Turning ‘I Notice / I Wonder’ into Targeted PLC Goals
Why “I Notice / I Wonder” Is a Smart Starting Point—And Where PLCs Often Get Stuck
Let’s be honest—data conversations in PLCs can be difficult. Whether you're analyzing student work, reviewing common formative assessments, or unpacking grade-level trends, it’s easy for emotions to rise. Teachers can feel vulnerable, especially when data reveals instructional gaps or persistent learning challenges. This can lead to shallow discussions, avoidance of the hard topics, or worst of all, no follow-through.
One of the most effective strategies to lower the stakes and invite genuine reflection is the “I Notice / I Wonder” protocol. This simple tool offers an open-ended way for teachers to engage with student data, without immediately jumping into conclusions or defense.
Here’s how it works:
“I Notice” prompts ask participants to share objective, observable trends or patterns in the data or student work.
“I Wonder” prompts allow participants to ask questions, reflect on causes, or identify areas that need deeper exploration without pressure to solve them on the spot.
This protocol levels the playing field and helps every voice contribute to early meaning-making. It shifts the conversation from judgment to curiosity and lays the groundwork for authentic inquiry.
But here's where teams often get stuck:
After generating a list of wonderings, conversations frequently:
Stall because no one knows what to do next
Drift toward tangents or complaints unrelated to instruction
Fail to connect reflections to clear, strategic next steps
The result? Great thinking gets lost, and the energy in the room fades. The protocol starts strong, but without a bridge to action, it ends up as another good discussion that doesn’t lead to instructional change.
That’s what this article is here to help with.
Why This Article Also Includes AI Tools
As a leadership coach and facilitator, I’m always thinking about how to help teams move from conversation to action efficiently and effectively. That’s why I’ve included practical AI suggestions in each step of this article.
Using generative AI tools (like ChatGPT or other platforms) in a professional context can:
Save time by organizing ideas or offering draft language
Offer multiple perspectives when teams feel stuck
Provide a neutral third voice to help balance strong opinions
Break the blank page problem when setting goals or writing action steps
Give educators a safe, low-stakes opportunity to try AI in a focused, productive way
Think of AI here the same way we think of protocols: it’s not the answer, it’s a support tool to help us get to better thinking faster.
Important Note:
When using AI to support PLC work, do not include student names or identifying information. All input should remain general and anonymized. AI should never be used to evaluate individual students or staff. It’s a tool for brainstorming and planning, not documentation or decision-making.
Let’s walk through how your team can move from “I Wonder” to action in five clear steps with suggestions for how AI can assist at each point.
Step 1: Identify Patterns in the Wonderings
After your PLC has gone through the “I Notice / I Wonder” process, you’ll likely have a long list of open-ended questions and observations. This is where many teams start to feel overwhelmed. To move forward, you’ll need to organize these thoughts into categories that help the team focus their inquiry and prioritize their work.
Let’s look at a real-world example.
Background: A Typical 6th Grade Math PLC
This 6th grade team meets weekly to review student data. They’re committed, experienced, and genuinely care about improving outcomes, but they’ve been frustrated. Despite strong instructional planning and consistent coverage of their pacing guide, their students’ performance on recent common formative assessments (CFAs) hasn't improved meaningfully.
During their most recent PLC meeting, they reviewed results from a CFA focused on fraction division. The assessment included both computation and multi-step word problems. Teachers noticed that students generally did fine on the computational tasks but struggled significantly on the word problems. Some questions were skipped, others were answered incorrectly without any work shown, and several students provided unrelated operations.
The team decided to use “I Notice / I Wonder” to guide the conversation and slow things down.
Here’s what they came up with:
Wonderings:
I wonder if students understand what the word problems are asking.
I wonder if our instruction on mixed numbers needs to be more explicit.
I wonder if they are rushing or running out of time.
The conversation was productive and reflective, but it began to drift. One teacher began discussing the assessment format. Another questioned student motivation. A third wondered if the issue was reading level.
This is a common moment where PLCs lose momentum: lots of ideas, not enough clarity.
Facilitator Move: Group Wonderings into Categories
Instead of diving straight into solutions, the team was guided to group their wonderings into themes. This allowed them to see the patterns in their thinking and set up a more focused next conversation.
These wonderings could be grouped into:
Comprehension of word problems (Are students understanding what’s being asked?)
Clarity of instruction (Have we explicitly taught the concepts embedded in the problems?)
Time management or stamina (Are students rushing or giving up halfway through?)
By doing this, the team was able to narrow their focus to problem comprehension and instructional clarity as their next step rather than trying to address every possible issue at once.
AI Support Prompt:
“Group the following teacher wonderings into categories such as instruction, engagement, comprehension, or assessment. Identify any patterns that emerge.”
This kind of categorization saves time and ensures the team begins its inquiry from a shared understanding. It shifts the tone from brainstorming to problem-solving with purpose.
Step 2: Explore Root Causes
After grouping their wonderings, the 6th grade math team agreed to focus on one central issue: Students are struggling to make sense of word problems, even when they’ve mastered the computational skills.
At this point, the instinct for many teams is to jump into trying a new strategy or resource. But effective PLCs pause and ask:
Why is this happening?
What’s beneath the pattern?
