District & School AI Policies

Exploring approaches to AI policy development in K-12 educational settings

Introduction

This resource explores how different K-12 schools are approaching AI policy development, highlighting different strategies and real-world examples from districts across the United States. As AI tools become more accessible, schools are finding various ways to address implementation, from comprehensive frameworks to targeted guidelines.

AI policies vary significantly across districts and should reflect local contexts, community values, and regulatory requirements. The examples below represent different approaches rather than recommended solutions.

Real School AI Policy Examples

The following examples showcase different approaches districts have taken, with links to actual policy documents:

Boston Public Schools

Boston Public Schools AI Guidance

District Size: 54,000+ students
Policy Released: August 2025
Document Link: BPS AI Guidance

• Five-pillar framework (Academic Excellence, Cybersecurity, Ethics, Bias Acknowledgment, Human Oversight)
• Approved tools list with vendor vetting process
• Grade-level restrictions

Notable Approach:

• Treats AI use as “case-by-case decision” by teachers based on learning goals
• Provides specific tool recommendations with procurement approval
• Includes AI Hub website with sample guidance and literacy lessons
• Student input incorporated into policy development

Chicago Public Schools

Chicago Public Schools AI Guidebook

District Size: 300,000+ students
Policy Released: July 2024
Document Link: CPS AI Guidebook PDF

• Elementary: AI for character interviews and word problems (supervised)
• Middle: Virtual experiments and research with oversight
• High School: Independent AI use for essay feedback and synthesis
• Phased implementation plan through 2025-2026

Implementation Features:

• Partnership with AI for Education for development
• Ed Tech Catalog system for approved tools
• Required citation of AI assistance in student work
• Age-based access restrictions

Peninsula School District (WA)

Peninsula School District AI Principles

District Size: ~9,000 students
Policy Released: July 15, 2023 (First in nation)
Document Link: Peninsula AI Principles

• “H-AI-H” framework (Human inquiry, AI assistance, Human reflection)
• Co-creation of classroom behavior expectations
• Process documentation over product focus
• Universal Design for Learning integration

Rural District Example

Taliaferro County Schools, Georgia

District Size: 177 students (K-12 in one building)
Policy Released: March 2024
Document Link: Taliaferro AI Policy

• Mandatory teacher supervision for all student AI use
• Three-strike disciplinary system for violations
• Detailed data privacy protections
• Annual policy review requirements

Unique Aspects:

• Despite small size, developed detailed policy framework
• Addresses high-poverty demographic considerations
• Emphasizes responsible use over prohibition
• Includes both student and educator guidelines

Suburban Example

Chappaqua Central School District, NY

District Size: ~4,000 students
Policy Adopted: August 29, 2024
Document Link: CCSD AI Policy 5110

• Age-appropriate AI tool access with privacy protection
• Transparent information about AI tool usage
• Recognition of AI bias and equity concerns
• Education Law 2-d compliance requirements

Policy Highlights:

• Emphasizes student entitlements to AI access and transparency
• Addresses copyright and data bias concerns explicitly
• Requires only approved, compliant tools for student use
• Developed following faculty AI learning sessions

Organizations for Creating Your Own AI Policy

Playlab does a lot of things, but at our core we are a learning and AI infrastructure organization. If your school or district is looking to develop its own AI policy, we recommend reaching out to the following organizations offer specialized support and consulting services:

aiEDU

Specialization: Comprehensive AI policy development and implementation support

Services Offered:

• • Custom AI policy development workshops
• • District-wide professional development programs
• • Policy framework templates and customization
• • Implementation planning and timeline development
• • Ongoing consultation during rollout phases

Throughline Learning

Specialization: Strategic planning and policy implementation for educational technology initiatives

Services Offered:

• • AI readiness assessments and strategic planning
• • Stakeholder engagement and community input processes
• • Policy development with equity and inclusion focus
• • Change management support for AI integration
• • Evaluation and continuous improvement frameworks

LEAP Innovations

Specialization: Innovation-focused policy development with emphasis on emerging technologies

Services Offered:

• • Future-focused AI policy design
• • Innovation labs and pilot program development
• • Cross-sector partnership facilitation
• • Research and evaluation of AI implementation outcomes
• • Leadership coaching for AI initiative champions

Considerations for Schools

Schools are addressing AI integration from multiple angles, with different districts prioritizing different things. Some considerations are shared below:

Academic Integrity Concerns

Navigating New Forms of Assessment

Districts are exploring various approaches to maintain academic standards:

• Some require disclosure of AI assistance in student work
• Others focus on redesigning assignments that are AI-resistant
• Many emphasize process documentation over final products
• Some districts allow AI for specific phases of learning

Emerging Practice: Some schools are asking students to submit their AI conversation logs alongside assignments to understand their thinking process.

Data Privacy & Safety

Addressing Student Information Protection

Schools are developing protocols around:

• Student data sharing with AI platforms
• Age restrictions on various AI tools
• FERPA and COPPA compliance considerations
• Parental consent requirements

Privacy Approaches

• Approved tool lists with vetted vendors
• Age-based access restrictions
• No-PII policies for AI interactions

Safety Measures

• Content filtering and monitoring
• Teacher supervision requirements
• Incident reporting protocols

Legal Compliance

Meeting Federal and State Requirements

Districts are navigating:

• FERPA: Educational record protection requirements
• COPPA: Children’s online privacy considerations
• State Privacy Laws: Varying jurisdictional requirements
• Accessibility: ADA compliance with AI tools

Compliance Approaches: Many districts are creating vendor vetting processes that include legal review of AI tools’ terms of service and data handling practices.

Equity & Access

Addressing Implementation Disparities

Schools are considering:

• Digital divide implications of AI requirements
• Differential access to AI tools at home vs. school
• Teacher preparation and comfort levels
• Student familiarity variations

Access Considerations

Some districts are providing AI literacy training to address preparation gaps, while others are designing policies that don’t require home AI access.

Contextual Factors Influencing Policy Development

Districts are finding that local context significantly shapes their AI policy approach:

Community and Regional Variations

Different educational environments are producing notably different policies:

Urban vs. Suburban vs. Rural

Large urban districts often focus on equity and access, suburban districts may emphasize academic integrity, while rural districts might prioritize resource efficiency.

Technology Infrastructure

Districts with robust 1:1 device programs approach AI differently than those with limited technology access.

Community Attitudes

Some communities embrace AI integration while others express concerns about academic authenticity.

Staff Readiness

Teacher comfort levels and training needs vary significantly across districts.

State and Local Regulatory Environment

Guidance Variation: State departments of education have released varying levels of AI guidance, with some providing detailed frameworks and others leaving decisions to local districts.

Common Elements in Effective Policies

Reviewing multiple district policies reveals several recurring themes:

1. Clear Purpose Statements: Most successful policies begin by articulating why AI integration matters for their specific community
2. Graduated Implementation: Many districts use phased or grade-level approaches rather than district-wide simultaneous rollout
3. Professional Learning Integration: Policies that include training requirements tend to have better implementation outcomes
4. Regular Review Cycles: Successful policies include built-in revision schedules (quarterly to annually)

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Policy examples current as of September 2025.