AI Detectors: The Uses and the Risks in 2025

They are Right...Until They are Wrong!

The increase in the availability of consumer-grade artificial intelligence (AI) and its use in writing has led to widespread concerns about academic integrity and authorship authenticity in recent years. Where there is an AI writing assistant, it is only natural to see an AI detection tool get developed to distinguish between human-written and AI-generated text. When they work the AI detectors generate a score of how much of a writing sample was generated by AI, but when they fail they can quite literally ruin someone’s reputation.

Many of these tools are far from foolproof, often producing false positives that wrongly classify human-authored content as AI-generated. In addition, non-native English speakers, individuals with disabilities, and minority groups face a higher likelihood of being incorrectly flagged due to unique linguistic characteristics. If writing is not their strong point, should they fail because a grammar corrector in a program or a website gave them an option to rewrite it?

This article examines how AI detectors work, explores documented cases where they have been misapplied, and outlines strategies to avoid wrongful accusations while considering the legal, institutional, and technological developments shaping the landscape.

How AI Detectors Work

AI detection tools use machine learning (ML) and natural language processing (NLP) to analyze text and estimate the probability that it was generated by AI. These tools rely on linguistic patterns, statistical analysis, and probabilistic modeling to make their assessments (Guo, Liu, & Kim, 2023).

1. Machine Learning-Based Detection

AI detectors are trained using large datasets of both human-written and AI-generated text. Through supervised learning, these models identify statistical differences between the two forms of writing. Key indicators include:

• Predictability: AI-generated text follows probabilistic language models, making it more structured and less varied than human writing (Zhang, Huang, & Davis, 2022). AI-generated text typically lacks the spontaneous shifts in tone, sentence complexity, and contextual nuance found in human writing, leading to greater uniformity in structure.

• Grammar and Syntax: AI tools often overcorrect, leading to mechanically precise grammar that lacks the natural variations present in human writing. It should be noted that word-processing programs and web applications that provide grammar-correcting capabilities can suffer from this same fault. Humans frequently make stylistic deviations and utilize regional idioms or slang, elements that AI detection tools may flag as anomalies.

• Word Choice and Repetition: AI-generated text can exhibit overuse of certain phrases, as models optimize for coherence rather than originality. AI-written content tends to prioritize clarity over personality, resulting in text that may appear polished yet lacks individual stylistic nuances.

2. Natural Language Processing and Quantitative Metrics

Many AI detection systems rely on specific quantitative techniques, such as:

• Perplexity Analysis: This measures the predictability that a word or a sentence was written by an AI. A lower perplexity scores suggest AI authorship, as AI-generated text tends to use common statistical structures, and a higher perplexity score indicates that the word or sentence combination is unique (Ji & Shen, 2023). AI language models are trained to predict the most statistically likely next word, making their output more formulaic than human writing, which often incorporates unpredictability and creativity.

• Burstiness Evaluation: Human writing tends to include a mix of short and long sentences, while AI-generated content often maintains uniform sentence lengths. For example, an AI writer might always use a paragraph with three sentences, and every sentence contains exactly 15 words. The variation in sentence structures and paragraph rhythm helps distinguish human-authored text from AI-generated material.

• Repetitive Pattern Detection: AI-generated text sometimes reuses phrases in an effort to maintain coherence. How many times have we seen phrases like “The Rise of the AI Writer: Unlocking Your Potential”, or words like “delve, furthermore, orchestrate, or tapestry.” Unlike human writers, who instinctively avoid redundancy for readability and engagement, AI models often generate repeated structures because of their training process.

The Risks of False Accusations

Legal and Ethical Implications

Despite their growing popularity, AI detection tools have been criticized for their unreliability. Several students and professionals have faced wrongful accusations due to inaccurate AI detection results. These accusations can damage reputations, and cost students potential scholarships from higher education institutions. Legal experts warn that institutions relying on these tools without proper review could face lawsuits and due process violations.

