As artificial intelligence rapidly transforms the landscape of higher education, college and university administrators are finding themselves on the frontlines of new, and sometimes unprecedented, legal challenges.
While much of the public discourse has focused on students using AI tools to cheat, the legal implications for administrators are far broader. From data privacy laws to anti-discrimination requirements and the complexities of policy development, the legal environment is evolving just as quickly as the technology itself.
This article identifies three significant AI-related legal issues facing higher education administrators—and provides actionable suggestions to navigate these challenges.
Data privacy, security, and compliance
The challenge
AI systems in higher education collect and process large amounts of personal and institutional data, including student records, behavioral analytics and, increasingly, biometric information. Administrators face overlapping data privacy laws, including the Family Educational Rights and Privacy Act, evolving state privacy statutes, and international regulations such as the General Data Protection Regulation.
These laws establish strict requirements for how student and institutional data must be collected, stored and shared, and they are designed to protect individual privacy and prevent unauthorized access or misuse of sensitive information. As AI technologies become more pervasive, ensuring compliance with these regulations is crucial to avoid legal penalties and maintain trust within the academic community.
AI-driven platforms often share data with third-party vendors, raising substantial questions about consent, control and oversight. As we enter a new year, the legal liability for data breaches or improper sharing is heightened by increased regulatory enforcement and class action litigation regarding student and employee data privacy.
To protect against this liability:
- Ensure contracts with third-party vendors require compliance with all relevant privacy laws and set clear protocols for breach notification and data handling
- Provide clear, advance notice and obtain valid consent whenever personal data, especially sensitive or student/employee data, is collected or shared
- Collect and retain only the minimum amount of data necessary for the stated purpose
- Continually audit vendors and internal processes for compliance, and quickly address potential vulnerabilities
- Have robust response plans for breaches, and be prepared with documentation of compliance efforts.
Practical steps for administrators
- Audit existing data practices: Proactively conduct an audit of all AI systems and third-party vendors to map data flows and ensure compliance with current federal and state laws and regularly review how institutional stakeholders use and input information into AI systems.
- Update contracts: Strengthen contract language with vendors to mandate compliance with privacy standards, notification protocols, and data protection requirements.
- Develop clear policies: Draft and disseminate clear institutional privacy and contracting policies tailored to AI tools and ensure ongoing training for staff and students.
Algorithmic bias and discrimination risks
The Challenge
AI systems used for admissions, grading, advising and faculty hiring can inadvertently perpetuate or amplify bias, potentially running afoul of federal antidiscrimination laws such as Title VI, Title IX and the Americans with Disabilities Act.
Algorithms trained on historical data may entrench past inequities, and lack of transparency can make it difficult to audit decision-making.
Litigation and regulatory actions alleging disparate impact and failure to prevent discrimination are increasing, putting institutional reputation and accreditation at risk. Institutions adopting AI systems in admissions, evaluation and other critical processes must proactively address potential sources of bias and ensure compliance with relevant antidiscrimination laws.
Key lessons include the importance of ongoing auditing and transparency in AI decision-making, involving diverse stakeholders in system design, and regularly updating algorithms to reflect current, equitable standards rather than outdated or biased historical data.
Proactive risk management, including legal reviews and bias mitigation strategies, not only helps avoid liability but also upholds institutional values of fairness, inclusion, and integrity.
Practical Steps for Administrators
- Assess and monitor AI systems: Require regular, independent testing of AI systems for evidence of disparate impact or discriminatory outcomes.
- Demand explainable AI: Insist that vendors provide audit trails and “explainability” features for all decision-making algorithms.
- Build diverse oversight panels: Establish interdisciplinary committees—including legal, ethics, IT and DEI representatives—to oversee adoption and review of AI tools.
Academic integrity and AI-generated content
The Challenge
While AI-enabled cheating is top-of-mind, the legal issues go deeper: unclear policies about AI use, inconsistent enforcement and concerns about due process. Ambiguous rules expose institutions to challenges by students and faculty alike, particularly when disciplinary action is taken.
Federal grant agencies are increasingly regulating the use of AI in applications, with some restricting or requiring disclosures where AI-generated content is included. Defining and enforcing what constitutes permissible AI use will be a central legal and reputational challenge for institutions next year and beyond.
Practical steps for administrators
- Clarify codes of conduct: Update student and faculty codes of conduct to explicitly address AI and its varied uses in academic work.
- Ensure procedural fairness: Prepare due process protocols and hearing procedures for alleged AI-related infractions, to reduce the risk of successful legal challenges.
- Educate the community: Launch ongoing educational campaigns highlighting ethical AI use, provide guidance on distinguishing between collaboration and misconduct, and provide timely information on compliance with federal regulations and agency-specific guidance on the use of AI in sponsored programs.
Leading in a new era
For higher ed administrators, successfully navigating the legal landscape of AI in 2026 demands a proactive, multidisciplinary approach. By prioritizing data privacy, guarding against algorithmic bias and updating policies around academic integrity, institutions can mitigate risk while harnessing the benefits of AI.
Implementing these practical measures will position universities not merely to comply with the law, but to lead in this new era of technology-enhanced education.
