AI Detection Tools and Academic Punishment: How Opaque Evidence Threatens Due Process
As artificial intelligence (AI) tools become common in education, schools are increasingly turning to AI detection software to identify potential academic misconduct [1]. While it is understandable that teachers, administrators, and professors in the United States want to implement rapid measures to combat AI use in academic assignments, caution is warranted before adopting sweeping measures. In response to growing demand, some companies have developed proprietary AI detection tools that claim high accuracy in identifying whether a piece of text was generated using AI, as well as estimating the extent to which AI was used in its composition. However, punishing students based solely on the results of these tools raises serious procedural due process concerns [2]. AI detection tools are opaque, given that they are based on probabilistic, and often unchallengeable, algorithmic outputs [3]. In accusing students of misconduct based on evidence from these opaque detection reports, schools risk punishing students based on faulty methods, unreliable evidence, or an unfair process, opposed to what is often required by law. The stakes are high, as being found to use generative AI in academic settings could have serious academic consequences [4]. These high stakes directly implicate students’ constitutional and institutional rights, particularly their entitlement to fair procedure, transparent evidence, and a genuine opportunity to challenge allegations of misconduct. In this article, I argue that public schools and universities risk violating the Fourteenth Amendment when they discipline students based solely on the results of AI detection tools. Due process under Goss v. Lopez and Dixon v. Alabama requires access to reliable, testable evidence and a meaningful opportunity to contest the factual basis of the charge, both of which are incompatible with the probabilistic and opaque nature of many AI detection tools.
Recent litigation and academic research highlight the risks of relying on AI detection tools as the primary evidence of academic misconduct. In 2024 litigation in Massachusetts, a student’s parents sued the school, alleging procedural due process violations and unwarranted penalties against their child [5]. While proving AI usage in this case was fairly easy, as the student included AI-generated sources in their assignment that did not exist in real life, relying on AI detection tools alone is often unreliable [6]. Even though the student’s AI usage was clear, the lawsuit highlights the procedural safeguards, like the opportunity to challenge evidence, that remain critical to ensuring fair due process. The case thus exemplifies why schools must treat AI-generated evidence cautiously and integrate it into a broader fair adjudication process rather than using it as sole proof of misconduct. Case law makes it clear that students should have a property interest in their education and should have an opportunity to be heard before disciplinary action is taken [7]. However, since the algorithms, training data, and evaluation methods used to generate the AI percentage scores are typically undisclosed, it’s difficult for students to test the accuracy of the results. The implications are substantial: students are unable to challenge the reliability of the evidence upon which their accusations are based because no one fully knows the algorithms used to make the AI detection reports. These practical limitations show that, when used in isolation, AI detection tools are insufficiently reliable and transparent to serve as the primary basis for disciplinary decisions.
While the opacity of AI detection tools prevents students from meaningfully contesting the evidence against them, the tools are also fundamentally unreliable as indicators of academic misconduct. For example, I uploaded President George W. Bush’s 2001 inaugural address to ZeroGPT, one of the leading AI detection platforms. This platform found it to be 83% “AI/GPT Generated.” This result is clearly erroneous, given that President Bush’s 2001 inaugural address predates the existence of generative AI systems, making it impossible for the speech to have been produced using such tools. Researchers at the University of Pennsylvania, University College London, King’s College London, and Carnegie Mellon University found that AI detectors are “not yet robust enough for widespread deployment or high-stakes use” and that many of the detectors they tested “are nearly inoperable at low false positive rates” [8]. Similarly, a 2023 study by researchers at eight different universities found that available detection tools are unreliable and easily manipulated [9]. These findings support the argument that using AI detection tools as primary evidence of academic misconduct is legally fraught. If detection tools are prone to error or manipulation, relying on them risks violating students’ constitutional and institutional rights to fair adjudication.
In criminal law, where evidence is also presented to determine culpability, courts require that forensic evidence is scrutinized before they are relied upon to justify punishment. In cases where there is a history of the evidence collection tool being unreliable or where evidence cannot be meaningfully tested, as is the case with reports made by AI detection tools, due process concerns are raised. Just as fines and jail time in the criminal justice system require rigorous evidentiary standards and due process protections, suspensions and expulsions can significantly affect a student’s education and future opportunities, and therefore demand similarly reliable forms of evidence.
The tension between opaque algorithmic evidence and 14th Amendment constitutional guarantees becomes especially clear when analyzed through the procedural due process lens set forth in Goss v. Lopez and Dixon v. Alabama. Under Goss v. Lopez and Dixon v. Alabama, procedural due process requires meaningful notice and a real opportunity to contest the evidence. In 1961, the United States Court of Appeals for the Fifth Circuit ruled in Dixon that publicly funded colleges cannot expel a student for misconduct without the due process guarantees of proper notice and a hearing [10]. Though older, this Fifth Circuit precedent remains foundational in higher-education due process law, and confirms that students at public universities cannot be disciplined without constitutional procedural protections. In Goss, fourteen years later, the Supreme Court expanded these protections to all public school students by establishing that students facing suspension must be given some kind of notice or hearing [11]. In the majority opinion, Justice Byron White wrote that “Due process requires, in connection with a suspension… that the student be given … an explanation of the evidence the authorities have and an opportunity to present his version” [12]. In other words, even relatively short suspensions implicate a property interest in education, which means, according to Justice White, that students must be told both the charges and the evidence against them, and be allowed to respond. The student’s inability to challenge the evidence means that any disciplinary actions or suspensions based primarily on AI detectors’ percentage scores seriously call into question due process concerns.
