Mature AI Use vs. Immature AI Use
A Replacement Framework for the Ethical AI Use Debate
Most schools have arrived at some version of the stoplight. Red means no AI. Yellow means AI with conditions. Green means AI use is permitted. Getting there — reaching a yes, a no, or a qualified maybe — has consumed years of committee meetings, professional development sessions, and policy drafts. For many schools, getting the light to turn is still the project.
But a growing number of educators have cleared that hurdle and are standing in the room that comes after. The light is yellow or green. A teacher has decided that AI use is going to happen in their classroom, under some set of conditions, for some defined purpose. And now they are looking at a blank page where the guidance should be.
The instinct in that room is to write an ethics policy.
It is the wrong instinct, and understanding why is the difference between a policy that does something and one that produces a document.
Nearly every educator I speak with who has reached the yellow or green stage asks the same set of questions: how do I define ethical AI use for my students? How do I teach it? How do I evaluate it? The questions are reasonable. The framework underneath them needs work.
Ethics is the right lens for the stoplight. Whether to allow AI at all, and under what circumstances, involves questions that belong to the community: the environmental cost of large language models, intellectual property and the integrity of other people’s work, the institutional standards a school sets for itself. These are communal decisions. Communal standards are the right tool for making them.
But once a student is sitting in front of an AI and engaging with it — once the light has turned and the decision has been made — the question of how that engagement unfolds is not an ethical question. It is a maturity question. Applying a communal ethical standard to the quality of an individual student’s cognitive behavior is an error of mis-categorization, and it is why most AI use policies, however carefully written, are not doing what their authors think they are doing.
Ethics Are Communal
Ethical frameworks are meant to govern behavior across a community. Every application of ethics in societal life, though, runs through individual circumstance.
Take violence. By most standards, it is wrong to commit murder, wrong to commit acts of violence. And yet the standards of fairness we maintain are predicated on self-defense, psychological wellness, and the specific facts of each situation. When a person commits a violent act and claims self-defense, a trial ensues. Circumstantial evidence is presented. What was going on before the act? Did the situation require violence to survive? Who was the aggressor, and to what degree? Was the defendant capable of knowing what they did was wrong?
Each of these questions takes into account the specific characteristics of the situation and the individual. The lawyers tell a story. The judge considers the person and the event in context. The jury determines whether the specific incident met one standard or another.
When the UnitedHealthcare CEO was shot in December 2024 and a significant number of people cheered. As abhorrent as it was, it points to something important that we seem to be forgetting about ethics in and of themselves.
Thousands of years of legal and philosophical development, and we still cannot agree on whether or not murder is always wrong. Why? Because we automatically take circumstances into account, and our evaluation diverges. People thought the murder was “right.” To be clear, I am not in their camp. But they thought the murderer was justified based on how health insurers have treated customers over the past few decades.
As a broad conclusion, these examples point to the fact that ethics provide a set of communal standards, but they are always applied via individual circumstance. The problem with Ethical AI Use discussions, it seems to me, is a failure to recognize and apply similar circumstantial evaluation practices when it comes to determining whether a student meets the “Ethical AI Use” standard.
How does this problem manifest itself in an AI engagement? Well, for one, AI use is highly circumstantial as well. Every AI chat is different. The record of actions, choices, and responses is individual in nature, and the circumstances that led a student to engage the way they did are unique and specific.
Was this a 9th grader? A freshman in college? A law student? A 5th grader? What were they using it for — brainstorming? Research? Drafting? How much instruction had they received beforehand? What scaffolds were they given? How much time? What were the incentives?
A single educator will see these variables and respond; “But my students all fall within the same set of circumstances?” Ah, but do they? Let’s go further.
Assume the answers to all of those questions are identical for a class of 25 fictional 9th graders. Same situational variables, every student.
What is Student A’s cognitive level compared to Student B’s? How much has Student C used AI compared to Student D? What is Student E’s reading level compared to Student F? Student G’s writing level compared to Student H? How much feedback on AI use has Student I received compared to Student J? How many opportunities has Student K had to practice meeting the standard compared to Student L?
That list does not begin to cover it. It doesn’t even account for the differentials in the AI’s responses themselves. Now imagine applying an ethical standard to all 25 students at once, without taking their individual circumstances into account. This is what an AI ethics policy asks teachers to do. And this is why it fails.
The Essay and The Chat
Teachers already know how to do what I am describing. They do it every time they grade an essay.
Consider how the average teacher evaluates a student’s ability to meet a writing standard. Science teachers: read this as if I am talking about lab reports.
