This is kind of where this started. So we were able to test this over the course of years when we deployed a particular set for a course and it all started with this question. Why are we assessing the students? What are we trying to prove?
And the concept of knowledge, what is knowledge was asked. What exactly is it knowledge? And how do we know that the student has gained knowledge? And this was a very expansive topic. So to bring it back to making it easy and malleable, we asked ourselves what are we hoping to tell ourselves when we look at a students submitted work. What are we hoping to capture?

And in this particular search for case study that we did it in, it was the proof that the student could work without supervision in their future role. That’s what I wanted to capture in the assessment.
So that tone sat in the underlying of every assessment, behind every question, behind every task. It was the capture of proof that the student could work without supervision.
And that’s what colored the assessments from then on. That was the foundation of it. So establishing that at the very beginning, before you start building assessments, ask yourself, what are you trying to prove?
Is it something very specific that you want to see the student is capable of understanding? If so, then make that obvious. Make it very clear. Then that’s what the assessment’s about. Because too often students don’t understand why they’re being assessed. They know that they have to be assessed. They just don’t understand why they’re being asked a certain question.
So their first instinct is just to run away and find the answer. But if they understand why they’re being assessed, it pulls it away from that answer searching mode into a more okay, what can I say to satisfy this question? So this is the first step in pulling it away from AI is to make it less of an AI focused task and something that the student wants to talk about or answer.