Your Result — Your AI Opportunity

Your role with AI

How you see the role of AI in education.

Four stages, from keeping AI at arm's length to designing your work around it from the start. Your answers to three questions put you here.

You bring AI into your work intentionally. You're past experimenting and into actually using it — but you're still deciding when it's worth it and when it isn't. That discretion is a strength, not a gap.

Phase 1 Avoid Mostly working
around AI

Phase 2 Bound Experimenting
with limits

Phase 3 Integrate Bringing AI into
real work

Phase 4 Design for it Building AI in
from the start

Your orientation

How you resolve five tensions between human judgment and AI-enabled work.

Your answers to 15 forced-choice questions reveal the priorities and assumptions shaping how you approach AI.

You are The Expert Rebuilder. Transforms systems confidently while keeping deep knowledge at the center.

🛡️

🧭

🤝

🤖

YOU

🚀

⚡

Human-firstAI-first

How your answers got you here

"Is caution about AI an investment in long-term value, or a cost that slows progress?"

Benefit · carefulCost · momentum →

You weigh caution against what moving forward makes possible — and choose momentum. Ethical concerns are real to you, but they don't stop you.

"Should AI make what we do better, or replace how we do it entirely?"

Preserve · improveTransform · replace →

You see AI as permission to start from scratch. Existing approaches earn their place or they get replaced.

"What makes someone hard to replace — what they know, or what they can do with AI?"

← Knowledge · depthAction · fluency

Your value is in what you know. The depth, the judgment, the craft built over years — AI extends it; it doesn't replace it.

"Do you need to be in the loop for AI output to be reliable?"

Low trust · verify allHigh trust · let it run →

You've built real experience. You trust AI with execution because you know what it can handle — and what it can't.

"Has AI made you sharper at what you already did, or made you something new?"

← Deep · sharperWide · transformed

AI has made you more of what you already were. You're sharper, more capable, more grounded — not a different person.

How you work with AI

The relationship you have with AI day-to-day.

Three patterns emerge from how people actually work with AI. Most people don't know which one they are until they see it named.

You probably spend more time shaping AI output than most people around you. You go in knowing what you want and don't stop until it reflects your thinking. That's not perfectionism — that's how Authors build fluency.

Author

Directs the work. You know where you're going before AI is involved, and you shape the output until it reflects your thinking. Your expertise compounds because of how you work.

Collaborator

Works with AI as a thinking partner. Back and forth. Real fluency building on both sides.

Delegator

Assigns work to AI. Uses what comes back. Neither expertise nor AI fluency builds underneath.

How your institution supports you

What your institution provides — and what you're doing beyond it.

Your answers to eight questions about tool access, training, and your own behavior beyond what's been provided.

The gap between these two numbers is the most important thing on this page. You're running well ahead of what your institution has built for you.

Org support

2.8/5

"What my institution provides"

Your initiative

4.3/5

"What I'm doing beyond it"

What your institution provides

I know which AI tools are available to me

4

I understand what those tools actually do

3

The tools cover my actual day-to-day work

3

I know how to apply them to my specific work

2

AI is actively developing my capabilities

2

What you're doing beyond it

Most of my AI learning is self-directed

5

I use AI tools my institution hasn't provided

4

I've built my own AI workflows

4

How AI fits into your work

How you're regularly using AI in your everyday work.

Ten common tasks. How often you're actually using AI for each one — daily, monthly, or not yet.

Using daily

Writing Research

Using monthly

Summarizing Decision support Learning Workflows

Not yet using

Data analysis Automating tasks Client-facing work Code

What comes next

You just surfaced how you think, act, and work with AI.

Most people haven't done that. Here are three things worth doing with it.

01

Share your result.

Send it to a colleague and see if they recognize you in it — or if they'd land somewhere different. That conversation is usually more interesting than the result itself.

02

Notice it in practice.

You're an Author. Next time you're working with AI, watch for the moment you stop editing and start directing. That's the pattern — and now you can see it.

03

Watch for the conversation.

Your results are part of your institution's diagnostic. The conversation your leadership is about to have starts with data like yours — and now you'll know exactly where you fit in it.