The AI Literacy Path for Private School Leaders

The most common AI question I get from private school administrators isn't about tools, costs, or risks. It's some version of: "I want to learn more about AI, but I don't know what I should know."

This article is the map I wish someone had given me — and the one I now use with every group I work with. It walks through the orientation problem, the literacy-versus-fluency distinction that underlies everything, and the six AI competency areas school administrators actually need to build. It's a starting point, not a syllabus.

"I want to learn more about AI, but I don't know what I should know."

In my conversations with private school administrators, this is the single thing I hear most often.

What I see in these conversations is a pattern. Many school administrators are trying to learn AI the same way they learned software tools in the past. That model no longer works.

There is no shortage of information about AI. What is missing is orientation. For most leaders, the real challenge is figuring out where to begin.

Without orientation, the cost shows up quietly across a school. Time gets spent exploring tools without clarity. Different departments experiment in isolation. Essential questions about judgment, risk, and appropriate use remain unanswered. And leaders are left making decisions about AI without a clear sense of what they should understand versus what they can safely ignore.

What private school leaders need is a path to AI literacy and AI fluency that actually fits the realities of school leadership — limited time, and the very different ways AI touches admissions, finance, development, communications, and operations.

That path begins with orientation.

The chicken-and-egg problem

When you do not have a starting point, even good information becomes paralyzing. That is where learning about AI often goes off the rails. A few common responses I see when administrators are trying to start:

• Waiting for the technology to "settle down" before engaging

• Assuming you need technical knowledge before you can do anything useful

• Sampling tools without clarity about where they actually fit

• Treating AI as something separate from everyday school work

• Feeling pressure to understand the whole landscape before taking a first step

None of these responses comes from apathy. They come from uncertainty.

I ran into this myself when I first set out to learn more about AI. I consumed large amounts of information, hoping clarity would emerge on its own.

What finally shifted things for me was recognizing a chicken-and-egg problem underneath all of this.

You need to understand AI before you can decide where to begin. But trying to understand everything before deciding what matters keeps you stuck.

Here is the insight that unlocked things for me:

You don't learn AI broadly to decide how to use it. You learn just enough to decide where depth actually matters.

That shift changes everything.

Once you stop trying to start everywhere, learning gains structure. You begin by understanding the landscape at a high level, then move toward depth only where AI can make a real difference in your specific work.

AI literacy ≠ AI fluency

If orientation is the starting point, the next question is: orientation to what?

AI literacy and AI fluency are not the same thing.

AI literacy is about ORIENTATION.

It is understanding what AI can do, where it tends to struggle, and where human judgment matters most.

I have worked with AI long enough that I have developed an instinct for its potential shortcomings as I use it. I know when to be cautious, when to question an output, and when something feels off, even if it sounds polished.

I worry about the new AI user who has not yet developed those instincts and jumps in expecting AI to behave like traditional software, only to find the results disappointing or, worse, misleading.

If I could bottle that early understanding and hand it to someone just starting out, it would make an enormous difference.

AI fluency, by contrast, is about EXECUTION.

It is using AI confidently in real administrative work, across real tasks, as an extension of your own thinking.

Fluency is often associated with learning specific tools or features, which can be immediately rewarding. But AI is not like the systems schools are used to. Traditional software follows fixed rules and produces consistent results. AI behaves more like a highly capable assistant, one that can help with drafting, analysis, and exploration, but that also makes mistakes and requires oversight.

This distinction matters because AI learning in schools should not be one-size-fits-all.

School administrators operate in non-profit, mission-driven environments. Much of the work is relational, contextual, and values-based. The goal is good judgment, clear communication, and responsible use.

That is why I intentionally separate literacy and fluency within each competency area in the framework I've developed for school administrators. Broad literacy allows school leaders to understand where AI is helpful, where it introduces risk, and where it simply does not belong. Only after that orientation is in place does it make sense to decide where deeper fluency will actually support the work of a particular school administrator.

The goal is not to learn everything. It is to learn what matters, at the right depth, for the work you do.

Six competencies for school administrators

That distinction shapes how learning happens. The next, more practical question is: what should school administrators focus on learning in the first place?

As I explored existing AI learning frameworks, I found most clustered in two areas.

The most common are designed for for-profit enterprises, technical teams, or software engineers. These frameworks emphasize optimization, automation, scale, and technical control. Those priorities make sense in corporate environments, but they don't map cleanly to schools.

Another set of common frameworks focuses on K-12 teachers and classroom use. These are centered on curriculum, instruction, student learning, and assessment. That work is essential, but it reflects a specific instructional context with its own risks and responsibilities.

School administrators sit somewhere else.

Their work sits at the intersection of deeply human decision-making and real operational responsibility. On one side are admission, curriculum leadership, advancement, and family-facing roles, where decisions are personal, emotional, and tightly connected to mission and values. On the other side are business, finance, and operations, where sustainability, budgets, and efficiency matter, as schools must remain financially healthy to fulfill that mission.

AI in this environment cannot be treated as a purely technical or efficiency-driven tool. It has to support judgment, communication, and trust across both sides of the work, respecting the human stakes while also acknowledging the practical realities of running a school.

With that lens, I considered the capabilities that consistently appear in school administrative work across roles. Through that process, I've arrived at six core AI competency areas that reflect how school leaders think, communicate, interpret information, and make decisions. Each competency is developed at two levels: literacy first, then fluency.

1. AI Conceptual Understanding. Understanding how AI behaves, where it is reliable, where it struggles, and why human judgment remains essential. Without this foundation, it's easy to over-trust AI outputs or dismiss them entirely. Good leadership requires knowing when AI helps and when it doesn't.

2. Prompting & Problem Framing. Clearly defining goals, context, and constraints so AI responses are relevant, appropriate, and usable. AI output is only as good as the question behind it. This competency turns vague requests into meaningful support for real administrative work.

3. Workflow Thinking. Seeing AI as part of multi-step administrative processes rather than a one-off tool. Real work doesn't happen in isolation. This competency helps leaders integrate AI where it naturally fits, instead of forcing it into places it doesn't belong.

4. Data Reasoning. Interpreting AI-assisted insights thoughtfully, validating outputs, and distinguishing patterns from conclusions. AI can surface trends quickly, but it doesn't understand context. Leaders need this skill to avoid false confidence and make informed decisions.

5. Strategic Application. Using AI to support planning, scenario exploration, and leadership decisions without outsourcing values or responsibility. AI can expand thinking, but it shouldn't replace it. This competency ensures AI strengthens leadership judgment rather than undermining it.

6. Confidence & Judgment. Using AI responsibly and ethically, with appropriate boundaries around privacy, risk, and role. In schools, trust matters. This competency protects students, families, staff, and institutional integrity while enabling thoughtful AI use.

Together, these six are how I think about the literacy-and-fluency work for school administrators. A leader can be strong in some and developing in others. The framework is a map for deciding where attention pays off most.

What this looks like in practice

A framework only matters if it shows up in the day-to-day work of school leaders.

That's the subject of the companion article, [[550-library/articles/Article 2 - Three Roles One Scenario|Three Roles, One Scenario]] — a Head of School, an Admission Director, and a CFO working through the same post-accreditation moment, each applying AI literacy and fluency differently to their role.

That's the concrete picture. This one was the map.