---
title: A Smarter Way to Assess Learning in the AI Era
description: Discover a smarter approach to learning assessment in the AI era, focusing on intentional design, visible evidence, and enhancing student capability.
image: https://leecrockett.net/hubfs/Evidence-Rich-Design-Chain.jpg
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[Lee Crockett](https://leecrockett.net/podcast/author/lee-crockett) November 2, 2026
# A Smarter Way to Assess Learning in the AI Era
Key takeaways:
- Evidence of capability must be designed before the task is submitted.
- The chain moves from curriculum intent to capability demand, learning design, visible evidence, feedback and growth.
- Better evidence does not mean more evidence.
- Leaders can use the chain as a planning conversation, not a compliance template.
---
## A Smarter Way to Assess Learning in the AI Era
### Transcript
There is a point in the life of a task when the evidence has already been decided, even if no one has marked the work yet.
It happens much earlier than we sometimes think. It happens when the task is designed. It happens when the criteria are written. It happens when teachers decide what students will have to explain, compare, revise, defend or submit. By the time the work arrives, the evidence is already shaped by those earlier choices.
That is why one of the most important Visible Agency questions is not asked at the end of the task. It is asked at the beginning:
What evidence of student capability will this design make visible?
In this episode, I want to explore the Evidence-Rich Design Chain. It is the Week 6 movement in the Visible Agency campaign because it takes us from noticing the problem to designing the response.
The earlier weeks named the challenge. Completed work can no longer carry the whole story. Coverage is not capability. Schools can experience a Human Capability Blind Spot. Products matter, but they cannot carry the whole evidence burden. Last week, we named five traces of student thinking: questions, choices, judgements, revisions and ownership.
This week asks how those traces become part of learning by design.
### The Evidence-Rich Design Chain begins with curriculum intent.
That may sound obvious, but it matters. Visible Agency is not a call to move away from curriculum. It is a call to recover the human purpose inside curriculum. Students need knowledge. They need explicit teaching. They need models, criteria, practice, feedback and disciplinary substance. Human capability does not grow in empty space.
So the first question is: what is this learning for? What knowledge matters? What concept matters? What skill or standard matters? What should students understand or be able to use?
Without that clarity, visible process becomes vague. Students may reflect, but the reflection may not connect to anything important. They may document AI use, but the documentation may not reveal learning. Evidence-rich design begins by anchoring the task in meaningful curriculum intent.
### The second link is capability demand.
This is where the conversation becomes sharper. Once we know the curriculum intent, we ask what students should become more capable of doing through the learning. Are they learning to weigh evidence? Make a design decision? Revise an explanation? Test the accuracy of a claim? Collaborate in a way that changes thinking? Take responsibility for a final position?
Many schools already value these capabilities. They appear in frameworks, graduate profiles, strategic plans and curriculum documents. The problem is not that the language is absent. The problem is that the language is often too far away from task design.
A team may say the task develops critical thinking, but where is the critical thinking required? A task may be described as collaborative, but where must collaboration improve the quality of thinking? A project may be described as student-centred, but where does the student exercise meaningful responsibility?
Capability demand asks the task to show its work.
### The third link is learning design.
This is where the capability has to become necessary. If we want evidence of judgement, the task has to require judgement. If we want ownership, the task has to require students to stand behind decisions. If we want metacognition, the task has to require students to notice and explain how their thinking changed.
In an AI-present world, this matters even more. If a task only asks students to submit a polished product, AI may be able to do too much of the visible work. The student may still be involved, but the teacher may not be able to see where. The design has to create moments where the learner remains necessary.
That does not always require a dramatic redesign. Sometimes the move is small. Compare two possible explanations before choosing one. Identify one AI suggestion you rejected and why. Annotate the revision that improved the reasoning. Defend the final claim in a short conference. Connect one design decision to a user need or source of evidence.
Small design moves can reveal significant learning when they are aligned to the capability demand.
### The fourth link is visible evidence.
This link matters because it protects teachers from overload. Evidence-rich design is not more evidence by default. It is better-chosen evidence.
A short note can be evidence. A highlighted revision can be evidence. A quick oral defence can be evidence. A comparison table can be evidence. A rejected suggestion can be evidence. The point is not to collect everything. The point is to collect enough of the right thing to see the capability developing.
This is where leaders need to be careful. Good ideas can become burdens when they are turned too quickly into templates. If the Evidence-Rich Design Chain becomes a form teachers have to complete for every lesson, it will lose its purpose. It should be a planning conversation before it becomes a document.
### The fifth link is feedback and growth.
Evidence only matters if it improves the learning conversation. A process note is not valuable because it exists. It is valuable if it helps the teacher give better feedback or helps the student understand their own learning more clearly.
If a student's question is weak, the feedback can focus on inquiry. If a student's judgement is strong, the feedback can name discernment. If the revision improves fluency but not reasoning, the feedback can move toward depth. If the ownership defence is thin, the teacher can see that the student may have produced the work without fully standing behind it.
This is the link that makes the chain educational rather than administrative.
For leaders, the chain offers a way to improve professional conversation. It gives teams a shared sequence: What is the curriculum intent? What capability is demanded? How does the design require that capability? What evidence will be visible? How will that evidence strengthen feedback and growth?
Those questions can be used in a planning meeting, a moderation conversation, a coaching cycle or a review of one common task. They do not require the whole school to stop and start again. They help the school look more carefully at the learning it already designs.
The deeper shift is this: schools stop waiting until the end to discover what the task made visible. They begin designing from the beginning for the evidence they most need.
That is especially important with AI. AI does not remove the need for learning design. It raises the stakes. If AI can help students produce more polished work, then schools need to be clearer about what human capability the work still requires. The task needs to make that capability necessary, visible and useful.
There is another reason the chain matters. It protects teachers from being asked to solve the AI problem one task at a time, alone. Without a shared design language, each teacher has to decide how much evidence is enough, what counts as responsible AI use, and how to balance process visibility with workload. That is exhausting, and it produces uneven expectations for students.
A shared chain does not remove professional judgement. It gives professional judgement a clearer pathway. Teachers can still make decisions that fit their subject, students and context, but the questions are common enough to create coherence. What is the curriculum intent? What capability is demanded? What design move makes it necessary? What evidence will be visible? How will that evidence improve feedback?
That shared language is especially important for students. If every class asks for a different version of AI transparency, students quickly learn to perform whatever each teacher wants. If the school builds a common understanding of capability evidence, students begin to see the deeper pattern. They learn that support does not remove responsibility. They learn that final products matter, but that the thinking behind them also matters.
So the practical invitation this week is to take one task and work backwards.
When the final work arrives, what will you wish you could see? Judgement? Revision? Ownership? Metacognition? Discernment? Collaboration? Once you name that, ask where the task requires it. Then ask what small piece of evidence would reveal it.
That is the Evidence-Rich Design Chain in practice.
The evidence available at the end is shaped by the design decisions made at the beginning.
Visible Agency is designed before it is expected.
For the fuller framework, download the Visible Agency Leadership Paper at [https://leecrockett.net/visible-agency](https://leecrockett.net/visible-agency).
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