A notable legal case involved a Massachusetts high school student who sued Hingham High School after being falsely accused of AI-assisted plagiarism. The lawsuit cited a lack of transparent AI policies, arguing that the student's due process rights were violated (Associated Press, 2024). In higher education, legal scholars highlight that institutions must provide an appeal process when students are accused of AI misuse to prevent unjust disciplinary actions (Brown, Zhang, & Patel, 2024).

Case Studies of False Accusations

• Western Washington University: A student, Annika Nelson, was twice falsely accused of AI-generated submissions. Her “clinical” tone, associated with autism spectrum disorder, was flagged as AI-generated. After proving authorship, her grade was restored (The Guardian, 2024).

• Massachusetts High School Lawsuit: A student and his parents sued Hingham High School after he was penalized for suspected AI usage in a history project. The student received a lower grade and detention, barring him from the National Honor Society. The lawsuit argues that the school lacked clear AI policies, violating the student’s due process rights (The New Yorker, 2024).

• Iowa State University: A professor at Iowa State University accused an entire class of AI-assisted plagiarism, prompting the university to later clarify that the detection tool was unreliable and that students should not be penalized solely based on automated AI detection (Inside Higher Ed, 2024).

• Vanderbilt University: Due to high false-positive rates, Vanderbilt disabled Turnitin’s AI detection tool after students using Grammarly and other writing aids were wrongly accused of AI authorship (Vanderbilt University, 2024).

• Racial Disparities: A study found that Black students were disproportionately accused of AI-generated writing, with 20% reporting false accusations compared to 7% of white students (Education Week, 2024).

• Non-English Speakers: A Stanford University report indicated that 61% of non-native English-speaking students had their work flagged as written by AI (Stanford HAI 2024).

Strategies to Mitigate False Accusations

• Teach Responsible AI Use: Teachers, professors, and students all need to be taught how to use AI responsibly. AI has not been widely incorporated into the public sector as of 2025. Until it is widely available and used by faculty and students, it may not be fair to any party to use professional tools to penalize writers. There seems to be this expectation at the moment that students know how to use AI to write, and that faculty know how to recognize AI writing without using AI themselves! Using AI to detect AI and punish untrained students seems like the wrong approach to me.

• Diversify Writing Structure and Style: AI-generated text is often highly structured and predictable. Writers can reduce the likelihood of being flagged by incorporating a mix of sentence lengths, using complex sentence structures, and varying their vocabulary. Additionally, personal anecdotes, case studies, and discipline-specific terminology can help differentiate human writing from AI-generated content.

• Document the Writing Process: Keeping detailed records of the writing process can serve as evidence of human authorship. This includes maintaining handwritten notes, multiple drafts, and using Google Docs’ version history or Microsoft Word’s track changes to show how a piece evolved over time. There is a case pending right now in Michigan where some of these tracking and changing factors are being considered to determine guilt or innocence.

• Use AI Detection Tools for Self-Assessment: Before submitting assignments or reports, individuals can use AI detection tools to check whether their writing is at risk of being flagged. If a document is mistakenly classified as AI-generated, making strategic adjustments to the flagged sections can help reduce false positives. Forbes usually maintains an annual list of the top AI detectors that can be used to evaluate written work.

• Leverage AI as a Learning Tool, Not a Generator: AI-powered writing assistants like Grammarly and Hemingway Editor can be used to improve clarity, grammar, and style without fully generating content. Using these tools responsibly ensures that the final output remains authentically human-written. Free ChatGPT, Claude, Gemini, Grok, and Qwen can be used as writing coaches, but simply copying and pasting their suggestions could result in a positive detection result.

• Advocate for Due Process in AI-Generated Accusations: Institutions should implement clear policies that require human review before penalizing individuals for suspected AI use. There should not be any situation in academia where a student is simply failed and removed from a class just because of an AI detector result. Students and professionals should push for transparency in how AI detection results are used in decision-making, and there should be a review panel process available to review and address AI writing accusations.

