Beyond Turnitin: Proactive Strategies to Curb AI Misuse in the Classroom

The Limits of Detection Tools

AI is changing how students approach assignments—and how educators respond. Tools like Turnitin now promise to detect AI-generated content. But while detection might catch misuse, it doesn’t prevent it. And it certainly doesn’t foster ethical, original thinking.

What higher education needs is a shift from policing to pedagogy—from flagging AI misuse to designing coursework that resists it from the start. That’s exactly what faculty are doing with Hypothesis.

1. Understanding the Scope of AI Misuse

Students don’t turn to AI because they’re lazy. More often, it’s confusion, time pressure, or fear of failure. Some students genuinely believe it’s harmless to use ChatGPT to summarize a reading or draft a paragraph.

Detection tools don’t address the why behind AI use. They offer a judgment, not a teaching moment. And they often do so unreliably—flagging human writing as AI-generated or missing AI text entirely.

Instead, faculty need to create conditions where genuine learning thrives—where students engage with the material because the assignment demands it.

“We’re showing students how to read closely, ask better questions, and contribute meaningfully—even in an AI-saturated world.”
— Diana Fordham, Missouri Southern State University
(Source: AI Isn’t the Enemy Case Study)

2. Designing AI-Resistant Assignments

Social annotation with Hypothesis offers an assignment structure that’s resilient to AI misuse. Students must engage with specific lines of text—annotating directly in the margins, sharing reactions, and responding to peers. There’s no easy shortcut for this kind of work.

“It’s harder to AI your way through an annotation assignment.”
— Merilee Madera, West Liberty University

Because every annotation is tied to a specific sentence or paragraph, generic or pre-written responses are easy to spot. The work has to be contextual, original, and specific—all qualities that AI struggles to mimic convincingly.

Want a real example of how to structure this?
Download: Sample AI-Comparison Assignment Using Hypothesis

3. Embedding Critical Thinking into the Assignment Process

Rather than banning AI tools, educators are increasingly embedding them into the learning process—turning potential crutches into critical thinking exercises.

At SUNY New Paltz, Rachel Rigolino assigns students to create SWOT analyses using ChatGPT, then annotate those outputs in Hypothesis. Students critique what AI got right—and where it missed the mark.

“This approach helps students see AI as a tool, not a crutch.”
— Rachel Rigolino, SUNY New Paltz
(Source: AI Isn’t the Enemy Case Study)

At MSSU, Diana Fordham uses a similar approach in her graduate education courses. Students evaluate AI outputs for bias and oversimplification, using annotations to surface those findings collaboratively.

Want to hear faculty talk about this live?
Watch: Liquid Margins 45 – AI and the Future of Learning

4. Replacing Passive Tools with Active Learning

Traditional essays and discussion boards are easily spoofed by AI. Social annotation isn’t.

With Hypothesis, students can’t simply generate a paragraph and paste it in. They must respond to a specific sentence. They must interact with a peer’s comment. They must show their work—in public.

These practices make it harder to rely on AI-generated content—and easier for instructors to see student thought in action.

Better yet, Hypothesis integrates with most LMS platforms. That means faculty can assign, grade, and comment within one environment—making feedback faster and the learning loop more efficient.

Conclusion: Shifting from Policing to Pedagogy

Turnitin might flag AI content. But it won’t foster ethical decision-making. It won’t create deep readers. It won’t guide students toward engagement.

That’s what pedagogically sound tools like Hypothesis are built for. They make thinking visible. They reward effort. And they give students space to wrestle with the very technologies shaping their future.

With AI here to stay, the real solution isn’t stricter detection. It’s a smarter instruction.