How to read MAP Growth-style assessment data, use AI to surface patterns, and translate numbers into differentiated instructional plans — without drowning in spreadsheets.
Assessment data is only valuable if it changes instruction. This series gives teachers the literacy to read growth data, the AI tools to analyze it efficiently, and the planning frameworks to act on it.
The DoWEA director called for a MAP Growth-style system: vertically scaled, growth-oriented, administered in fall/mid-year/EOY windows. GES doesn't need to wait for the system rollout — this series builds data literacy NOW so teachers are ready when it arrives.
MAP Growth measures how much a student grew — not just where they are. A student at the 30th percentile who grew 2 years in one year is succeeding. A student at the 70th who stagnated is at risk.
AI tools can analyze a class roster of MAP scores in seconds — identifying clusters, flagging anomalies, comparing to growth norms, and suggesting instructional groupings. Teachers focus on the instructional response, not the arithmetic.
Focused Collaboration meetings restructured as monthly data-response cycles: look at the data, name the pattern, plan the instructional response, implement, and return with evidence. Not compliance — precision.
Before teachers can use data, they must be able to read it. This session builds universal MAP Growth data literacy — what each number means, what "growth" means, and what questions to ask.
| Time | Activity | Description | Format |
|---|---|---|---|
| 0:00–0:15 | LaunchWhat Does This Number Mean? | Show a real (anonymized) MAP report. Ask: "What do you see? What do you wonder? What questions would you ask about this student?" Collect responses. Use to diagnose what teachers already know vs. what needs building. | Whole group |
| 0:15–0:35 | TeachMAP Vocabulary: RIT, Growth, Norms | Explicit teaching of key terms: RIT score (like a ruler, not a grade), conditional growth norms (how much growth is typical for a student at this starting point?), growth percentile (where does this student's growth fall compared to peers?), projected proficiency. Use NWEA's visual explainers. | Whole group |
| 0:35–0:55 | PracticeRead a Student Report Together | Provide a model student report. Teacher pairs work through guided questions: What is this student's fall RIT? What was their spring RIT? How much did they grow? What did NWEA project? Did they meet their growth target? What does this student need next? | Pairs → share |
| 0:55–1:20 | ApplyClass Report Analysis | Provide a model class report (anonymized, multi-student). Teachers sort students into four quadrants: High RIT + High Growth / High RIT + Low Growth / Low RIT + High Growth / Low RIT + Low Growth. Each quadrant requires a different instructional response. | Grade-band groups |
| 1:20–1:40 | DiscussWhat Data Tells Us — and Doesn't | Critical discussion: MAP is one data point. What does it NOT tell us? (Why a student grew, what specific skill is missing, whether the test was a good day.) How do we combine MAP data with our classroom observations? What's the danger of over-indexing on one number? | Whole group |
| 1:40–2:00 | PlanThree Questions Per Student | Each teacher selects 3 students from their roster and writes three data-driven questions for each: "What does this student need next?" "What might explain this growth trajectory?" "Who else might be in a similar situation?" These become the FC meeting agenda item before Session 2. | Individual |
AI doesn't replace a teacher's instructional judgment — it processes data so teachers can focus on students instead of spreadsheets. This session gives every GES teacher a practical AI data workflow.
Before any AI data work: student names and identifying information are NEVER entered into AI tools. Teachers use initials, student numbers, or anonymized data only. This is non-negotiable and must be established before the hands-on portion begins. Principal reviews DoDEA-approved AI tools before this session.
| Time | Activity | Description | Format |
|---|---|---|---|
| 0:00–0:15 | LaunchThe Spreadsheet Problem | Discussion: "How long does it take you to go from receiving MAP data to having a usable instructional plan?" Surface the answer: too long. That's the problem AI solves. Show a 2-minute demo of AI analyzing 24 student RIT scores and generating a grouping recommendation. | Whole group |
| 0:15–0:30 | TeachThe AI Data Workflow | Introduce the 4-step process: (1) Anonymize data, (2) Structure the prompt, (3) Review AI output critically, (4) Add your professional judgment. Walk through each step with a live demonstration using an approved tool (Claude, Khanmigo, or similar DoDEA-approved option). | Whole group |
| 0:30–1:00 | PracticePrompt Engineering for Data | Teachers practice writing AI prompts using a structured template: "I have [X] students with MAP Reading RIT scores ranging from [low] to [high]. The class growth target is [X] RIT points. Please: (1) identify students who need urgent support, (2) suggest 3 groupings for differentiated instruction, (3) identify the top 2 skills to prioritize based on these RIT scores." Iterate prompts together. | Pairs → share |
| 1:00–1:25 | ApplyLive AI Analysis with Class Data | Each teacher (or team) enters their anonymized class data into the AI tool using their refined prompt. AI generates: growth groupings, skill gap summary, instructional recommendations. Teachers annotate: "This matches my observation" / "This surprises me" / "I'd adjust this because..." | Individual/pairs |
| 1:25–1:45 | CritiqueWhen AI Gets It Wrong | Show examples of AI data analysis errors: misidentifying a student's greatest need, overfitting to one data point, ignoring context. Discussion: AI is your assistant, not your expert. What does YOUR professional knowledge add that AI cannot provide? Establish the "always verify" habit. | Whole group |
| 1:45–2:00 | CommitAI Data Analysis Workflow Card | Teachers complete their personal AI data workflow card: which tool, which prompt template, which privacy rules, and how they'll verify AI output. This card goes on their desk — it is their standing protocol for the rest of the year. | Individual |
Data is only valuable if it drives instruction. This session builds the specific instructional responses to each data pattern — so teachers leave with actual lesson adjustments, not just groupings.
