Is AI Still Optional for Educators?

For the past few years, educators have largely been given a choice about AI. They could attend a training, experiment with a tool, use it to support planning, or decide they were not ready yet. There was room to watch, learn, question, and figure out where AI fit into their work. But as another school year begins, that choice is starting to feel different. No policy may say teachers are required to use AI, and no administrator may have formally announced that expectation, but knowing how to use it is beginning to carry professional weight.

You can hear it in ordinary conversations. A teacher shares that AI helped her differentiate a lesson in minutes. A colleague talks about using it to draft family communication. A team discovers that something that used to take an afternoon can now be completed much faster. None of this is unusual anymore, but these small changes begin to establish a new baseline for what educators are expected to know and how quickly certain work is expected to happen.

That is where the shift becomes important.

AI is moving from something educators could choose to explore toward something they may increasingly be expected to know how to use professionally, even when schools have not formally defined that expectation.

Consider what happens in a planning meeting when most of the team is already using AI to develop materials, adjust reading levels, or generate starting points for instruction. The teacher who is not using it may still be doing excellent work, but the conversation around her has changed. She may begin wondering whether her choice is still viewed as professional discretion or whether it is becoming evidence that she has fallen behind.

The same thing can happen with professional learning. A district offers an AI session that is completely voluntary. Some educators attend, begin using what they learned, and bring those practices back to their teams. Months later, the training may still be optional on paper, but understanding what everyone is talking about no longer feels optional.

There is also a quieter expectation developing around time. If AI can draft a document, organize information, create a first version of a lesson, or reduce the time required for certain administrative tasks, expectations about how long professional work should take may begin to change. A principal does not have to tell a teacher to use AI for that pressure to be felt. The availability of the tool itself can begin changing what people consider reasonable.

The expectation may also begin showing up in places where teachers are accustomed to receiving professional feedback. Imagine a teacher being observed and later hearing that AI could have helped her differentiate the lesson more effectively for the range of learners in the room. Another teacher may be told that AI could have helped him provide more targeted feedback to students, develop additional scaffolds, or adjust materials more efficiently. The administrator may intend those comments as suggestions, not requirements. But once AI use enters observation and feedback conversations, the distinction becomes harder to maintain.

A teacher could reasonably leave that conversation wondering whether she was being encouraged to try something new or whether not using AI was now being viewed as a weakness in her practice. If AI does not appear anywhere in the evaluation criteria, has not been established as a professional expectation, and may still be presented by the district as optional, that ambiguity matters.

This raises a question schools need to consider before the answer is established informally through culture:

Can an educator still be considered highly effective if they choose not to use AI for certain parts of their work?

That question becomes more complicated when the decision not to use AI is intentional.

A teacher may decide not to use AI to respond to student writing because she wants to hear the student's thinking herself. A counselor may understand AI well and still decide that it does not belong in a sensitive conversation with a student. A special educator may determine that a decision involving a student's needs requires professional knowledge and context that should not be handed to a tool. A principal may choose to write a difficult family communication personally because the relationship matters.

None of these decisions necessarily reflects a lack of AI competency. In some cases, they may demonstrate it.

Why It Matters

Schools are going to have to decide what they actually mean when they say educators need to become competent with AI. If that definition remains unclear, professional culture may create one on its own.

Frequent use can begin to look like competence. Speed can begin to look like effectiveness. Experimentation can begin to look like professional growth. Caution can begin to look like resistance.

That creates a problem because responsible AI use requires more than knowing how to get a useful output. It requires knowing what information should not be entered, when an output needs to be questioned, when student context changes the decision, when professional judgment matters more than efficiency, and when AI simply does not belong in the work.

An educator who understands those boundaries and decides not to use AI in a particular situation may be demonstrating exactly the kind of professional judgment schools should want.

This distinction becomes especially important when schools evaluate professional practice. Two teachers may demonstrate the same instructional competency in very different ways. One teacher may use years of experience and deep knowledge of her students to differentiate a lesson. Another may use AI to help develop differentiated materials and then apply professional judgment to make them appropriate for the students in front of her. If both teachers are effectively meeting student needs, the competency being evaluated should be the quality of the instructional practice, not whether AI was used to produce it.

The same is true for feedback. If the professional expectation is that students receive timely, specific feedback that advances their learning, then that is what should be evaluated. Whether a teacher used AI to help develop that feedback is a separate question unless the school has explicitly established AI use as part of the professional standard.

Schools should begin asking themselves: Are we evaluating the quality of professional practice, or are we beginning to evaluate whether educators used AI to produce it?

Without that distinction, another shift could follow. Educators may eventually find themselves explaining not why they used AI, but why they did not.

Why did this take so long? Could this have been done more efficiently? Why are you still doing this manually? Why aren't you using the tools available to you?

Those questions may be appropriate in some circumstances. But they become much more consequential when educators are being measured against an expectation their school has never actually defined.

Schools can address this before it becomes embedded in professional culture. During early faculty and leadership conversations this year, leaders can define AI competency in terms of judgment rather than frequency of use. Staff should understand what everyone is expected to know about privacy, accuracy, bias, student safety, appropriate use, and human oversight, whether they use AI every day or rarely.

Principals and supervisors can also pay attention to how AI use enters conversations about efficiency and professional growth. Using AI should not automatically become evidence that one educator is more innovative, capable, or committed than another. Instructional leaders can ask educators to explain both kinds of decisions: why AI was appropriate for a particular task and why they intentionally decided not to use it for another.

District leaders have a role as well. If AI competency is becoming part of professional expectations, staff deserve clarity about what is actually expected. Educators should not have to infer those expectations from colleagues, optional professional development, productivity pressures, observation feedback, or the language used in evaluation conversations.

Educators do need to understand AI because it is increasingly part of the environment in which they teach and lead. But understanding a technology is not the same as being obligated to use it for every task it can perform.

Sometimes professional competency will mean knowing how to use AI well. Sometimes it will mean recognizing its limitations. And sometimes it will mean making a deliberate decision that this particular work still needs to be done by a person.

Schools need to make room for all three before an unwritten expectation decides what being a competent educator looks like.

This is one of many hidden shifts AI is introducing into education. I write these newsletters to help educators and school leaders see them early and respond intentionally. If that matters to you, stay with the series.