
Classroom Observation & Coaching Agent
AI-powered support for walkthroughs, teacher feedback, instructional trends, and coaching cycles.
The Classroom Observation & Coaching Agent helps principals, assistant principals, deans, and instructional coaches turn quick classroom notes into clear, professional feedback and actionable coaching plans.
It organizes walkthrough data, surfaces patterns across classrooms, and supports leaders in delivering timely, growth-focused feedback without adding hours of writing time.
Classroom visits are essential—but writing, organizing, and tracking feedback often keeps leaders at their desks instead of in classrooms.
This agent allows leaders to:
It shifts observation from “compliance and paperwork” to continuous instructional growth.
Writing high-quality feedback after each walkthrough can take 30–45 minutes per visit.
The Observation Agent can transform quick notes like:
“Students in groups, some off task; teacher circulating; objective not posted; strong questioning; transitions messy.”
Into:
This keeps feedback clear, specific, and supportive, while saving leaders significant time.
When leaders observe many classrooms, it can be hard to see the big picture.
The Observation Agent can summarize patterns such as:
Example Summary:
“Across 14 walkthroughs this week, objectives were clearly posted in 6 classrooms. Checks for understanding were observed in 4. Recommend a focus on learning targets and quick CFUs during next PLC.”
This helps teams choose building-wide priorities based on evidence, not guesswork.
The agent supports ongoing coaching by generating:
Example:
“Based on the last three observations, you’ve strengthened student engagement through partner talk. Next, let’s focus on using exit tickets twice a week to track understanding.”
This keeps coaching focused, encouraging, and measurable.
Administrators often jot shorthand like:
Raw Notes – Example A (Math)
Raw Notes – Example B (ELA)
The Observation Agent turns these into polished, ready-to-send feedback.
Agent-Generated
Feedback – Example A
Agent-Generated Feedback – Example B
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