| Time | Activity | Description | Format |
|---|---|---|---|
| 0:00–0:15 | DebriefImplementation Share | Pairs share: "I analyzed my data using AI. Here's what I found. Here's one thing that surprised me." Principal listens for patterns — any AI tools concerns, privacy issues, or misconceptions to address. | Pairs → whole |
| 0:15–0:35 | TeachThe Four Quadrant Response Framework | Present the instructional response for each data quadrant: (1) High RIT + High Growth → Acceleration, enrichment, AI-extended challenges; (2) High RIT + Low Growth → Engagement, monitoring, goal-setting conversations; (3) Low RIT + High Growth → Celebrate, sustain, vocabulary-building; (4) Low RIT + Low Growth → Intensive re-teach, small group, progress monitoring. | Whole group |
| 0:35–1:00 | AI ApplyGenerate Differentiated Lesson Adaptations | Teachers use AI to generate 3–4 differentiated versions of an upcoming lesson. Prompt model: "I am teaching [skill] to a [grade] class. I have 4 students who need acceleration, 8 who are on track, 6 who need additional scaffolding, and 3 who need intensive support. Generate differentiated practice tasks for each group." Review and refine AI output together. | Subject groups |
| 1:00–1:30 | Build2-Week Response Plan | Each teacher builds a 2-week instructional response plan for their lowest-growth student cluster: specific instructional moves, AI tools to deploy, progress monitoring checkpoints, and a success indicator. Plans are shared across pairs for feedback. | Individual + pairs |
| 1:30–1:45 | DiscussWhat About the High Flyers? | The director specifically named agentic AI for Tier 2 gifted learners. Discussion: how do we use AI to extend and challenge high-growth students without losing them to busywork? Preview Session 4 of the AI Instruction series. Teachers identify their highest-growth students by name. | Whole group |
| 1:45–2:00 | CommitImplementation + FC Preview | Teachers commit to implementing their 2-week plan. Principal previews how the next FC meeting will function as a data-response cycle — teachers will bring evidence of how the plan worked. | Whole group |
The final session embeds the data analysis workflow into GES's ongoing FC meeting structure — so data-driven instruction becomes the default, not the exception.
| Time | Activity | Description | Format |
|---|---|---|---|
| 0:00–0:20 | ModelA Model FC Data Meeting | Principal and one volunteer teacher model a 15-minute data FC cycle: teacher brings anonymized data, AI-generated summary, and a specific student cluster. Principal asks: "What does the data show? What's your hypothesis? What's your instructional response? What evidence will you bring back?" Class observes and debriefs. | Model + debrief |
| 0:20–0:45 | PracticeRun a Grade-Band Data FC | Each grade band runs their own 20-minute data FC using real (anonymized) data. Roles: data presenter, questioner, recorder. Rotate through. Principal circulates. Each group leaves with one agreed instructional priority and a monitoring plan. | Band groups |
| 0:45–1:05 | DesignThe GES 3-Window Calendar | Together, map GES's assessment calendar: fall window, mid-year window, EOY window. What happens at each window? What AI analysis runs at each? What does the FC meeting look like after each window? Build the shared calendar that goes into every teacher's planning system. | Whole group |
| 1:05–1:25 | BuildAI FC Prep Protocol | Design the standard AI prompt teachers will run before every data FC: "Here is my class's growth data from [window]. Prepare a 5-bullet summary of key patterns, identify 3 students who need urgent attention, and suggest 2 instructional priorities for our next 4-week planning cycle." Practice running it live. | Individual + pairs |
| 1:25–1:45 | ReflectData Culture Self-Assessment | Teachers rate current data practice: Do I look at data before or after making instructional decisions? Do I use data to celebrate growth or just flag failure? Do my FC meetings produce instructional plans or compliance reports? Set one data culture commitment for the next 90 days. | Individual |
| 1:45–2:00 | CloseGrowth Is the Story | Principal closes: "Every student at GES will have a growth story this year. Our job is to know that story before a parent asks — and to be the ones who made it happen." Connect to the GES data identity: we measure growth, not just achievement. We celebrate trajectory. | Whole group |