Policies and Guidance

The policy library contains legislation, education-department guidance, strategic frameworks, and other formal materials concerning AI in education. The collection is organized first by jurisdiction and then by the source document.

Policy Themes

These tags identify the main policy mechanism or concern represented in a record. A policy may have more than one tag because, for example, a state framework can combine teacher training, student-privacy protections, and classroom-use rules.

Theme	What it means	Examples in this collection

mandatory-credit

Compare jurisdictions: Student instruction comparison

The policy requires students to receive AI-related instruction through a course, curriculum requirement, graduation credit, or defined learning standard. The requirement may cover AI directly or include it within computer science, digital literacy, or technology education.

Examples: Mississippi SB 2294 requires a high-school credit in computer science or a related CTE course with AI included in the learning expectations. Estonia’s digital-integration policy embeds AI and digital skills in formal curriculum development.

student-privacy

Compare jurisdictions: Student privacy comparison

The policy governs how student information may be collected, shared, stored, reviewed, or used to train or operate AI systems. It can include consent, data minimization, vendor contracts, security reviews, and protections for disability or language-support data.

Examples: California AB 1159 addresses restrictions on using covered student information to train generative-AI systems. NYCPS requires education technology to pass privacy and security review through its ERMA process.

teacher-training

Compare jurisdictions: Teacher training comparison

The policy expects educators, administrators, or other staff to build the knowledge needed to use AI safely and effectively. This can include professional development, implementation training, educator guidance, or a requirement that a trained teacher supervise student-facing AI.

Examples: Ontario’s guidance connects AI expectations with mandatory educator professional activity. NYCPS high-school pilots require direct supervision by a trained educator.

usage-guardrails

Compare jurisdictions: Classroom guardrails comparison

The policy sets boundaries for when, where, and how AI may be used, along with human-oversight, academic-integrity, accessibility, procurement, or accountability requirements. Guardrails can permit limited use, restrict particular applications, or prohibit student-facing AI for a grade band.

Examples: NYCPS prohibits student-facing generative AI in grades 2-K–8 while allowing limited supervised high-school pilots. Illinois SB 3735 requires human teacher review of certain AI-generated or automated scores.

research-taskforce

Compare jurisdictions: Research and task-force comparison

The policy creates a study, advisory group, pilot evaluation, reporting process, or formal review intended to inform later policy decisions. This tag signals that the document is partly about learning what works before expanding or revising implementation.

Examples: Oklahoma’s legislation establishes study and guidance functions for responsible technology in schools. NYCPS created a Technology in Schools Coalition to evaluate its moratorium and pilots and recommend future policy.

Coverage

Use the expandable jurisdiction sidebar to move directly to a location. The jurisdictions index groups all current coverage and points to the corresponding repository folders.

Source Records

Each policy record preserves a link to its primary official source and uses standardized metadata tags for status, themes, and governance focus.