Three Roles, One Scenario — What AI Literacy Actually Looks Like

The previous article in this series laid out a path to AI literacy and AI fluency for private school leaders — the orientation problem, the conceptual distinction, the six competencies. This article shows what that looks like in practice.

It's a three-part story. A Head of School, an Admission Director, and a CFO are working through the same moment in their school's life: the period right after an accreditation cycle, where the report is affirming but a few concerns linger. Each of them does different work with AI. Each shows what AI literacy and fluency actually mean in their role.

The Head of School

A Head of School sits at home on a Sunday night, a few weeks after completing an accreditation cycle. The noise has died down, and there's finally space to think about what comes next.

The report was affirming. But a few concerns linger.

The school has struggled with full enrollment for several years. The surrounding community has shifted demographically, culturally, and economically. None of this rose to the level of an accreditation finding, but it raises real questions about long-term alignment and financial sustainability.

She opens an AI chat, not looking for answers, but to organize her thinking. What she wants is clarity and control — a way to turn a vague concern into something she can articulate clearly before bringing it to others.

This is where AI literacy comes into play.

A Head with strong AI literacy doesn't ask AI for solutions. She uses it as a thinking partner. She talks through her concerns, asking AI to help summarize and clarify the core problem. She expects iteration. When the language resonates, she leans in. When it drifts, she redirects.

As clarity emerges, she adds context. She shares the accreditation report and asks AI to surface where these concerns may already be present. She introduces high-level enrollment and fundraising signals, avoiding sensitive data, and asks for synthesis, not conclusions.

Over time, a working document takes shape. Not a plan yet, but a clear articulation of the issue that will anchor an upcoming leadership meeting.

Ahead of that meeting, she asks AI to pressure-test the thinking. Where might tension arise? What questions should she be ready to answer? What outcomes should the meeting produce, and what gaps could stall momentum?

Only then does she ask AI to help sketch an outline — early considerations, discovery work, and milestones.

This is the transition from AI literacy to AI fluency: when clarity gives way to execution, and AI begins supporting repeatable work with intention and responsibility.

The Admission Director

The same school. The same moment. Now through the Admission Director's lens.

He has been asked to research and validate a central thesis:

Does the school's mission and value proposition still resonate with today's families — and could misalignment be contributing to enrollment softness?

At this stage, his role isn't to solve the problem — it's to gather evidence, reduce uncertainty, and help the leadership team see the situation more clearly.

This is where AI literacy comes first.

An AI-literate Admission Director doesn't start with tools. He starts with a research plan.

He uses AI to think through:

• What signals are already available?

• What evidence would support or challenge the thesis?

• What insights can be gathered responsibly, without crossing privacy lines?

To ground the work, he provides context for AI: the Head's problem statement, leadership notes, and the questions the team is trying to answer. Together, they identify a structured set of research areas worth pursuing.

Once the plan is clear, AI fluency comes into play.

He uses AI to execute the work efficiently and repeatably:

• Reviewing anonymized survey feedback to surface themes aligned or misaligned with the concern

• Auditing the website and admissions language to see where the school sounds distinct versus interchangeable

• Researching how peer schools have adjusted messaging in response to demographic and cultural shifts

• Incorporating publicly available sentiment signals on social media as texture, not proof, of how families may be perceiving the school

• Pulling public census and government data to validate assumptions about changes in the local community

He then asks AI to do what it does best: synthesize.

He requests a balanced analysis that includes:

• The most substantial evidence supporting the misalignment thesis

• The most substantial evidence against it

• The key uncertainties that remain

• The questions leadership should discuss before deciding next steps

The deliverable isn't a verdict. It's clarity.

The CFO

As leadership discussions continue around mission, enrollment, and long-term alignment, the financial implications come into focus. Any shift in positioning, program emphasis, or enrollment strategy affects tuition revenue, financial aid, staffing, fundraising expectations, and long-term sustainability.

The CFO knows this terrain well.

She supports the leadership team's willingness to revisit first principles, and she also knows this will be a long and complex conversation. As CFO, her responsibility is to ensure financial implications stay central, not become an afterthought.

She also sees the risk. She does not want the team to move too far down a path that proves financially unworkable. She needs a way to test evolving ideas without repeatedly rebuilding fragile spreadsheets.

She knows how quickly spreadsheets break. Formulas get altered, versions multiply, and she becomes the bottleneck.

This is where AI literacy informs restraint, and AI fluency enables progress.

Rather than starting with a perfect model, she starts with intent. The team is exploring, not executing — what they need is understanding.

Instead of building a robust spreadsheet, she vibe codes through AI a simple internal planning tool for the leadership team. It includes clear, protected inputs, dropdowns for key levers like enrollment, tuition, aid, and fundraising, and immediate visibility into short- and long-term impact.

No one can break it. No one needs to understand formulas. Everyone can explore tradeoffs together.

Because she is AI literate, she knows what not to do. She does not treat this as a final model, assume outputs are audit-ready, or outsource financial judgment.

And because she is AI fluent, she moves quickly. She builds something usable in days, gathers feedback as leaders interact with it, and learns which variables truly matter.

Only later, once direction is clearer, does she translate that learning into a precise, report-ready financial model spreadsheet.

What these three share

All three roles use AI. Each uses it differently. The Head of School uses it to articulate. The Admission Director uses it to investigate. The CFO uses it to model.

What they share is a discipline — letting literacy guide what they ask of AI, and reserving fluency for the work where AI clearly helps.

The goal isn't to use AI. The goal is better decisions, at the right level of fidelity, at the right moment.