AI is the Best Offer—So Why Would Students Choose Anything Else?
To students, AI isn’t innovative. It isn’t controversial. It’s just… there. Like the Internet. Like Google. Like spellcheck. Like the calculator you were told you wouldn’t always have with you back in the day (and hello, calculators everywhere).
So when we design courses as if AI is optional, students experience us as optional. And we’re up against some doozies if we expect them to care about AI not being optional.
The Best Offer in the Room
From a student’s perspective, AI offers an incredible deal:
- instant answers
- polished writing
- less effort
- faster completion
What feels like a huge reward for minimal effort.
And we’re asking students to choose the opposite.
More effort. More time. More struggle. Because we know they learn in failure and in the process.
But there’s a tension we don’t always acknowledge.
Most traditional-age college students are still developing the very skills we have claimed to assess all along (and what AI has shown us that we probably weren’t). Planning. Judgment. Delayed gratification. Metacognition. The work of a prefrontal cortex that, for many, is still under construction well into their mid to late twenties.
When faced with a constant cost-benefit decision, psychology tells us that they are going to choose what feels like the better deal. The dopamine hit. The quickest route.
So, of course they are going to use it.
This Is Not a Character Problem
“How do we stop them?” the people shout! “Why would they choose anything else?” the logics reply.
We know AI can help them finish the work. They know it too. But many of them also know it’s not actually helping them learn. And still, they choose it.
That’s not a character issue. That’s learning design.
If we can’t answer why engaging deeply is worth it, in the moment, inside the assignment, we’re not competing with AI. There’s no competition. But we are losing.
The Challenge of Online and Asynchronous Courses
And if you are thinking, “Sure, but what about asynchronous online courses?” They’re not doomed. But they are the clearest version of this problem.
No room. No eye contact. No social pressure. Just a student, a deadline, and the biggest, best offer sitting one tab away. Which means everything above matters even more.
So let’s make this practical.
Four Shifts for an AI-Normal Classroom
If we want students to care in a classroom where AI use is normal and not exceptional, then there are four key shifts we must make.
Make Thinking Visible and Necessary
If a student can complete an assignment without making a single meaningful decision, AI will do it for them. Design assignments where choosing, justifying, revising, and explaining are not add-ons, but the work itself.
- In face-to-face courses, this might look like in-class decision points, think-alouds, or live revision.
- In asynchronous courses, this means capturing thinking: short video or audio reflections, annotated drafts, or step-by-step submissions instead of a single final upload.
If thinking is invisible, it is optional.
Build Stakes That Are Human, Not Just Graded
Students will outsource low-stakes, impersonal work every time. They will show up for work that feels seen.
This means designing assignments where their presence matters:
- Peer feedback that actually influences outcomes
- In-class application where their voice is required
- Work connected to something beyond submission
In online courses, this cannot be “reply to two classmates.” We must design for intentional connection: small groups, ongoing roles, or work that depends on someone else showing up.
Normalize AI, Then Raise the Bar
When AI is treated as forbidden, students hide it and disengage. When AI is treated as baseline, instructors can design above it.
Ask students to critique AI output. Improve it. Disagree with it. Reflect on what they changed and why. Make “What did you do with it?” more important than “Did you use it?”
This is especially critical online, where AI use is already happening quietly. Bringing it into the assignment prevents it from replacing thinking.
Design for the Moment of Temptation
Learning design often focuses on policy—after the fact. But temptation happens inside the assignment.
Where is the shortcut most appealing? Where is effort easiest to avoid? That is where design matters most.
Break work into stages. Ask for early decisions. Add just enough friction that thinking has to happen before completion—especially in asynchronous courses, where temptation often shows up late at night, under pressure, with multiple deadlines looming.
Making Engagement the Better Deal
Students aren’t making one big ethical decision, but dozens of small, fast ones under pressure… assuming it’s “no big deal.” And AI is always offering the better deal.
So we have to make engagement the better deal. Not by making it easier. But by making it matter.
AI is not just changing how students complete work. It is changing what feels worth doing. If we don’t design for that reality, no policy, no detector, no syllabus statement is going to fix it.
This isn’t about catching students using AI. It’s about designing environments where using AI alone is not enough. Where thinking still matters. Where contribution still matters. Where being in the room still matters… even when there is no actual room.
Let’s move forward. Not AI-resistant, but AI-aware and human-centered.
Meet the Author
Dr. Ashley L. Dockens, Ph.D., Au.D., CCC-A, serves at Lamar University as Associate Provost of Academic Innovation and Digital Learning and Associate Dean of Policy and Procedure, College of Graduate Studies. As an Associate Professor of Audiology, she leads university AI initiatives and speaks on generative AI. Dr. Dockens chairs the AI Taskforce for the Council of Academic Programs in Communication Sciences and Disorders, contributing to higher education through diverse leadership roles.
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