Mississippi professor reveals hidden way he used to expose AI cheating in viral video
A university professor in Mississippi has gone viral after explaining a hidden method he uses to catch students who submit AI-generated essays. The technique reportedly relies on an invisible or easily overlooked clue embedded within the assignment itself, which flags whether a student's work has been produced or heavily assisted by an AI tool such as ChatGPT. The video's popularity reflects wider anxiety among educators about the growing use of generative AI in academic work, and how institutions can verify genuine student effort.
The professor's approach adds to a broader debate in education about detecting AI use fairly and effectively, especially as some educators argue that too much focus has gone into catching cheats rather than improving how students learn with AI tools. The article notes this comes amid wider discussion around AI in education policy, including moves by the Trump administration to expand AI teaching in schools and Purdue University's introduction of AI-related graduation requirements.
- Mississippi professor's hidden AI-cheating detection method goes viral.
- Highlights ongoing debate over AI use in student assignments.
- Comes amid push for expanded AI education in US schools.
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Mississippi professor reveals hidden way he used to expose AI cheating in viral video
The story centres on a US university lecturer in Mississippi who has attracted widespread attention online after describing a method he uses to spot essays written with the help of artificial intelligence tools such as ChatGPT. He says he builds a subtle clue into assignments that reveals whether a student's answer came from an AI rather than their own work. This taps into a growing worry among teachers about how to tell genuine student writing from machine-generated text.
The video has struck a nerve because schools and universities everywhere are grappling with the same problem: AI chatbots can now produce convincing essays in seconds, making traditional plagiarism checks less reliable. Some educators argue the emphasis on catching cheats is misplaced, and that more effort should go into teaching students how to use AI responsibly as part of their learning.
Both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
Advocates of active AI-detection methods argue that academic integrity depends on verifying that a student's submitted work reflects their own understanding, particularly in courses where writing itself is the skill being assessed. They contend that clear, well-publicised deterrents protect honest students from being outcompeted by peers who outsource their thinking, and that educators have both a right and a duty to safeguard the credibility of the qualifications they award. Embedding subtle checks, in this view, is simply a pragmatic response to a genuine and rapidly growing problem, not an act of hostility towards students.
The case against
Critics of this detection-first approach argue that fixating on catching cheats treats a symptom rather than the underlying shift in how learning happens, and risks fostering a climate of suspicion that undermines trust between staff and students. They point out that hidden traps can be applied inconsistently, may unfairly ensnare students who used AI tools in permitted or ambiguous ways, and do little to prepare young people for a working world where AI collaboration is increasingly expected. In their view, institutions would better serve students by redesigning assessments and teaching AI literacy directly, rather than relying on covert gotchas.
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