Critics of my defense of student rights under the Fourteenth Amendment may argue that Goss only requires minimal process, and that the invention of generative AI does warrant heightened scrutiny of student work and more flexible evidentiary standards in academic discipline. While this point is true, it is insufficient to justify disciplining students based mostly on opaque and unreliable algorithmic outputs. In Goss, the Court explicitly rejected formal evidentiary procedures and only required schools considering suspensions of less than 10 days to provide some kind of notice or hearing [13]. I am not arguing for schools to have robust evidentiary procedures similar to those found in courts of law. Instead, I am making the case that the hearing requirement set forth in Goss must still be meaningful. If the methodology and error rates of AI tools are unknown, then the students’ abilities to defend their case is eroded and the hearing loses its value.
Other critics may say that AI detection tools are the same as plagiarism detection software, which schools have used for years to justify suspensions and disciplinary action [14]. Many parallels exist between AI detection tools and plagiarism detection tools regarding how educators use them to justify student disciplinary actions. Yet, I am not arguing that solely relying on plagiarism tools violates due process the same way that primarily relying on AI tools does. Plagiarism tools reveal specific source overlap, so students can see and respond to the matching text. AI detectors often provide only probabilistic authorship claims. Since students cannot respond to accusations of academic dishonesty when AI detection tools are used as evidence with the same certainty as they would if plagiarism detection tools were used instead, the meaningfulness of any type of hearing where evidence is presented would also be diminished.
As any new technology introduced to the academic world, artificial intelligence is transforming classrooms [15]. Schools must balance the desire to protect academic integrity with their obligation to respect students’ constitutional rights. Students retain their Fourteenth Amendment protections while at school, including the right to due process before being disciplined. Legally, this means that disciplinary action for alleged AI usage cannot be primarily based on reports from AI detection tools and students must have the ability to properly respond to accusations. Failure to comply with these parameters could render disciplinary actions legally invalid under the Constitution. Thus, policymakers and educators must have serious conversations about how to best protect students’ due process rights when they face disciplinary action for alleged artificial intelligence usage. Due process is supposed to prevent punishing uncertainty, yet any punishment made based solely on reports from AI detection tools does exactly that. As established in Goss v. Lopez and Dixon v. Alabama, students must be given a meaningful opportunity to contest the evidence against them, something these opaque systems often fail to provide. If schools wish to preserve both academic integrity and constitutional fairness, they must ensure that emerging technologies do not replace the fundamental due process protections that students are guaranteed under the Fourteenth Amendment.
Bibliography
[1] Benjamin Ryan, “Students Accused of Using A.I. Say the Proof Is Flawed,” The New York Times, May 17, 2025, https://www.nytimes.com/2025/05/17/style/ai-chatgpt-turnitin-students-cheating.html.
[2] Lee V. Gaines, “Teachers Are Using Software to See if Students Used AI. What Happens When It’s Wrong?” NPR, December 16, 2025, https://www.npr.org/2025/12/16/nx-s1-5492397/ai-schools-teachers-students.
[3] Ido Hefetz, “Integrating AI Systems in Criminal Justice: The Forensic Expert as a Corridor Between Algorithms and Courtroom Evidence,” Forensic Sciences 5, no. 4 (2025): 53, https://doi.org/10.3390/forensicsci5040053.
[4] Natasha Singer, “Online Cheating Charges Upend Dartmouth Medical School,” New York Times, May 9, 2021, https://www.nytimes.com/2021/05/09/technology/dartmouth-geisel-medical-cheating.html.
[5] Fisher Phillips LLP, “Court Backs School in AI Cheating Case: 5 Things Your School Can Do to Avoid Trouble,” Insights, December 9, 2024, https://www.fisherphillips.com/en/insights/insights/court-backs-school-in-ai-cheating-case-5-things-your-school-can-do-to-avoid-trouble.
[6] Geoffrey A. Fowler, “Detecting AI May Be Impossible. That’s a Big Problem for Teachers,” Washington Post, June 2, 2023, https://www.washingtonpost.com/technology/2023/06/02/turnitin-ai-cheating-detector-accuracy/.
[7] Goss v. Lopez, 419 U.S. 565, 579 (1975).
[8] Liam Dugan et al., “RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors,” arXiv, May 13, 2024, https://doi.org/10.48550/arxiv.2405.07940.
[9] Wesley H. Y. Liang et al., “GPT Detectors Are Biased Against Non-Native English Writers,” arXiv, June 27, 2023, https://arxiv.org/abs/2306.15666.
[10] Dixon v. Alabama State Board of Education, 294 F.2d 150, 158–59 (5th Cir. 1961).
[11] Goss v. Lopez, 565.
[12] Goss v. Lopez, 579.
[13] Goss v. Lopez, 581.
[14] Karl O. Jones and T. A. Moore, “Practical Issues for Academics Using the Turnitin Plagiarism Detection Software,” Proceedings of the 9th International Conference on Computer Systems and Technologies (CompSysTech 2008), Sofia, Bulgaria, June 2008, 1–6, https://doi.org/10.1145/1500879.1500935.
[15] Natasha Singer, “Will Chatbots Teach Your Children?” New York Times, January 11, 2024, https://www.nytimes.com/2024/01/11/technology/ai-chatbots-khan-education-tutoring.html.