The academic world is currently facing an existential threat, and its name is Generative AI.
For decades, we relied on a social contract: we assign work, and students do it to demonstrate their learning. But tools like ChatGPT and GitHub Copilot have shattered that contract. Traditional plagiarism detection tools—like Turnitin—are no longer sufficient. They check for matching text, but AI generates new text. If a prompt is generic, an AI can answer it perfectly, often better than the average student.
The creative storytelling is an ‘unfair advantage’ that is difficult for students to plagiarize using standard AI tools.
This has created an Academic Integrity Crisis. Instructors are terrified. How do we assess genuine understanding when a machine can generate code, write essays, and solve proofs in seconds? We are seeing a retreat to pen-and-paper exams and surveillance software, neither of which supports a positive learning environment.
But there is a better way. The solution isn’t to ban AI; it’s to design curriculum that is “AI-resistant” by nature.
At Betty Ivy Reads, we use narrative-driven content as our “unfair advantage.” Here is the secret: AI models are trained on the internet. They know everything about generic coding problems. They can write a “Python script to calculate the Fibonacci sequence” in their sleep.
But they don’t know Betty Ivy. They don’t know the specific fuel consumption rates of a fictional mining ship on the moon of Barrata. They don’t know the unique socio-political context of the “Neon Punk” universe we have created.
AI-generated content makes it difficult for instructors to assess genuine student understanding and originality.
By weaving technical problems into a unique, bespoke story, we create assignments that are authentic and incredibly difficult to plagiarize. If a student asks ChatGPT to “solve the Betty Ivy fuel problem,” the AI will hallucinate or give a generic answer that doesn’t fit the specific constraints of our story. To solve the problem, the student must engage with the narrative. They must read the context. They must apply the code to the specific, fictional dataset we provide.
This approach transforms assessment. We aren’t just asking students to write code; we are asking them to solve a problem within a specific context. This requires critical thinking, interpretation, and creativity—skills that AI still struggles to replicate authentically.
We call this Plagiarism-Resistant Design. It’s not about policing students; it’s about creating work that is meaningful enough that they want to do it, and specific enough that they have to do it.
Furthermore, our open-source notebooks encourage students to document their thought process. We assess the journey, not just the output. By blending art, poetry, and code, we create a learning experience that is uniquely human.
The age of the generic assignment is over. If we want to save academic integrity, we have to get creative. We have to tell better stories.
