From trading answers for minor assignments to leaking of exam answers, cheating has almost become a necessity in competitive schools due to the students’ heavy course load. The recent development of Large Language Models (LLM) and Artificial Intelligence (AI) has allowed a more subtle way of academic dishonesty; a student can claim a non-person’s work as their own and bypass checkers with edits. This state of widespread cheating, through LLM and other means, has blurred the boundaries between original and academically dishonest work, and has devalued the “A” in school.
Though general cheating is identified across all courses, AI use is most pervasive in writing-heavy classes where one can complete assignments at home. For example, AP Seminar had a cheating scandal where a large portion of the student body was identified for AI use. English classes, on the other hand, have shifted to handwriting drafts before typing the final product, so they have seen lower than expected AI use. Plenty of works generated by AI are identified and given proper consequences through detectors, checking edit history in Google Docs, or teacher observation, though students often bypass it through rewording of the sentences and typing them out.
General cheating in of itself remains an issue. Cheating in competitive schools is often justified by students as “leveling the playing field.” This justification operates under the assumption that every other high-achieving student cheats, and pressures students to maintain high grades at any cost, neglecting the primary purpose of education: To learn, explore concepts, and develop learning skills. All such gains are secondary to the race for the transcript. Ironically, while students are supposedly “helping themselves” by cheating — outside the platitude that they are undermining their own intellectual growth — they devalue the grade that they seek for themselves and their peers.
The rise of AI made this even more complicated. Unlike general cheating, which often manifests in the form of copying a friend’s homework or memorizing answers through oral delivery, AI-generation sets in a gray area where the work is technically “new,” in the sense that it has never existed before, and yet it is not the student’s thought process. This forces consideration of how learning is conceived: should originality be measured by the source from which the contents came, or by the ideas behind them? AI blurs this boundary, making it easy to use AI without self-consciousness, encouraging more brazen and sophisticated forms of academic dishonesty.
Ultimately, academic dishonesty reflects an issue in competitive education where people weigh outcomes more than understanding. Standardization is effective, but also encourages students to “game the system,” leading to cheating’s persistence through incentivization. Combating it requires both student integrity and conceptual clarity of cheating and its contexts. One may find cheating to be less appealing if they consider whether “A” still stands for achievement, or just for whatever loophole a student can exploit.
