When Universities Punish Students
When Universities Punish Students for Using AI, Everyone Loses
A doctoral student in health economics sat down to take a preliminary exam in August 2024. He was traveling in Morocco. The exam allowed class materials. It prohibited artificial intelligence (AI) tools. Weeks later, a faculty committee flagged his answers as inconsistent with his writing voice. They ran the exam through a detection tool. The tool flagged similarities. He was reported for academic dishonesty.
He was expelled. His student visa was revoked. His academic career ended on the basis of a detection score from a tool with a documented 50 percent false accusation rate.
That case is not an outlier. It is a pattern. And it raises a question that every institution in the country is now being forced to answer: when a student uses AI, is that a problem to punish or a skill to teach?
The scale of the problem is not a projection
Before looking at what universities are doing about AI, it helps to understand how completely AI has already arrived on campus.
Created in Midjourney.
By The Numbers
86% of college students use AI in their academic work
95% of students and educators use AI tools of some kind
26% of institutions have a formal AI policy in place
That last number is the one that matters most. Only 26 percent of institutions are operating under a formal AI policy. Which means at the vast majority of U.S. colleges and universities, students are navigating a rule that has not been written yet. Faculty are enforcing expectations they have not clearly communicated. And when something goes wrong, the institution reaches for a detection tool that was never designed to carry that weight.
That is not a governance strategy. It is a governance vacuum with a lawsuit waiting inside it.
What happens when detection tools make the call
The problem with using AI detection tools as the primary evidence in a misconduct case is not that the tools exist. It is that they are not reliable enough to justify the consequences institutions are attaching to their results.
"With traditional plagiarism analysis, a human can examine how a tool reached its conclusions and verify or dismiss the finding. That is not possible with AI detectors. They are black boxes." |
That observation came from a plagiarism expert commenting on the Adelphi University case, in which a 19-year-old student named Orion Newby was accused of submitting AI-generated work after his professor ran his essay through Turnitin. The tool returned a result of 100 percent AI-generated. Newby denied the accusation. He submitted the same essay to multiple other detection tools. All of them concluded the work was human-written.
Newby has learning and neurological disabilities. Writing patterns that fall outside statistical norms for standard academic prose are exactly the kind of work that triggers false positives in these tools. A New York court reversed the expulsion in early 2026.
He is not alone. Three documented cases have now produced or are producing active litigation.
Created in Midjourney.
Case Study: University of Minnesota
Haishan Yang, Ph.D. Candidate in Health Economics
Expelled in 2024 based substantially on GPTZero detection output, which carries a reported 50 percent false accusation rate in some analyses. His academic advisor, a professor with over 40 years of experience at the university, testified that expulsion was "an odd punishment for something that is as difficult to establish as a correspondence between ChatGPT and a student's answer." The Minnesota Court of Appeals upheld the decision in February 2026. Federal litigation continues.
Case Study: Yale School of Management
The institutions that saw this coming have since updated their guidance. Vanderbilt, Yale, the University of Michigan, the University at Buffalo, and the University of Minnesota itself have all warned that AI detection scores should not serve as primary or sole evidence in misconduct cases. Washington State University cancelled its Turnitin AI detection contract entirely in February 2026. The University of Texas at Austin went further, prohibiting faculty from using unauthorized third-party AI detection software and warning instructors that they could be held personally liable for legal costs if a student brings a claim on privacy or copyright grounds.
The tool was never the policy. Institutions that treated it as one are now discovering what that costs.
Case Study: Adelphi University
Orion Newby, Undergraduate Student
Accused of submitting AI-generated work after Turnitin returned a 100 percent AI result on his essay. Newby has documented learning and neurological disabilities. Alternative detection tools found no AI-generated content. A New York court reversed the expulsion in early 2026. The case is now cited as a turning point in how courts assess AI detection as evidence.
The institutions that saw this coming have since updated their guidance. Vanderbilt, Yale, the University of Michigan, the University at Buffalo, and the University of Minnesota itself have all warned that AI detection scores should not serve as primary or sole evidence in misconduct cases. Washington State University cancelled its Turnitin AI detection contract entirely in February 2026. The University of Texas at Austin went further, prohibiting faculty from using unauthorized third-party AI detection software and warning instructors that they could be held personally liable for legal costs if a student brings a claim on privacy or copyright grounds.
The tool was never the policy. Institutions that treated it as one are now discovering what that costs.
Created in Midjourney.
What the other model looks like
While some institutions were building enforcement infrastructure, others were building something different. The contrast is not subtle.
Ohio State University launched an AI fluency initiative in June 2025 with a stated goal: every undergraduate in the class of 2029 will graduate fluent in their major field and in the application of AI within that field. That is not an elective. It is a graduation requirement. More than 1,300 faculty members engaged with the professional development program in its first two months.
