AI is already finding its way into teacher evaluation.
Not through policy.
Not through formal rollout plans.
But through day-to-day practice.
In a recent survey of 1,280 educators conducted by veteran educator and Marshall Memo founder Kim Marshall, school leaders shared how they’re actually using AI in evaluation — and where they’re still deeply uncertain.
The results tell a clear story:
AI adoption is accelerating… but trust, clarity, and guardrails are lagging behind.
For district and school leaders, this creates both risk and opportunity.
Let’s break down what the field is saying — and what to do next.
What’s Actually Happening: AI Is Already in the Workflow
Across the survey responses, administrators reported using AI to:
- Draft observation notes
- Organize evidence aligned to rubrics
- Summarize post-observation conversations
- Capture and clean up walkthrough data
In some cases, leaders are experimenting with more advanced use:
- Transcribing classroom lessons
- Generating draft evaluation language
- Aligning feedback to instructional frameworks
This isn’t theory. It’s happening in real schools, with real teachers, right now.
The Core Tension: Efficiency vs. Trust
While adoption is growing, the open-ended responses revealed a consistent theme:
Educators want the benefits of AI — without losing the human core of evaluation.
One respondent captured it simply:
“Supervision is relational and cannot be automated.”
Another added:
“AI should assist, not replace.”
This is the tension every district is now navigating:
Principals need efficiency → less time on paperwork
Teachers need trust → authentic, human-centered feedback
The mistake many systems make is treating AI as a replacement tool.
The opportunity is to position it as an amplifier of human judgment.
Four Signals School Leaders Can’t Ignore
#1 AI Use Is Largely Unregulated
Most respondents reported:
- Few (if any) formal policies
- Minimal training
- No shared expectations for use
This creates inconsistency — and potential risk.
Leadership implication:
If you don’t define how AI should be used, your staff will define it for you.
#2 Human Judgment Is Non-Negotiable
Educators were clear:
- Evaluation is built on relationships
- Context matters
- Professional judgment cannot be outsourced
Leadership implication:
AI should support reflection and coaching — not determine outcomes.
#3 Transparency Builds (or Breaks) Trust
A recurring concern:
“Teachers should know if AI is involved.”
Without transparency, even helpful tools can erode confidence.
Leadership implication:
Clear communication is just as important as the tool itself.
#4 Privacy Concerns Are Real — and Rising
Educators raised concerns about:
- Student data
- Teacher performance data
- Where and how information is stored
Leadership implication:
Any AI solution must be aligned to your district’s data and privacy standards.
The Opportunity: Reclaiming Time for What Matters Most
Here’s what’s easy to miss in all of this: The real value of AI isn’t better, or less, paperwork.
It’s better conversations between building principals and classroom teachers — and, ultimately, between classroom teachers and students.
When used well, AI can:
- Reduce time spent writing up observations
- Organize evidence more efficiently
- Capture key moments from classroom visits
Which frees up time for:
- Meaningful coaching conversations
- Relationship-building with teachers
- Instructional leadership
That’s the shift:
From documenting teaching → to developing teachers
A Practical Approach for Schools
If you’re a superintendent, principal, or instructional leader, here’s the practical takeaway:
You don’t need to “figure out AI.”
You need to define how it supports your existing priorities:
- Building trust with staff
- Improving instructional practice
- Creating consistency across evaluators
- Protecting time for real coaching
The districts that get this right won’t be the ones using the most AI.
They’ll be the ones using it most intentionally.
If you want to explore how this shift is already happening in schools, we’ve pulled together a few resources to help you go deeper:
1. Watch the Conversation with Kim Marshall
A practical, real-world discussion on:
- Mini-observations
- Coaching conversations
- Where AI fits — and where it doesn’t
👉 https://rocketpd.com/webinars/from-mini-observations-to-meaningful-debriefs-rethinking-teacher-evaluation-conversations-with-kim-marshall/
2. Read the Guide: Disrupting Teacher Evaluation with AI
A step-by-step look at how to:
- Save time
- Build trust
- Improve feedback quality
👉 https://rocketpd.com/k-12-guides/disrupting-teacher-evaluation-guide-save-time-build-trust/
3. Try the Conversation Summarizer Tool
See how AI can support — not replace — your evaluation process.
- Upload or demo a sample conversation
- See summaries aligned to your framework(s)
- Experience how the process enriches coaching moments
👉 https://rocketpd.com/jesse-schedule/
AI is not the future of teacher evaluation.
Better conversations are.
AI simply gives us a way to get there — faster, more consistently, and with more time for what matters most.
The question isn’t:
“Should we use AI?”
It’s:
“How do we use it to strengthen the human side of teaching and leadership?”