Jimmy and Jenny are in the same class at different performance levels. Jimmy struggles with reading, Jenny reads above grade level. Jimmy’s organization is behind the curve, Jenny has received support at home that keeps her diligent. When I grade Jimmy’s paper I apply my rubric, find the gaps and the highlights, mark them and record the points.
Then I take into account Jimmy the person:
Based on where he was before this paper, did he improve? Did he try? Is there evidence of effort? Did he take my feedback and apply it in some fashion?
Those last questions are maturity questions.
In academic terms, maturity equates to effort. I cannot expect Jimmy to ace the essay, but I can expect him to try. If he does, he deserves credit. He may have fallen short of the standard, but that does not make him a bad person. Nor does it mean he did not grow, or try, in the context of the assignment. He showed maturity, and for that he deserves credit.
Now imagine evaluating that same paper through the lens of ethics. Imagine saying to Jimmy: you wrote this paper, but you did not meet my standard. Therefore, you are unethical.
That sentence sounds absurd when applied to a writing assignment. It sounds equally absurd when applied to an AI chat — and yet that is the framework most schools are currently building and seeking to apply.
What the Maturity Standard Actually Looks Like
The above graphic visualizes what it might look like to design evaluation standards for a student’s AI use. You certainly can - and probably should – have a standard set of criteria for the whole class if you are assigning a “yellow” or “green” assignment.
But if you are subsequently reading their AI use and asking yourself whether they met your standard, it isn’t enough to execute a 1:1 comparison.
Instead, your evaluation criteria should be combined with these (and other) highly individualized set of evaluation mechanisms. From here, it becomes much easier to say to a student something like the following:
“I like how you did X. That very clearly matched the criteria I laid out for this AI engagement. However, at one point in the dialogue (Y), your approach appeared to veer away from what we have defined as meaningful engagement for this AI use. In the future, try Z.”
Sound familiar? It’s the Glow-and-Grow framework, applied to an AI chat. Complicated? I dare say not, especially if you have already determined your criteria for that particular engagement (research, brainstorming, drafting, sparring — all taking into account the level of scaffolding provided).
This is how I provide AI use feedback to students in my Rhetoric and Inquiry class at Fairleigh Dickinson University — and it works. Students understand what I want them to do, and apply it the next time around.
Additionally, please notice that nowhere in this feedback did I imply that the failure to meet the criteria indicated an ethical failing. I wouldn’t tell Jimmy (or Jenny) that they were a bad person if they failed to meet my essay requirements. So why am I telling a student that they committed a moral failing when they struggle to meet my metacognitive engagement standards with AI?
AI Discussions at Schools
One of the reasons schools stall in the policymaking process is that their committees are collapsing two distinct conversations into one. Educators and administrators keep asking what it means to use AI ethically, when the more useful question — once you are past the stoplight — is what it means to use AI maturely.
The stoplight itself is an ethical decision. A red assignment means the educator has determined, for whatever reason, that AI use is inappropriate in this context. That determination belongs to ethics: it is a communal judgment about what is acceptable.
Once the light is yellow or green, the ethical question has been answered. What remains is the maturity question: given that a student is engaging with AI, given everything we know about where they are developmentally, did they engage at the level they are capable of?
Institutions that recognize the distinction can structure their conversations accordingly. The stoplight debate — whether and when — belongs in one room, governed by communal ethical standards. The guidance on how to teach and evaluate AI use belongs in another room, governed by the maturity framework. Both conversations need to happen. Running them simultaneously is why so many committees produce documents that satisfy no one and guide no one.
The Conditions That Make It Possible
The maturity frame has direct implications for how you design AI interactions in the first place. The design of the interaction matters as much as the student’s response to it. When students encounter a tool that removes all resistance, they find the minimum viable input and stop. That is a rational response to the incentive structure. When they encounter an interaction that maintains resistance — one that requires thinking, adapting, and persisting — the transcript reveals something no essay can: how they engage with difficulty. And that, in turn, reveals maturity.
AI Friction Labs designs interactions that meet these conditions. They are structured in a way that makes meaningful engagement much clearer to the student. They reduce the cognitive burden on both the educator and the student as it pertains to defining the “how.” In turn, they develop the “soft” or “future-ready” skills in context. You can see how they engage with simulated real-world difficulty and provide an arena for them to engage with AI maturely.
Reach out at https://aifrictionlabs.com to learn more.
Ethical AI Use is about “Whether and When.” Mature AI Use is about “How.”
When you have your next AI Committee meeting, split the two. You will find that the conversation gets shorter, the guidance gets clearer, and the teacher standing in front of a student’s chat transcript finally has a framework for understanding what they are looking at.
I still think the root of the problem is that we need to rethink our instructional design entirely. We are putting old wine into a new wineskin.