Michigan AI Comprehensive Guide for Districts

This policy document outlines a year-long, four-phase roadmap for Michigan school districts to integrate Artificial Intelligence responsibly. It emphasizes privacy compliance (FERPA/COPPA), teacher-led oversight, and the alignment of AI tools with existing state improvement processes.

# Artificial Intelligence (AI)

# Comprehensive Guide for Districts

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# Table of Contents

## Introduction

## Essential Practices to Consider

## Phase 0 (Pre-Launch, Weeks 0–6): Organize and Listen

## Phase 1 (Weeks 7–12): Guardrails, Policy, MICIP Alignment

## Phase 2 (Weeks 12–24): Classroom Practice, PD, Procurement

## Phase 3 (Weeks 24–36): Classroom AI Implementation, Community Engagement and Board Adoption

## Phase 4 (Weeks 37-52): Evaluate, Report, Iterate (MICIP Cycle)

## Potential AI Implementation Measures of Success

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# Introduction

This comprehensive guide provides Michigan school districts with structured administrative guidelines for implementing responsible AI practices across organizational roles, policy, and operations. It offers practical examples and templates to help district leaders establish clear guardrails of AI use in teaching and learning, and to integrate AI readiness for staff and students into Michigan Integrated Continuous Improvement Process (MICIP) cycles. By following these guidelines, administrators may help support safe, equitable, and effective AI use while maintaining educator oversight, protecting student privacy, and fostering transparency with stakeholders.

As AI continues to evolve, districts may find value in gradually developing the knowledge, systems, and safeguards that help support thoughtful and effective use of these tools. When integrated in ways that align with local priorities, AI has the potential to enhance instructional practices, streamline operations, and expand the capacity of staff to meet the diverse needs of students and the broader school community.

# Essential Practices to Consider

- **Keep AI Purposeful and Safe** Districts may consider using AI to support learning and operations in ways that align with district goals and educational priorities. - **Protect Privacy and Integrity** o Districts should be aware of and follow federal privacy and accessibility laws. - Family Educational Rights and Privacy Act (FERPA) - Follow federal requirements for protecting students’ education records. Useful resources include the U.S. Department of Education’s FERPA overview and technical assistance tools. - Children’s Online Privacy Protection Act (COPPA) - For online tools and applications used with children under 13, ensure parental consent and proper data collection practices, following the Federal Trade Commission's COPPA FAQs: COPPA compliance guide. - Accessibility-Meet digital accessibility requirements under Section 504 of the Rehabilitation Act of 1973 (29 U.S.C. § 794), Title II under the American With Disabilities Act of 1990, and Section 508 of the Rehabilitation Act of 1973, as amended (29 U.S.C. 794d). 29 U.S.C. 794d).

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- See the U. S. Department of Education’s guidance for making educational technology inclusive: Technology accessibility requirements. o Districts may encourage staff and students to document when and how AI tools are used (e.g., in assignments) to support academic integrity and transparency. - **Build AI Literacy for All** Districts may consider offering computer science concepts, such as algorithms, data, and computational thinking, that form the foundational knowledge needed to fully develop AI literacy. - **Maintain Human Oversight** AI can assist, but not replace, educators or decision-makers. Districts may choose to review tools for bias, accuracy, and appropriateness prior to adoption. - **Promote Equity and Accessibility** Districts may assess whether AI tools effectively support diverse learners and provide necessary accommodations to promote equitable access. - **Support Transparency and Continuous Improvement** Districts may communicate openly with stakeholders and regularly review AI practices through continuous improvement processes, such as MICIP cycles (needs assessment → plan → implement → monitor → evaluate).

# Phase 0 (Pre-Launch, Weeks 0–6): Organize and Listen

## Team Structure and Roles

Districts may consider:

- Designating a district AI lead. This might be the curriculum director, chief information officer, chief technology officer, or data privacy officer. - Creating a diverse AI working group. This should include teachers, admins, students/families, technology staff, and special education representatives. - Setting a communication cadence. This should include biweekly meetings and/or monthly updates to the board and/or cabinet.

