AI Regulation and Higher Education in the US: What Your Institution Needs to Know

AI regulation is accelerating — and universities are in scope

The United States does not yet have a single comprehensive federal AI law equivalent to the EU AI Act. However, the regulatory landscape is far from empty. The October 2023 Executive Order on Safe, Secure, and Trustworthy AI (EO 14110) set the federal direction, the NIST AI Risk Management Framework (AI RMF) provides the compliance playbook, and a growing wave of state AI laws — from Colorado's SB 24-205 to California's proposed regulations — is creating binding obligations (Source: White House, NIST AI RMF).

For higher education institutions, this is not an abstract topic. The moment a university deploys an admissions chatbot, a candidate-scoring tool, an AI plagiarism detector or an algorithm that recommends study programs, it is deploying an AI system that falls under existing federal guidance and emerging state regulation. Beyond AI-specific rules, FERPA already governs how student data can be used in automated systems, and the FTC has signaled active enforcement against deceptive or unfair AI practices.

The question is not whether your institution should pay attention to AI regulation — it should. The question is which frameworks apply and how to prepare.

The US AI regulatory landscape: federal and state layers

Unlike the EU's single-regulation approach, the US operates on multiple layers. Understanding each layer is essential for compliance planning.

Federal layer: Executive Orders and NIST

The Executive Order on AI (EO 14110) directs federal agencies to develop AI safety standards, requires transparency in government AI use, and instructs the Department of Education to produce guidance on AI in education. While the EO does not directly regulate universities, it shapes the standards that accreditation bodies and federal funding agencies will enforce.

The NIST AI Risk Management Framework (AI RMF 1.0) is the de facto compliance standard for US organizations. It organizes AI risk management around four functions: Govern, Map, Measure, and Manage. Although voluntary, the AI RMF is increasingly referenced by federal agencies, state regulators, and accreditation bodies as the benchmark for responsible AI deployment. Institutions that align with AI RMF now will be ahead when binding requirements arrive.

State layer: a patchwork with real teeth

State AI laws are where binding obligations emerge fastest. Key developments:

• Colorado SB 24-205 — Requires deployers of "high-risk AI systems" (including those that make consequential decisions about education) to conduct impact assessments, provide notice to consumers, and maintain documentation. Effective February 2026.
• California — Multiple bills in progress targeting automated decision-making, with proposed requirements for bias audits and transparency in AI systems used for admissions and employment.
• Illinois AI Video Interview Act — Already in effect, requires consent before AI analysis of video interviews — relevant for institutions using video in admissions.
• Connecticut, Texas, Virginia — Active legislation on AI transparency and algorithmic accountability.

The trend is clear: state-level regulation is accelerating, and higher education is explicitly in scope for any law targeting automated decisions about individuals.

FERPA: the existing framework that already governs AI

The Family Educational Rights and Privacy Act (FERPA) is not an AI law, but it directly constrains how institutions can use AI with student data. Key implications:

• AI tools processing education records must comply with FERPA's data sharing restrictions. Feeding student transcripts, grades, or behavioral data into a third-party AI system requires a valid FERPA exception (school official, legitimate educational interest)
• Students and parents have the right to inspect records that inform decisions about them — including AI-generated assessments or scores
• Outsourcing to AI vendors does not remove FERPA obligations. The institution remains responsible for ensuring vendor compliance through contracts that include data use limitations

Risk classification: applying NIST AI RMF to your institution

The NIST AI RMF does not prescribe rigid risk categories like the EU AI Act. Instead, it provides a flexible framework for institutions to assess and manage AI risk based on context. Here is how to apply it to common university AI use cases.

High-risk AI systems in higher education

The following uses carry the highest risk profile and demand the most rigorous controls:

• Automated candidate-screening tools — any system that filters, ranks or scores applications based on algorithmic criteria. The US Department of Education has flagged algorithmic admissions as a priority area for guidance.
• AI plagiarism detectors that influence grading or academic assessment. AI detectors currently show a false-positive rate of 5 to 15% (Source: Stanford HAI, 2025). They disproportionately flag non-native English speakers, raising Title VI and civil rights concerns.
• Placement or orientation algorithms that determine access to specific programs or course sections.
• Automated grading systems that produce or influence academic evaluations.

For these systems, NIST AI RMF recommends: documented governance policies, bias testing across protected classes (race, gender, national origin, disability under Title IX, ADA, and the Civil Rights Act), human oversight for all consequential decisions, and regular impact assessments.

Limited-risk AI systems

Admissions and information chatbots fall into this category. The primary obligation is transparency: inform the user that they are interacting with an AI system, not a human. The FTC has made clear that deceptive AI interactions — where a user reasonably believes they are speaking with a human — constitute an unfair or deceptive practice.

In practice, your chatbot must clearly state that it is an AI assistant. A message such as "I am an AI assistant for [Institution Name]. A human adviser is available on request" is sufficient.

Also in this category:

• AI-generated content systems (automated emails, program descriptions)
• Emotion recognition systems (tone analysis in video interviews — subject to the Illinois AI Video Interview Act and emerging state laws)
• Automatic translation tools for pedagogical content

Minimal-risk AI systems

AI tools with no impact on individual rights: spell checkers, spam filters, timetabling optimizers. No specific regulatory obligations, though transparency best practices remain recommended.

