AI adoption in schools is accelerating—but most districts have no consistent way to measure whether teachers are ready to use AI safely and effectively.

Understanding staff AI literacy is critical for:

• risk reduction
• PD planning
• equitable implementation
• compliance with emerging state guidelines
• building districtwide AI strategies

Yet, most districts rely on surveys and self-assessments—tools that capture confidence, not capability.

The Problem: AI Literacy Is Not a Self-Reported Skill

Self-evaluations cannot answer questions like:

• Can a teacher spot harmful or hallucinated AI outputs?
• Can they ethically use AI with student data?
• Can they revise AI-generated content to meet grade-level standards?
• Can they detect bias in AI-produced materials?

Districts like Township High School District 211 have resorted to building their own assessment rubrics, highlighting a clear lack of validated tools.

The reality is simple: You can’t improve what you can’t measure.

The Four Competencies Districts Must Assess

A comprehensive AI literacy assessment should evaluate:

1. Recognizing AI

Does the teacher understand where and how AI appears across instructional and administrative tools?

2. Evaluating AI Outputs

Can they judge accuracy, quality, alignment, and potential bias in AI-generated content?

3. Ethical Navigation

Do they understand safe data handling, age-appropriate use, and risk mitigation?

4. Instructional Application

Can they apply AI effectively in real classroom scenarios?

These are the exact four dimensions Adapt AI measures through a research-backed, task-based assessment model built using ETS’s evidence-centered design.

Why Scenario‑Based Assessments Matter

To evaluate real teacher ability, districts need performance tasks such as:

• reviewing an AI-generated lesson plan for problems
• detecting hallucinations in a sample explanation
• identifying ethical concerns in teacher-student AI interactions
• revising an AI output to meet curricular goals

These tasks reveal competencies that surveys simply cannot.

What High-Quality AI Literacy Assessment Produces

The output of a research-based approach—like Adapt AI’s Skillprint dashboard—gives districts:

• individual and group profiles
• heatmaps of strengths and gaps
• risk indicators
• recommended PD pathways
• insights for policy and rollout planning

Districts can finally see who is ready, who needs support, and where risks exist, allowing targeted interventions.

Why This Matters Now

Districts in Mississippi, Massachusetts, Arizona, and others are launching AI literacy pilots focused specifically on teacher readiness—not student output. They need objective data, not assumptions.

As AI tools flood the market, a structured approach to measuring literacy becomes essential for:

• equitable implementation
• staff safety
• student protection
• sustainable long-term adoption

Bottom Line

A research-backed approach to AI literacy assessment is no longer optional—it’s foundational.

Districts that measure AI literacy now will lead safer, more successful AI implementation for years to come.