Why Root Cause Analysis Matters
If the team rushes to apply a fix without fully understanding the issue, they might address the wrong problem altogether. For example, adding more practice problems might not help if the problem is instructional clarity. Or adjusting test formatting won’t matter if students don’t understand the mathematical language embedded in the question.
By slowing down and exploring root causes, teams shift from surface-level solutions to strategic planning.
What This Looked Like for the 6th Grade Team
They used a simplified version of the “5 Whys” protocol to explore:
Why are students skipping word problems?
Because they may not understand what the problems are asking.Why don’t they understand what’s being asked?
Because the language and structure are unfamiliar or overwhelming.Why is the language unfamiliar?
Because the team hasn't yet taught explicit strategies for deconstructing or visualizing problem scenarios.
This led to a realization: While the team had been teaching fraction computation well, they hadn’t taught students how to unpack and approach the story problems systematically. That’s a different instructional gap and one that’s fixable.
Common Root Causes to Consider in Similar Situations:
Lack of explicit modeling of problem-solving processes
Limited student access to academic language
Inconsistent use of scaffolds or sentence frames
Cognitive overload from multi-step problems
Low student confidence or perseverance with longer tasks
AI Support Prompt:
“We noticed that many 6th grade students are skipping multi-step word problems on a formative assessment. What are four potential instructional or cognitive root causes our team should consider?”
Using a prompt like this can give teams a starting point for reflection and spark healthy discussion, not replace it.
Step 3: Turn Wonderings into SMART Goals
Once the team identified the root cause, lack of explicit instruction in how to break down word problems, they needed a clear, measurable goal to guide their next steps. This helps ensure alignment across classrooms and builds accountability.
Why SMART Goals Matter
Vague goals like “We’ll help students do better on word problems” often lead to vague results. A SMART goal (Specific, Measurable, Achievable, Relevant, and Time-bound) keeps the team focused and makes it easier to track progress.
What the 6th Grade Team Created
Wondering:
I wonder if students understand what the word problems are asking.
SMART Goal:
By October 1, all 6th-grade math teachers will implement a shared strategy for teaching problem deconstruction. Students will show a 20% increase in identifying the correct operation in word problems on the next formative assessment.
This goal gave the team clarity on:
What they would teach
How they would measure progress
When they would revisit results
AI Support Prompt:
“Turn this wondering into a SMART PLC goal: ‘I wonder why students aren’t understanding how to solve fraction word problems.’”
Again, AI can be used as a brainstorming partner, not a decision-maker.
Step 4: Plan Action and Monitor Progress
With their SMART goal in place, the 6th grade team built a 4-week action plan to test out a shared instructional strategy and monitor student responses.
Why Planning Matters
This is where many teams fall into the “hope trap.” They identify a problem and set a goal, but don’t outline exactly what they’ll do differently. Without intentional planning, even the best intentions fade under the weight of day-to-day demands.
Sample Plan from the 6th Grade Team
Week Action Step Week 1 Introduce a visual anchor chart that shows how to break down word problems into steps: underline key words, label quantities, and identify the operation. Week 2 Model the process using think-alouds during whole-group instruction. Include “we do” and “you do” practice problems. Week 3 Have students complete labeled exit slips with multi-step problems. Collect 3 samples per class for team review. Week 4 Bring samples to PLC. Analyze responses to determine trends and needed adjustments.
Teachers also committed to:
Using common language when modeling
Bringing student work to the next PLC
Sharing notes on how students were responding
AI Support Prompt:
“Draft a 4-week PLC action plan to help students improve their comprehension and response to fraction word problems.”
This support can be a huge time-saver when building consistency across grade-level teams or departments.
Step 5: Revisit and Recalibrate
At the end of the four-week cycle, the team reconvened to reflect on progress and decide what to do next.
They looked at:
Exit slip data: Were students identifying the correct operation more often?
Student work samples: Did students attempt more problems and show evidence of reasoning?
Teacher reflections: What worked well? What didn’t?
The Outcome:
Student accuracy increased on the next CFA by 18%, just shy of their 20% goal.
More importantly, teachers saw increased engagement and confidence—students were no longer skipping the word problems.
Teachers realized they needed more time to reinforce the new strategies and adjust pacing.
Rather than abandoning the strategy or declaring “it didn’t work,” they adjusted and planned another 2-week cycle to reinforce and deepen the approach.
Why This Step Matters
This short-cycle reflection is what makes PLCs effective over time. It's not about perfection. It’s about building a habit of inquiry, trying strategies, and learning from what happens.
Final Thoughts
The “I Notice / I Wonder” protocol is an incredibly powerful way to surface insight, build trust, and spark genuine curiosity in PLC conversations. But it’s just the beginning.
Without a clear structure to follow, teams can drift back into reflection without action—or worse, action without clarity.
By using a simple five-step process: identify patterns, explore root causes, set a SMART goal, plan intentionally, and reflect, you empower teams to focus their energy on what matters most: improving outcomes for students.
Adding thoughtful, responsible use of AI into this process can:
Accelerate planning
Provide neutral starting points for conversations
Help teams focus on student learning—not logistics
It’s not about replacing professional insight. It’s about supporting teachers with tools that help them move further, faster—together.