The University of California, Davis developed the Peer and AI Review and Reflection (PAIRR) model, a five-step curricular intervention in which students give peer feedback on drafts, prompt AI to review the same drafts, then critically compare the two types of feedback before revising. The model won a $1.5 million grant from the California Learning Lab and is now scaling across eight campuses.
The University of South Carolina added an AI Literacy Certificate open to students from all majors, paired with a $1.5 million partnership with OpenAI that gives every student, faculty member, and staff member on campus free access to ChatGPT.
McCombs School of Business at the University of Texas at Austin tried the restriction model first. It did not last. Leadership concluded that trying to protect assignments from a technology that was rapidly becoming pervasive was not a viable strategy. They now offer 10 digital skill badges, a dedicated AI Essentials pathway for MBA students, a weekly faculty knowledge-sharing session, and an AI Executive Panel Simulator.
The two models, side by side
Measure | Punitive Model | Integration Model |
Legal exposure | High: active litigation at Minnesota, Yale, Adelphi | Low: no documented litigation |
External funding | None documented | $1.5M grants; institutional partnerships |
Faculty development | Minimal: detection tool deployment | Active: 1,300+ OSU endorsement participants |
Student outcomes | No improvement documented | Measurable: workforce-ready AI skills |
Employer alignment | Misaligned | Aligned: employers expect AI fluency |
The workforce reality institutions cannot afford to ignore
This is not a values argument. It is a market argument.
A 2025 Microsoft AI in Education Report found that 37 percent of students use AI to brainstorm assignments, 33 percent to summarize information, and 32 percent to get feedback on their work. A 2026 Coursera report found that students at AI-friendly institutions report higher academic performance. A 2025 Insight Global survey found that 29 percent of job seekers used AI to customize their job applications, up from 17 percent the previous year.
Employers are not asking whether graduates used AI. They are asking whether graduates can use it well. A student who gets suspended for AI use does not leave the institution more prepared. A student who completes a structured AI literacy curriculum does.
The institutions generating lawsuits today are the ones that never built an AI literacy infrastructure in the first place. Punishment filled the policy vacuum that education should have occupied.
Created in Midjourney.
Three principles the evidence supports
Across the institutions that have built integration models, three structural principles show up consistently.
First, they treat AI fluency as a workforce readiness outcome. The starting question is not how to stop students from using AI. It is what a graduate needs to know about AI to compete after they leave.
Second, they build structured transparency into the learning experience. Students practice using AI openly, reflect critically on its output, and learn to decide when and how to use it. The skill is in the judgment, not in the avoidance.
Third, none of the effective models rely on AI detection tools as a primary enforcement mechanism. The investment goes into instructional design. The surveillance infrastructure never gets built.
Frequently asked questions
Are AI detection tools reliable enough to use in disciplinary proceedings?
No, not as sole or primary evidence. Turnitin acknowledges a nonzero false positive rate. Independent analyses of GPTZero have reported false accusation rates near 50 percent in some conditions. Courts in the Adelphi University case reversed an expulsion where Turnitin was the primary evidence. Multiple universities have since updated guidance to prohibit using these tools as the sole basis for misconduct findings.
Which universities have been sued over AI-related disciplinary decisions?
As of September 2026, documented litigation involves the University of Minnesota, Yale School of Management, and Adelphi University. Additional cases may not be in the public record.
What does a good university AI policy actually look like?
The strongest policies share three features: they treat AI fluency as a learning objective rather than a conduct risk, they build transparent AI use into the curriculum rather than policing it, and they do not rely on detection tools as enforcement. Ohio State, UC Davis, USC, McCombs, and Columbia Law School all offer documented examples.
What percentage of students are actually using AI in their coursework?
A 2024 study found 86 percent of college students use AI in their academic work. A 2026 report found 95 percent of students and educators use AI tools of some kind. Only 26 percent of institutions have a formal policy governing that use.
Does this apply to businesses, or just universities?
The structural logic is identical. Any organization responding to employee AI use with restriction and surveillance rather than training and clear guidelines is making the same error the punitive universities made. The risk profile is different, but the outcome pattern is the same.
The close
A student in Morocco sat down to take an exam. He wrote his answers. He submitted them. Months later, a detection score ended his academic career in the United States.
That outcome was not inevitable. It was a policy choice. A different institution, with a different framework, would have handled the same situation differently. Not necessarily more leniently. More accurately.
The universities that are not in court right now are the ones that decided to educate instead of police. That is not a coincidence. It is the finding.
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Michael Carmine
Founder & CEO | AIEducationalSolutions.org
About the Author
Michael Carmine, Ed.D., MBA, is the Founder and Chief Executive Officer of AI Educational Solutions (AIES), a boutique AI consulting and training firm based in Chapin, South Carolina, serving small and medium businesses across the Midlands and nationally. He holds a doctorate in Curriculum and Instruction with a specialty in Instructional Design and Technology, and has trained over seven hundred professionals across the United States, South Korea, the Netherlands, and Qatar. Most AI consultants come from technology. Michael comes from education, and that difference shapes every tool he builds.
