EdLawConnect Blog

Institutions that take proactive steps now to establish expectations, review policies, engage governance bodies, and educate campus communities will be better positioned to navigate this landscape and remain effective in an evolving technological landscape.  Act now to clarify what is required versus recommended, strengthen oversight, and train stakeholders. 

From Restriction to Responsibility: Managing AI on Campus

Many institutions are moving away from blanket prohibitions on AI and instead incorporating frameworks that emphasize responsible use, transparency, and accountability. Existing academic integrity policies often predate generative AI and may not clearly address when AI assistance is permissible, when disclosure is required, or how AI-related misconduct will be evaluated. As a result, institutions are increasingly revisiting policies and guidance to provide greater clarity for students, parents and faculty. Required elements should include clear definitions, permitted uses with disclosure expectations, prohibited conduct, and procedures for evaluation and enforcement.  Recommended elements include model syllabus language, examples by discipline, and periodic review cycles. 

Faculty Discretion and System Guidance

The Chancellor’s Office maintains an AI website and has developed systemwide guidance and a policy framework. (https://ai.cccco.edu.) Ultimately, faculty retain discretion regarding authentic assessment of student abilities because educational objectives vary across disciplines and courses. As such, policy creation and/or updates regarding the use of AI in supporting student success will require some level of collegial consultation and Academic Senate involvement.[1]

Employee Use: Privacy, IP, Bias, and Accountability

Faculty, administrators, and staff increasingly use AI for research, communications, data analysis, and administrative functions, raising important questions regarding confidentiality, data privacy, accuracy, intellectual property, and oversight. AI also raises ethical concerns regarding perpetuating bias, copyright, data privacy, accuracy, and environmental impact. Policy language should address human review of AI outputs, prohibit entry of confidential or regulated data into public tools, and assign accountability to the human operator. Recommended practices include providing an approved tool list, creating reasonable retention standards, and testing for bias/accuracy. 

Emerging Risks: Pell Runners/Ghost Students and AI-Assisted Fraud

Institutions face rising enrollment and financial aid fraud by “Pell runners” or “ghost students,” with AI enabled false identities, fabricated documents, automated communications, and even participation in coursework. In 2024, California Community Colleges reported that out of 116 campuses, nearly 31% of all applications received were fraudulent, which resulted in $3 million in state funding and $10 million in federal funding losses. The federal government reported nearly $350 million in losses within the last five years. Colleges should reassess enrollment verification, identity confirmation, and fraud detection practices as AI capabilities evolve.  To address these issues, it is helpful to coordinate your agency’s plan across admissions, financial aid, IT, student services, and departments to identify and respond to suspicious activity.

Policy Check-Up and Training: Are Your Institution’s Policies Ready for AI?

Many institutional policies (academic integrity, student conduct, and acceptable use) were developed before generative AI became commonplace and may not set clear expectations for students, faculty, and staff. Policy revisions are most effective when paired with training that addresses AI opportunities and limitations and promotes consistency.

Lessons from Early Institutional Adopters of AI

After reviewing the Chancellor’s guidance and policy framework, CCCs can learn a lot about tailoring an AI policy for their district from other California public educational institutions. For example, after using the Chancellor’s Guidance, UC Berkeley Law has published its own AI use policy as it relates to academic integrity.  This policy acknowledges the potential value of AI tools while also outlining prohibited uses and the need for compliance with both the law school’s academic standards and the preservation of the legal profession. Similar approaches are emerging across higher education, with post-secondary institutions increasingly focusing on transparency, human oversight, and clear expectations on fair use rather than categorical bans.

Preparing for What is to Come

AI is no longer a future issue for higher education institutions to tackle. It is a present operational, academic, and governance challenge. Our team here at AALRR is here to help ensure your institution is at the forefront of the era of AI.

Quick Checklist & Next Steps

1. Update academic integrity and personnel policies to clearly define acceptable AI use for students and employees, including disclosure expectations and prohibited uses. Existing policies often predate AI and lack clarity on permissibility and required citations.
2. Require human oversight of AI-generated work and make employees accountable for final work product.
3. Protect confidential, personnel, and proprietary information by limiting what can be entered into institutionally approved AI tools.
4. Address AI risks such as bias, inaccuracies, and fraud through appropriate oversight and stronger identity verification tools, including in the areas of enrollment and financial aid.
5. Work with faculty and shared governance bodies to ensure AI policies align with institutional requirements while preserving faculty discretion.
6. Map where AI is being used across academics and operations; identify high-risk use cases and required controls.

Please contact the authors of this blog post or your regular AALRR counsel if you have any questions regarding this topic. 

[1] (ASCCC Academic Integrity Policies in the Age of Artificial Intelligence (AI) Resource Document; 5 CCR §§ 55200 et seq.). 

This AALRR publication is intended for informational purposes only and should not be relied upon in reaching a conclusion in a particular area of law. Applicability of the legal principles discussed may differ substantially in individual situations. Receipt of this or any other AALRR publication does not create an attorney-client relationship. The Firm is not responsible for inadvertent errors that may occur in the publishing process.

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