Honor code violations are – and should be – serious matters. On occasion, some AI detectors have misidentified student work as AI-created, thus making accountability for plagiarism or cheating problematic. As educators adjust to AI and students acquire enough knowledge to use, manipulate, and exploit it, we must ensure that accusations impacting students’ academic records are accurate. But, how can we be sure if the AI isn’t?
AI Detection Versus Plagiarism Checkers
When AI detection and plagiarism checkers are discussed with students and teachers, there is universal confusion about both. Some college-level adult learners report confusion about the differences between AI detection and plagiarism, at times equating AI use with plagiarism or viewing detectors and checkers as intrusive or misaligned with academic policies. Equally muddled, high school students, with limited understanding and education on the seriousness of each topic, simplify their confusion on AI detection versus plagiarism by conflating the two concepts entirely. Clarity and definition should be daily conversations to encourage academic and ethical distinctions.
So, let’s be clear on some shared definition; after all, vocabulary matters-particularly in education:
- AI Detectors are the AI tools that analyze the probability of whether or not text is AI-generated from ideas, concepts, conventions, and/or writing structures that appear to have AI patterns. Detectors are not reliable (yet) because the training for the AI models is still developing. So, in reality, patterns are the driving force behind detectors rather than direct comparison techniques with the actual text submitted by a student.
- Plagiarism Checkers are direct comparison tools that track and analyze published text, paraphrased content, and cited material. Plagiarism checkers have been reliably used for many years and can provide a percentage ranking of real-world duplicated material or specify identical text combinations. Thus, a checker reviews text that already exists in the published space and compares it against new material submitted by a student.
Suspect AI Cheating - Now What?
Understanding the difference between detectors and checkers is only the first step in addressing accountability measures for violating either plagiarism rules or unauthorized AI use; the real issue is the ethical implications behind the infractions. Unauthorized AI use raises significant ethical concerns, particularly when it substitutes for independent work. However, what counts as ‘unauthorized’ varies across institutions. To prevent confusion, educators must clearly define expectations and articulate what independent work means in the context of AI support.
Now, don’t get me wrong, I don’t intend to exonerate a student who plagiarizes or minimize the seriousness of the offense. Rather, it’s essential to understand that plagiarism is solely academic, which can be unintentionally committed by inexperienced writers or rise to an ethical breach of cheating if committed intentionally (Smith, 2025). Unauthorized AI use, however, when defined as prohibited, is deceitful and thus unethical. After all, one does not accidentally open an AI chat and inadvertently gain a precisely written answer to an inquiry, and then paste the copied answer into a homework assignment. Of course, one could argue intentionality isn’t met if a student was unaware that AI use was unauthorized; however, this becomes a spurious and circular argument when students are requested to complete independent assignments. Should we define “independent” too? Perhaps we should, and when we do so we must also clearly state that “unauthorized use” is a cheating boundary on any AI use, and differs from independent work, which may include a spectrum of limited allowance.
Further, the challenge isn’t whether or not we understand that cheating has occurred when a student uses unauthorized AI to falsely complete an assignment, but rather the problem lies with how we handle the transgression. Accusations of unauthorized AI use are problematic due to the unreliable outcome of the detectors and the lack of definitions surrounding “unauthorized use”. When discussing the possibility that students may have used AI to construct a portion – or possibly an entire paper – educators must rely heavily on the assertions that the material was AI-created when the AI may have constructed a false-positive. Be careful! If a student completely denies AI use, then the evidence to counter the denial may be absent. In such cases, and until AI detectors become more reliable, educators should rely on the timeless tradition of teaching intuition and experience. Experienced educators have shown remarkable intuition for identifying AI-created material that appears out of alignment with typical student writing conventions, grammar, and syntax, reflecting a shift from educator resistance regarding AI and towards an acceptance on how to teach through it among varied age groups in the instructional space (Fredrick et al., 2024; Coffey, 2024; Tripathi et al., 2025).
Cheating or Plagiarism: Actions that Educators Trust
A student’s work that benefited from unauthorized AI use will appear to an experienced educator to be different in outline, tone, word choice, and sophistication, for which many options can remedy:
- A discussion with a student that addresses stark differences and obvious concerns may yield a voluntary response and a willingness of the student to resubmit the assignment in question. Take this as a win! If a student denies the writing irregularities or unauthorized AI use, then cautiously move on, knowing that the student is now aware that you are watching their writing conventions more closely in future assignments. This too is a win!
- If plagiarism is suspected, address the infraction as a purely academic matter by using side-by-side comparisons to offer meaningful discussions with students regarding accountability and violations. Requiring resubmission, providing a penalty for the offense, such as grade deduction, or referring the matter to higher remediation are acceptable outcomes for a confident educator to take upon the notification that a plagiarism checker has identified infractions.
- Suspected plagiarism that is the result of intentionally copying another person’s work, is actually cheating, which is unethical behavior, and thus should be remediated according to a school’s integrity procedures.
Final Thoughts
It’s essential that distinctions, definitions, and ethical implications are discussed regularly with students to ensure they understand their roles in plagiarism and cheating and how each may be verified by AI detectors and plagiarism checkers. Further, we would be remiss if we didn’t acknowledge that students struggle with what level of AI support may be authorized, such as to help formulate questions, create research ideas, or narrow topics, which educators must clearly outline in advance of an assignment to avoid the eventual denial of understanding. Plagiarism and cheating are not new concepts, but how we manage them in the age of AI detection is. Thus, we must be vigilant on many fronts to ensure academic accountability and a thorough understanding of AI detectors versus plagiarism checkers is encouraged.
References
Coffey, L. (2024, February 9). Professors cautious of tools to detect AI-generated writing. https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2024/02/09/professors-proceed-caution-using-ai
Fredrick, D. R., Craven, L., Brodtkorb, T., & Eleftheriou, M. (2024). The role of faculty expertise and intuition in distinguishing between AI-generated text and student writing. ESBB, 10(2). https://www.englishscholarsbeyondborders.org/wp-content/uploads/2024/09/Fredrick-et-al-1.pdf
Smith, T. (2025, June 22). Ethical artificial intelligence: Proactive districts, better teaching and stronger students. https://edu-ai.org/ethical-artificial-intelligence-proactive-districts-better-teaching-and-stronger-students/
Tripathi, T., Sharma, S. R., Singh, V., Bhargava, P., & Raj, C. (2025). Teaching and learning with AI: A qualitative study on K-12 teachers’ use and engagement with artificial intelligence. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1651217
Terri Smith has more than twenty years of experience in the education sector. Currently, she is a technology faculty member at a college preparatory school, where she designs the curriculum for Graphic Design and Artificial Intelligence. Additionally, she is a university instructor leading graduate-level courses in educational technology and research methodology. Terri’s extensive education includes a master’s degree in teaching, an MBA in IT management, and a doctorate in education, for which she was awarded an outstanding graduate and distinguished commencement speaker. She holds multiple teaching and administrator licenses and has varied experiences spanning numerous states and countries, including Germany, Guam, and Russia. Terri is presently conducting authentic research on technology use within non-technology disciplines, creativity expansion through technology use, and artificial intelligence for the non-technical consumer.
