16/02/2026
If you are a teacher or someone who works with teachers, you know that assessment is the topic that keeps coming up in every conversation about AI in education.
The discourse tends to focus on students cheating and academic integrity. And those are valid concerns.
But students are only part of the equation, maybe even a small part. The biggest piece is assessment design and assessment strategies.
The problem, as I argued in a previous guide, is really one of assessment literacy.
The old techniques, the standard essays, the recall-heavy exams, the formulaic problem sets, they just don't hold up anymore when students have access to tools that can produce competent work in seconds.
We need to rethink how we assess learning.
And yes, that requires creativity, experimentation, and a willingness to try new approaches.
I know that can push some teachers out of their comfort zone. But unless we do the hard work of redesigning our assessments, we won't be able to evaluate genuine learning.
We'll only be measuring a student's ability to prompt an AI.
So I put together this guide. I compiled insights from researchers, fellow teachers, and assessment specialists along with practical strategies and tips to help you create assessments that are harder for AI to shortcut.
And no, there is no such thing as an AI-proof assessment.
AI can now handle just about any traditional assignment you throw at it. But that doesn't mean we're powerless.
In this guide, I share frameworks, research findings, and specific strategies that can help you design assessments focused on deeper thinking and understanding.
Link in the first comment