How AI regulation interacts with FERPA and civil rights law

AI regulation in the US does not exist in isolation — it layers on top of existing federal protections that carry significant enforcement weight.

FERPA governs the collection, storage and processing of student education records. Any AI system that accesses these records must comply with FERPA's consent, notice and access requirements. This is not theoretical: the Student Privacy Policy Office has issued guidance specifically addressing third-party AI tools and cloud services.

Title IX prohibits sex-based discrimination in education programs receiving federal funding. An AI admissions tool that produces disparate impact by gender triggers Title IX liability — regardless of whether the bias was intentional.

The ADA and Section 504 require accessibility for students with disabilities. AI systems that disadvantage students with disabilities (for example, an AI proctor that flags atypical behavior as cheating) create compliance risk.

The Civil Rights Act (Title VI) prohibits discrimination based on race, color, or national origin. AI plagiarism detectors that disproportionately flag non-native English speakers raise Title VI concerns that institutions must address.

For a deeper dive into data compliance specific to student data, see our dedicated guide.

10-point compliance checklist

1. Inventory: list all AI tools (chatbot, CRM, scoring, plagiarism, recommendation, proctoring)
2. Risk assessment: apply NIST AI RMF to classify each tool by risk level
3. Vendor audit: demand compliance documentation, bias audit results, and FERPA compliance commitment from each supplier
4. Chatbot transparency: AI identification + human contact option on all automated interactions
5. Human oversight: no admissions decision fully automated — maintain human-in-the-loop for all consequential decisions
6. Bias testing: regular audits across race, gender, national origin, disability status and socioeconomic background
7. FERPA compliance: ensure all AI tools processing student data have valid FERPA agreements (school official exception documented in contracts)
8. Privacy policy: integrate AI processing disclosures, aligned with state privacy laws (CCPA, state data breach notification laws)
9. Training: admissions and academic teams on their obligations under FERPA, Title IX, ADA and emerging AI laws
10. Annual review: AI audit aligned with accreditation review cycles and state regulatory updates

Sanctions and enforcement

Enforcement in the US comes from multiple directions. The FTC can impose penalties for deceptive or unfair AI practices — with fines reaching $50,000+ per violation under the FTC Act. FERPA violations can result in loss of federal funding, which for most institutions is existential: federal student aid (FAFSA-based Pell Grants, Stafford Loans) represents the largest revenue stream for many schools.

State attorneys general can enforce state AI and consumer protection laws, with penalties varying by state. Colorado's SB 24-205, for example, provides for civil penalties and private rights of action.

Beyond financial penalties, the reputational risk is at least as concerning: a university sanctioned for AI-related discrimination or privacy violations undermines its credibility to train the next generation of digital professionals. Accreditation bodies (SACSCOC, HLC, MSCHE, WASC, NEASC/NECHE, NWCCU) are also beginning to incorporate AI governance into their review criteria.

What universities should demand from AI vendors

As deployers of AI systems, institutions share responsibility. Demand from each vendor: a dated compliance statement covering FERPA, state AI laws, and NIST AI RMF alignment; risk classification with justification; accessible technical documentation; contractual commitment to human oversight and transparency; a documented bias audit with results disaggregated by protected class; and a regulatory update plan that tracks evolving state requirements.

For insight into how AI influences university visibility beyond compliance, our article on AI recommendation criteria for universities explores the topic in depth.

FAQ

Is my admissions chatbot classified as high risk under US AI regulation?

Not under most current frameworks, unless it makes autonomous admissions decisions. A chatbot that informs, answers questions and qualifies prospects is considered limited risk. It must identify itself as AI and offer human access, but is not subject to the heavier obligations that apply to automated decision-making tools. However, if the chatbot automatically decides to accept or reject a candidacy, it moves into high-risk territory under state laws like Colorado's SB 24-205 and under NIST AI RMF guidance.

Do federal AI regulations apply to private universities?

Yes. While the Executive Order on AI primarily directs federal agencies, its downstream effects touch all institutions. FERPA applies to any institution that receives federal student aid — which includes virtually all accredited private universities. State AI laws apply based on where the institution operates or where the affected individuals reside, regardless of public or private status. The FTC's authority over deceptive practices applies broadly to all organizations.

What is the relationship between FERPA and AI regulation for student data?

FERPA governs the collection, storage and processing of student education records. AI regulation adds obligations on how that data is used by AI systems (transparency, human oversight, bias auditing). FERPA compliance does not exempt from AI regulation compliance, and vice versa. Institutions need to satisfy both layers simultaneously.

How much time and budget should we plan for compliance?

For a university using a chatbot (limited risk) and a CRM with basic scoring: 2 to 4 weeks and $3,000 to $10,000 in auditing and adjustments. For a university using an automated screening system (high risk): 2 to 4 months and $20,000 to $60,000, including vendor compliance documentation, bias audits, and FERPA agreement updates. Institutions in states with active AI legislation (Colorado, California, Illinois) should budget for legal review of state-specific requirements.