Readiness Self-Assessment

This tool guides districts through a structured self-evaluation, helping them:

• Assess their maturity in Generative AI implementation
• Identify focus areas for growth and investment
• Align AI use with instructional and operational goals
• Build upon foundational steps introduced in the DPI AI Guidance & Roadmap (see planning checklist)

Tip: Pair the maturity tool with a short stakeholder survey (students, families, educators) to validate priorities before setting timelines.

• Organizes readiness into seven major domains with clearly defined subdomains
• Uses a three-level maturity rubric—Emerging, Developing, and Mature—to guide self-assessment and progression

Rubric snapshot (examples)

• Emerging: Low awareness and limited infrastructure; early experimentation with minimal planning or evaluation.
• Developing: Growing awareness, increasing investment, evolving governance and training; AI use is becoming regular.
• Mature: Deep AI expertise, full integration into educational and operational processes; dynamic policies and continuous evaluation in place.

Note: “Mature” ≠ “finished.” Districts revisit policies, equity reviews, and training as tools and laws evolve.

Evaluates leadership’s ability to develop and sustain strategic AI initiatives. Areas include:

• Vision and alignment with district goals
• Policy and legislative awareness
• Equity, ethics, and oversight responsibilities

Examples

• Adopt a board-approved AI vision statement and success metrics tied to the Strategic Plan.
• Convene a cross-functional AI Steering Committee (curriculum, IT, SPED, ELL, legal, HR, library/media, family engagement).
• Publish a one-page “What AI Means for Our District” for families (plain language, multiple languages).

Helpful links

• Blueprint for an AI Bill of Rights
• ISTE Standards (International Society for Technology in Education)

Focuses on organizational structures and staffing needed for effective Gen AI implementation:

• Staffing models and capacity building
• Procurement processes for AI tools
• Cross-departmental coordination

Examples

• Designate product owners for each AI tool; keep a living AI Tool Registry with purpose, data flows, and renewal dates.
• Use RFP (Request for Proposal) prompts that require a VPAT/ACR (Voluntary Product Accessibility Template / Accessibility Conformance Report) and NDPA (National Data Privacy Agreement).
• Set a 90-day pilot → feedback → approval/retire decision cycle.

Helpful links

• VPAT / ACR overview (Section 508)
• WCAG (Web Content Accessibility Guidelines) 2.1 Level AA primer

Assesses systems for managing data with integrity and compliance:

• Data governance, quality, and privacy protocols
• IAM (Identity and Access Management) & SSO (Single Sign-On)
• Security safeguards and system monitoring

Examples

• Implement role-based access and periodic access reviews for AI tools.
• Adopt data minimization: redact PII (Personally Identifiable Information) in prompts and uploads.
• Create a Data Catalog with owners, retention, and lawful basis for processing.

Helpful links

• FERPA (Family Educational Rights and Privacy Act)
• PPRA (Protection of Pupil Rights Amendment)

Ensures infrastructure supports Gen AI with reliability and accountability:

• Technology architecture and system compatibility
• Monitoring and response systems for misuse
• Management of “hallucinations” (confident but incorrect AI output) or inappropriate content

Examples

• Enable content filtering, logging, and safe-search for AI features in browsers and LMS (Learning Management System).
• Adopt a ticketing tag (e.g., “AI-Issue”) to track patterns and remediation steps.
• Pilot on-prem / private-tenant AI where needed for sensitive workflows.

Helpful links

• CIS Controls (Center for Internet Security)
• NIST CSF (National Institute of Standards and Technology Cybersecurity Framework)

Evaluates practices that protect systems and users:

• Cybersecurity frameworks and policies
• Ongoing staff training and security awareness
• Incident response and threat mitigation planning

Examples

• Require MFA (Multi-Factor Authentication) for staff/admin roles on AI tools.
• Run quarterly phishing simulations and AI-prompt safety training (do not paste PII).
• Maintain an IRP (Incident Response Plan) with 24/72-hour comms templates.

Helpful links

• CISA (Cybersecurity & Infrastructure Security Agency) resources

Prepares districts to manage legal obligations and risk exposure:

• Risk assessments and mitigation strategies
• Compliance with legal standards
• Protocols for loss notification and remediation

Examples

• Use DPIA (Data Protection Impact Assessment) or PIA (Privacy Impact Assessment) for new AI tools.
• Ensure contracts include a DPA (Data Processing Agreement), data-deletion timelines, and breach terms.
• Publish a plain-language AI FAQ for families, with translation options.

Helpful links

• SDPC (Student Data Privacy Consortium) – NDPA
• COPPA (Children’s Online Privacy Protection Act)

Supports AI integration into teaching and learning:

• Curriculum design and instructional materials
• Teacher professional development and support
• Ethical use and evaluation of AI in academic settings
• Accessibility, equity, and automation for instructional improvement

Examples

• Map AI skills to ELA/Science/Social Studies standards; add “Human → AI → Human” checkpoints (student drafts → AI feedback → student revision).
• Create brief disclosure prompts students paste at the end of work (tool, purpose, what they kept/changed, verification steps).
• Ensure WCAG-aligned materials (captions, alt text, plain-language summaries) for AI-assisted lessons.

Helpful links

• Common Sense: AI Literacy Lessons (6–12)
• WCAG 2.1 Level AA overview