The integration of AI into education has reignited long-standing debates about plagiarism and academic integrity. With generative AI capable of producing essays, solving equations, and drafting code at a moment’s notice, institutions are turning to AI detection tools as their primary defence. The idea is straightforward: if students use AI dishonestly, these tools will expose them. But the reality is far less reliable, and this approach is proving to be both ineffective and counterproductive.

Detection tools are marketed as a solution, yet their limitations are glaring. False positives—instances where authentic student work is flagged as AI-generated—are alarmingly common. Imagine a student putting significant effort into crafting an original essay, only to have their work questioned because it doesn’t align with an algorithm’s expectations. This erodes trust and discourages genuine effort, creating an atmosphere where students feel scrutinised rather than supported.

At the same time, detection tools are far from infallible when it comes to catching actual misuse. Students who want to bypass these systems will always find ways to do so, especially as generative AI evolves. The result is an arms race that institutions cannot win—while detection tools struggle to adapt, students remain several steps ahead. The core problem isn’t just the futility of detection; it’s the misplaced focus on surveillance rather than meaningful engagement with how learning is assessed.

Current assessment methods are part of the problem. Essays, long a staple of education, are also the format most vulnerable to misuse. Generative AI excels at producing formulaic, coherent essays, often indistinguishable from those written by students. Instead of doubling down on detecting this misuse, perhaps it’s time to rethink whether the essay, as we currently use it, remains fit for purpose in an AI-rich world.

Alternative approaches to assessment offer a more promising path. Oral exams, for example, require students to explain their understanding in real time, making it far more challenging to rely on pre-generated material. Project-based learning shifts the emphasis from the final product to the process, allowing educators to track progress and collaboration. In-class handwritten tasks reduce opportunities for external assistance, offering a straightforward way to ensure authenticity. These methods not only sidestep the limitations of detection tools but also enrich the learning experience by fostering deeper engagement and critical thinking.

The drive to “catch” students using AI also risks missing an opportunity to integrate these tools constructively. Generative AI isn’t just a potential shortcut for plagiarism; it’s also a powerful aid for brainstorming, refining ideas, and exploring new perspectives. Penalising its use without acknowledging its value stifles innovation and discourages students from experimenting with the very tools they’ll likely rely on in their professional lives.

Instead of focusing on enforcement, institutions should shift their attention to helping students use AI responsibly and transparently. A student who drafts an essay with AI assistance but can critically evaluate and improve it demonstrates a far deeper understanding than one who avoids these tools altogether out of fear. The goal should be to teach students how to work alongside AI ethically, using it to enhance their learning rather than undermine it.

The fixation on detection tools is a distraction from the real challenge: reimagining education for a world where AI is ubiquitous. This requires a fundamental rethink of how we evaluate learning, moving away from formats that are easily replicated by machines and toward methods that prioritise originality, adaptability, and genuine understanding. By letting go of the arms race against plagiarism, education can focus on what truly matters—equipping students with the skills and mindset to navigate a future where AI is not a threat but a tool.