seeing these gaps in the market, have begun offering dedicated AI policies.
AI model underperformance (model drift).
Third-party compensation for algorithmic discrimination.
Regulatory defence costs—especially for AI Act and KVKK investigations.
Harm from hallucinations (wrong information output).
For the risk of student data theft, Educators Legal Liability (ELL) insurance generally does not provide coverage, while Cyber Insurance is the primary coverage. Tech E&O and Affirmative (AI-specific) policies typically do not cover this risk.
For wrong grade by algorithm (systematic error), ELL struggles to cover due to uncertainty whether it is “professional error”; Cyber does not cover because there was no data breach. Coverage for such “performance failures” is only possible if the institution has Tech E&O or is definitively provided by Affirmative (AI-specific) policies.
The scenario of discriminatory face recognition (Title VI) may be partially covered by ELL only if the policy has a discrimination clause. Cyber coverage is unclear (a media liability clause may be needed). This risk is covered by Tech E&O, and the strongest coverage is from Affirmative (AI-specific) policies.
Harm from wrong accusation by plagiarism software (reputation) is treated as an unjust disciplinary action and falls under ELL Insurance. Cyber and Tech E&O generally do not cover such reputational harm, while AI-specific policies may provide coverage for this risk.
Situations of privacy violation with Proctoring (Ogletree) remain in a grey area under ELL. This risk can, however, be covered under Cyber Insurance as Privacy Liability and is also covered by AI-specific policies.
Dive deeper into Turkey's IT law structure, regulatory authorities, and legal obligations for educational institutions, encompassing KVKK and cybersecurity in the digital age.
Compensation for loss caused by AI error is one of the most complex areas in the legal landscape.
If a student cannot get into Harvard because of an algorithm error and has to attend a lower-tier school, how is loss quantified?
Courts may use lifetime earning expectancy models. The difference between the average earnings of a Harvard graduate and a graduate of the other school, calculated with actuarial tables, can be claimed as “compensation.” These figures can run into millions of dollars and rapidly exhaust schools’ insurance limits (Aggregate Limits).
A plagiarism accusation can end an academic’s or student’s career. In such cases, defamation / reputational damage and emotional distress compensation come into play. It is critical that insurance policies explicitly include coverage for non-pecuniary compensation.
Schools should not bear AI risk alone. Contracts with technology providers (EdTech vendors) are vital for recourse to the source of the risk.
Schools must include the following in contracts:
“The provider will defend, indemnify and hold harmless the School against any third-party claim (including student litigation) arising from the use, outputs or errors of the AI product.”
Most AI firms (OpenAI, Google, Microsoft, etc.) state in their standard terms that “Risk of use of outputs is on you.” Education institutions should not accept this disclaimer, especially for “high-risk” decisions (grades, admission), and should seek guarantees as to output accuracy.
In standard contracts the provider’s liability is usually limited to “fees paid in the last 12 months” (Cap). A $20,000 Cap cannot cover $10 million in loss when 500 students sue over an AI error.
Schools should demand removal of the liability cap (uncapped liability) or a “Super Cap” of the order of 5–10 times the contract value for “Data Privacy Breaches,” “Intellectual Property” and “Bodily/Non-Pecuniary Harm.”
Education institutions and insurers must prepare for a new reality in 2025 and beyond.
As AI turns education from “service” into “product,” courts’ approach to Educational Malpractice claims will change. If teaching is done by AI, it is a product liability case and easier to win. To manage this risk, schools must never fully remove the human element from pedagogical processes.
Review your existing ELL and Cyber policies with an insurance broker or legal adviser. Identify Silent AI risk and consider Affirmative AI coverage or buy-back options.
For high-risk decisions (discipline, grades, admission) use AI only as decision support. Final sign-off and responsibility must always rest with a human. This both shifts legal liability from “product defect” to “professional judgment” (which is better protected by ELL) and ensures KVKK/GDPR compliance.
EdTech contracts should be reviewed not only by the “procurement” function but also by legal and risk management. Indemnity clauses, insurance limits and data use rights (model training) must be subject to tough negotiation.
The risks education institutions face with AI integration can no longer be addressed only within the “human error” framework. Our analysis clearly shows the risk paradigm shifting from “negligence” to technologically and statistically rooted defects: “system failure” and “algorithmic bias.” This major shift is making traditional legal doctrines and financial protection mechanisms inadequate.
In particular, the Silent AI hazard in traditional insurance products leaves institutions exposed to reputational loss, opportunity cost and high compensation claims arising from algorithmic errors. ELL and Cyber policies tend to cover “security breaches,” not “performance failures.”
Three critical steps are necessary to manage these existential risks:
Human control: Using AI for high-risk decisions (grading, discipline, admission) only as a decision-support tool and having final approval always given by Human-in-the-Loop is vital to shifting legal liability into the better-protected domain of professional judgment (ELL).
Insurance calibration: Institutions should identify gaps in their existing policies (Gap Analysis) and explicitly cover algorithmic error, discrimination and hallucination risks through Affirmative AI Coverage or buy-back options.
Contractual indemnification: In contracts with EdTech providers, securing the provider’s acceptance of liability through Super Cap mechanisms or uncapped liability is the most effective way to seek recourse to the source of the loss.
The AI revolution in education is not only a technological leap; it is also a call for recalibration in legal and financial management. The future of institutions will depend on how proactively they act at this complex intersection.
The legal and financial gaps created by the AI revolution in education demand expertise that goes beyond traditional risk management. Take proactive legal steps to protect your education institution or EdTech venture from the risk of millions in compensation from algorithmic errors, KVKK non-compliance penalties or reputational loss. Genesis Hukuk provides legal risk analysis of your AI systems, reviews your insurance policies for Silent AI risk and redrafts indemnification clauses in your vendor contracts to ensure the highest level of protection. Contact us today for your strategy to manage AI-related risks in education and move into the future with confidence. Our expert team is with you even in the most complex legal challenges.