## Landscape and Listening

Districts may consider:

- Running a baseline AI landscape scan (current staff use, student use, tools, and frequency of use). Examples include: o Project Tomorrow AI in K-12 Survey o AI Education Project (aiEDU) Student and Teacher Readiness Rubrics (download needed) o Michigan Virtual Teacher Readiness for Implementing Generative AI

- Using School and District Readiness Rubrics to self-assess strengths/gaps. Examples include: o aiEDU School and District Readiness Rubrics (download needed) o Consortium for School Networking (CoSN) K12 Gen AI Readiness Checklist

- Exploring tools for evaluating meaningful learning with technology resources. Examples include: o Triple E (Engage, Enhance, Extend) Framework o Technological, Pedagogical Content Knowledge (TPACK)

- Having the technology department inventory/register AI systems, owners, data, risk, etc. Examples include: o CoSN K-12 Gen AI Maturity Tool (download needed) o National Institute of Standards in Technology (NIST) AI Risk Management Framework (RMF) o Algorithmic Impact Assessment (AIA) Tool for moderate/high-risk use cases

- Exploring and evaluating a range of AI toolkits to find the best match for the district’s needs based upon the baseline AI landscape scan and surveys that were conducted. Examples include:

- Michigan Virtual Admin Guide to AI

- TeachAI Toolkit

- Common Sense Media AI Toolkit

# Suggested Deliverables (Phase 0)

District may consider creating:

- A document that identifies roles, meeting cadence, decision rights, etc. for the AI workgroup. - An AI inventory spreadsheet with rubric scores to identify use and gaps in familiarity with AI tools. - A draft update of the acceptable use policy (AUP) for staff and student use of AI.

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Phase 1 (Weeks 7–12): Guardrails, Policy, MICIP Alignment

Policy and Board Readiness Considerations

Districts may consider:

• Drafting a model board policy.

• Drafting a superintendent memo. To share with whom?

• Drafting a board resolution.

• Updating the staff AUP and the student AUP

• Adding human oversight clauses to relevant policies—no high-stakes decisions by AI without documented human review and appeal.

Compliance and Risk Controls

Districts may consider:

• Aligning organizational practices to NIST AI Risk Management Framework (RMF).

• Having an AI team review compliance with:

o FERPA

o COPPA

o Children’s Internet Protection Act (CIPA)

o Cybersecurity and Infrastructure Security Agency (CISA)/K12 Security Information Exchange (SIX) cybersecurity baseline.

MICIP Integration (Continuous Improvement)

Districts may consider:

• Adding AI readiness indicators and goals to MICIP needs assessment/plan.

• An updated AUP for staff and students.

Districts may consider creating:

Suggested Deliverables (Phase 1)

• Planning quarterly monitoring checkpoints aligned to MICIP monitoring.

• Entries in the district MICIP plan that include needs assessment, root cause, challenge statement, goals, interim and end targets, strategy, implementation,

monitoring, and funding alignment.

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# Phase 2 (Weeks 13–24): Classroom Practice, Professional Learning, Procurement

## Instructional Guardrails and Considerations

Districts may consider:

- Posting classroom AI norms (student disclosure, privacy, verification, fairness, age- appropriate use). Examples include: o Guidance on the Use of AI in Our Schools by TeachAI o Teacher Guide to Artificial Intelligence in Education by Michigan Virtual o Student Guide to Artificial Intelligence in Education by Michigan Virtual - Adding student and educator AI competency frameworks (grade-band progression K–5, 6–8, 9–12) to curriculum maps. Examples include: o AiEDU Student and Educator Readiness Competency Rubrics o International Society for Technology in Education Standards for AI in Education o AI4K12.org Grade Progression Charts o AI competency framework for teachers by UNESCO - Offering or developing academic integrity guidance when using AI-such as requesting process evidence (drafts, reflections, oral defenses). Examples include: o Guidance on the Use of AI in Our Schools by TeachAI o Academic integrity in the age of AI by Turnitin

## Professional Learning

Many education organizations offer professional learning on AI topics; however, an opportunity supported through MDE grant funds is available for media and AI literacy trainer cohorts. This program provides ready-to-use agendas and slide decks that trainers can bring back to their districts and use to support staff learning.