• Develop Institutional Guidelines on AI Use: Schools, universities, and workplaces should establish clear policies on AI-assisted writing, including ethical use guidelines. This helps prevent misunderstandings about what constitutes acceptable AI assistance. If a student used AI to assist with proofreading and an AI detector shows it was 50% AI written does that mean the student fails? What standards are being used to audit the quality of the AI detector being used in the institution? What constitutes a passing or failing percentage?

Final Thoughts

While AI detection tools aim to safeguard academic integrity, their limitations raise concerns about accuracy and fairness. Documented cases highlight their tendency to misclassify human writing, disproportionately affecting non-native English speakers, individuals with disabilities, and minority groups. Until more AI training can be provided to students and faculty on how to use AI responsibly, I don’t think AI detectors are worth ruining lives or reputations.

Also, not all AI detectors are made equally. There needs to be some algorithm audit standards for these tools if they are going to be used by public institutions, and someone needs to be held accountable for recording and resolving positive AI detections. That is probably another article for another day, but I suspect that few of these educational institutions are tracking these things in a coordinated and comprehensive manner.

References

• Associated Press. (2024). Massachusetts student sues over false AI cheating accusation. Retrieved from https://apnews.com/article/8f1283b517b2ed95c2bac63f9c5cb0b9

• Brown, L., Zhang, W., & Patel, R. (2024). Understanding the Limitations of AI Content Detection. Journal of Artificial Intelligence Research, 37(2), 112-129. Retrieved from https://www.jsr.org/hs/index.php/path/article/view/5064

• Center for Democracy & Technology. (2024). Bias in AI detection tools and their impact on marginalized communities. Retrieved from https://cdt.org/ai-bias-detection

• Education Week. (2024). Black students are more likely to be falsely accused of using AI to cheat. Retrieved from https://www.edweek.org/technology/black-students-are-more-likely-to-be-falsely-accused-of-using-ai-to-cheat/2024/09

• Guo, T., Liu, H., & Kim, S. (2023). Machine Learning Approaches to AI Text Detection: Strengths and Weaknesses. Computational Linguistics Review, 45(3), 89-104. Retrieved from https://openreview.net/pdf?id=pNAhvp3naMd

• Inside Higher Ed. (2024). Professor falsely accuses entire class of AI plagiarism at Iowa State University. Retrieved from https://www.insidehighered.com/news/2024/05/30/professor-accuses-students-ai-use-without-evidence

• Ji, Y., & Shen, L. (2023). Perplexity and Burstiness as Indicators of AI-Generated Text. AI and Language Studies, 12(4), 75-91. Retrieved from https://www.unic.ac.cy/telblog/2023/04/11/perplexity-and-burstiness-in-ai-and-human-writing-two-important-concepts/

• Stanford HAI. (2024). AI detectors are biased against non-native English writers. Retrieved from https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers

• The Front. (2024). WWU student falsely accused of AI use. Retrieved from https://www.thefrontonline.com/article/2024/12/wwu-ai

• The Guardian. (2024). Students with disabilities disproportionately accused of AI use. Retrieved from https://www.theguardian.com/technology/2024/dec/15/ai-cheating-controversy

• The New Yorker. (2024). The legal fight over AI plagiarism accusations. Retrieved from https://www.newyorker.com/news/2024/06/ai-plagiarism-legal-battle

• Vanderbilt University. (2024). Guidance on AI detection and why we’re disabling Turnitin’s AI detector. Retrieved from https://www.vanderbilt.edu/brightspace/2023/08/16/guidance-on-ai-detection-and-why-were-disabling-turnitins-ai-detector/

• Wikipedia. (2024). Turnitin AI Detection Controversy. Retrieved from https://en.wikipedia.org/wiki/Turnitin

• Zhang, K., Huang, P., & Davis, M. (2022). Comparative Analysis of AI Writing Models and Detection Techniques. Journal of Computational Linguistics, 28(3), 144-163. Retrieved from https://link.springer.com/article/10.1007/s10849-023-09409-x