Courts Push Back on AI Detection Tools in Academic Misconduct Cases
Universities that rely heavily on AI detection software to catch cheating face legal risk. A New York court this year sided with a student who challenged his expulsion based partly on an AI detector's findings, signaling that institutions need stronger due process protections and should treat these tools as leads, not proof.
The case, Newby v. Adelphi University, involved an undergraduate accused of using generative AI on an essay. Turnitin, an AI detection service, flagged the work with a score of 100%, suggesting it was AI-generated. The university found him in violation of its academic integrity policy. When he sued, the court denied the university's motion to dismiss, ruling that Adelphi had failed to meaningfully consider the student's evidence during appeal.
The court emphasized that due process rights apply even in academic misconduct cases, not just criminal proceedings.
Why AI Detectors Fail
AI detection tools work differently from traditional plagiarism checkers. Plagiarism software compares student work against a database of published material and prior papers. AI detectors can't do that-generative AI isn't in any database. Instead, they flag patterns: uniform sentence structure, specific syntax, and other stylistic markers. They then assign a probability score, like "85% likely AI-generated."
Unlike plagiarism findings, which can pinpoint exactly what was copied and from where, AI detector results are probabilistic guesses. The company behind one of the leading AI platforms launched an AI detection tool and quickly withdrew it, concluding it wasn't accurate enough.
Studies have documented additional problems. Research shows AI detectors consistently misclassify writing by non-native English speakers as AI-generated, while accurately identifying native English writing. Other research found that detectors fail to identify AI-rewritten text.
A Different Outcome in Minnesota
Not all courts have ruled against universities. In Yang v. Neprash, a Minnesota federal court sided with the University of Minnesota when a Ph.D. student sued after expulsion for using AI on a doctoral exam.
The key difference: the professor didn't rely on an AI detector. Instead, he concluded AI was used because the student's answers contradicted his earlier writing style and contained material not covered in class. The court found the student received adequate notice, representation, a chance to present evidence, and appellate review-all the due process required.
What Institutions Should Do Now
Universities should update policies to address generative AI explicitly. Best practices include:
- Treating AI detection tools as screening devices only, not proof of cheating. If a detector flags work, use it as a reason to follow up with the student.
- Protecting due process by allowing students to challenge any AI detector's reliability during misconduct hearings.
- Clarifying in institutional policies what uses of generative AI are permitted and what are prohibited.
Instructors should rethink assessment methods. Clear syllabi statements about AI use help. So do supervised exams using blue books, oral exams, in-class writing, multiple draft submissions, or assignments requiring personal engagement that AI can't easily complete.
For educators navigating these issues, understanding both the technical limitations of AI detectors and the legal standards for academic misconduct is essential. AI for Education resources and an AI Learning Path for Teachers can help institutions develop sound policies.
The courts have signaled that technology alone won't solve academic integrity problems. Institutions that rely on detectors without robust due process protections risk litigation and reversed decisions.
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