AIP Tools Β· Kβ12 AI Governance
AI Adoption Metrics Worksheet
A measurement framework for schools. Use this as a planning worksheet: pick the modules that matter, define your KPIs, and decide which charts to build in your existing reporting tools (Sheets, Excel, Power BI, Looker).
The problem: Without data, AI decisions are made by instinct. This worksheet defines the modules, KPIs, and visualizations a school should track. Pair it with your existing reporting stack (Google Sheets, Excel, Power BI, Looker) β the framework is tool-agnostic.
10 Modules to Track
π
Teacher AI Literacy Tracker
- Aggregate by school, grade, subject
- Pre/post quiz scores
- PD credential level distribution
- Trend over time (monthly)
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Student AI Literacy Tracker
- Pre/post by grade level (7β12)
- Proficiency band distribution
- Equity analysis by student group β no group under 5 reported
- Year-over-year cohort tracking
π±
Tool Usage Analytics
- Logins per tool per week
- Active users vs. licensed seats
- Feature adoption rates
- Underutilized tool alerts
π¨
Incident Tracking System
- Frequency by class (P1/P2/P3, policy Β§10)
- Resolution time tracking
- Tool-incident correlation
- Board reporting auto-export
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PD Completion Tracker
- Credential levels by staff member
- School-level completion %
- Annual renewal compliance
- Roster gaps alert
π¨βπ©βπ§
Parent Sentiment Dashboard
- Survey scores (1β5 avg)
- Opt-out rates by grade/school
- Sentiment trend over time
- Concern category breakdown
π°
Budget Tracking Module
- Spend vs. plan by category
- Cost per active student (actual)
- ROI calculation (live)
- Grant offset tracking
π’
Vendor Performance Scorecard
- Uptime % per tool (vs. SLA)
- Support ticket volume
- Privacy compliance status
- Contract renewal alerts
πΊοΈ
Visualization Library
- Heat maps (school vs. metric)
- Trend lines (6-month, 12-month)
- Baseline vs. re-baseline charts
- Board-ready exports (PDF)
π
Six-Month Re-baseline
- The same instruments, re-run
- Readiness and Governance, never averaged
- Change against your own baseline
- No comparison with other schools
Key Performance Indicators
| Metric | Data Source | Example target β set your own | Frequency |
| Teacher AI literacy gain | Quiz pre/post | +30% | Per training cycle |
| Student AI literacy gain | Quiz pre/post (Grades 7β12) | +25% | Annually |
| PD completion rate | Credential system | 100% Level 1 | Monthly |
| Tool usage rate | Vendor analytics API | 80%+ licensed users active | Weekly |
| Incident rate | Incident log | Recorded, not targeted β a target on incidents discourages reporting them | Real-time |
| Incident deadlines met | Incident log | Policy Β§10: P1 contained in 24h Β· P2 panel in 48h Β· P3 closed in 5 days | Per incident |
| Parent sentiment score | Survey | 3.5/5 average | Quarterly |
| Parent opt-out rate | Opt-out forms | <20% | Per semester |
| Cost per active student | Budget + usage | Within 10% of plan | Monthly |
| Vendor SLA compliance | Uptime logs | 99%+ per tool | Monthly |
Suggested Output Formats
π Board Report (print to PDF)
π Excel / Google Sheets
π§ Email Digest (manual)
π Internal Wiki Embed
π Grant Application Data
Suggested Visualizations
- Teacher Literacy Heat Map β Color-coded grid showing AI literacy scores by school and subject area. Identifies low-performing pockets for targeted PD.
- Student Progress Trend Line β Pre/post literacy scores over multiple years by grade cohort. Correlates with training investment.
- Incident Timeline β Chronological log of all incidents with class (P1/P2/P3), tool, resolution time, and outcome. Board-ready.
- Opt-Out Rate by Grade β Bar chart showing parent opt-out rates disaggregated by grade level and school. Flags equity concerns.
- Budget vs. Actuals β Waterfall chart showing planned vs. actual spend by category with grant offset visualization.
- Re-baseline Radar Chart β Your school across 8 dimensions, now against your own baseline. There is no validated sector average to compare with, so the comparison is with yourself.
- PD Completion Funnel β Shows progression from Level 1 to Level 3 across staff β identifies credential gap at each stage.