AI has already become part of everyday work in schools. You're probably seeing it across instructional planning, student support, tutoring, career exploration, and even special education workflows. Educators and students are moving fast, often faster than most districts can actually govern.
But how do you make sure AI is being used thoughtfully and safely, in ways that actually improve student outcomes?
The Adoption-Only Goal Trap
Here's a goal we see a lot: "By the 2026-27 school year, 75% of teachers will use [insert AI product] regularly."
It's a measurable goal and sounds forward-thinking, but missing something crucial: the why. What outcomes are you hoping to improve? Is adoption actually making the work better, or just making more activity happen?
There's a better way to set AI goals and measure success, and it starts with four questions.
Question 1: What Are Our Leadership Goals?
Most AI conversations go like this: Should we enable this platform? Give teachers access to ChatGPT? Open it up for students?
Those questions make sense, but they shouldn't be first. Start here instead: What problem are you actually trying to solve? For example:
- You need to increase graduation rates
- Special ed teams are buried in documentation
- Attendance interventions aren't moving the needle on chronic absenteeism
- Teachers need better support differentiating and implementing state-required HQIM with fidelity
- Students need more personalized support exploring career options
Once you're clear on what you're trying to fix, it becomes easier to see where AI can help.
Question 2: What Are the Measurable Outcomes That Would Show Progress?
Once you know what problem you're solving, get specific about what progress looks like. We're talking outcomes, not activity metrics.
Most districts already have outcome goals in their strategic plan, so connect your AI work directly to one of them, and make sure it's quantifiable. So instead of this goal:
- We'll hit 75% teacher adoption of AI by next year.
Connect to an outcome goal like one of these (ideally with a specific target):
- We want to improve literacy outcomes (e.g., move 3rd-grade reading proficiency from 62% to 70%)
- We want to stop students falling off track before graduation (e.g., reduce semester failures by 15%)
- We want special ed teams creating stronger, compliant documentation (e.g., achieve 90% IEP compliance audit scores)
- We want faster, more actionable student support (e.g., reduce intervention referral-to-implementation time from 6 weeks to 2 weeks)
Question 3: What Would High-Quality AI Actually Look Like?
Before you scale anything, define what "good" means in your context.
This should involve a broad team: teachers, counselors, curriculum leaders, special ed leaders—anyone who will be using and measuring the success of AI should be involved. The goal is a shared definition of quality.
The questions to ask:
- What should a strong output include?
- What should it never do?
- What local context does it need to understand?
- What compliance or policies apply?
- Where does human judgment need to stay in charge?
- What would make this useful to an educator or student?
Some examples of what "high-quality" might mean:
- Educators trust outputs
- Recommendations feel specific to students
- Documentation meets district standards and state requirements.
The higher the stakes of the workflow, the more critical this gets. If you're using AI in special ed? Define quality clearly. Instructional planning? Same thing. Student-facing? Absolutely.
Question 4: How Will We Know It's Working?
The short answer? You need evidence. That evidence could look like:
- Feedback from educators
- Quality audits of outputs
- Student outcome data
- Documentation reviews
- How much time educators are saving
The whole point is having enough visibility to answer one simple question: Is this helping us do better work for students?
Before You Scale, Secure
One more thing: security and privacy come first.
You need a foundation for privacy, security, and governance, because AI could have access to student data, instructional planning, intervention supports, and documentation.
When you're evaluating tools, ask:
- Is this secure?
- How is student data protected?
- Who can see what AI is doing and what it produces?
- What guardrails exist for student-facing AI?
- Where do humans need to stay in control?
The Bottom Line
AI won't improve outcomes on its own. But districts that approach it with intentionality, clear guardrails, and a definition of quality? Those are the ones that see real change.
Ready to Dig Deeper?
These four questions are just the starting point. Our AI Governance Playbook provides frameworks for steering AI toward quality, testing outputs in realistic scenarios, scaling responsibly.
