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K‑12 AI Policy & Implementation

This collection is a practical, district-facing toolkit for building and communicating responsible K-12 AI policy. It encompasses vendor acquisition and procurement vetting, public communication, equity and bias mitigation, decision-making and governance, protocols for classroom and operational use, and organizational readiness—each with its own activities, rubrics, and references to guide the transition from exploration to full implementation.

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The AI4MI Resource Collection supports school systems as they navigate the transition from exploration to implementation. It includes protocols for vendor acquisition, communication strategies, equity and bias mitigation, decision-making and governance, classroom and operational practices, and organizational readiness. Each section offers activities, rubrics, and references to support policy development.

Contributors:

Developed collaboratively by education leaders and researchers including Doug Wilson, Elaine Stiefel, Alena Zachery-Ross, Dwight Levens, Elizabeth Macklin, and Tovah Sheldon, synthesizing best practices from national and state education initiatives, academic studies, and policy frameworks.

Intended Audience:

School boards, superintendents, technology and data privacy officers, curriculum leaders, legal and procurement teams, administrators, teacher leaders, union representatives, students, families, and community partners.

🎯 Purpose and Scope

AI in education presents both opportunity and risk. This document guides districts in adopting policies that ensure safety, equity, transparency, and human-centered practices while advancing teaching, learning, and operational efficiency.

Key areas addressed include:

  • Vendor acquisition and procurement
  • AI communication and public engagement
  • Equitable access and bias mitigation
  • Ethical frameworks and governance
  • Protocols for classroom and operational use
  • Awareness, readiness, and implementation stages

🛒 Vendor Acquisition

Districts exploring AI for instruction, operations, and student support must adopt thoughtful vendor acquisition protocols. Unvetted tools can risk student data privacy, misalignment with learning goals, and wasted resources. Effective acquisition balances innovation with responsibility.

Key Considerations:

  • Comply with FERPA, COPPA, and state data laws.
  • Evaluate instructional alignment and equity impact.
  • Require transparency and explainability from vendors.
  • Ensure human oversight in AI-supported decisions.
  • Assess contract clarity, sustainability, and renewal terms.
  • Require vendor training and support for staff.
  • Use cross-functional evaluation teams (legal, IT, instructional).
  • Maintain a public list of approved AI tools.
  • Reassess AI tools annually for safety and effectiveness.

Pros:

  • Reduces educator workload and administrative tasks.
  • Enables adaptive, personalized learning.
  • Expands tutoring and translation access.
  • Improves data-driven decision-making.
  • Demonstrates district innovation.

Cons:

  • Privacy and ethics compliance risks.
  • Opaque “black box” AI models.
  • Possible algorithmic bias.
  • Cost and scalability challenges.
  • Resistance from untrained stakeholders.

Staff Activity – AI Vendor Vetting Workshop

Teams analyze mock vendor proposals for AI tools such as tutors or grading assistants. Each group reviews privacy terms, alignment, and use cases, then evaluates the proposal using a rubric to recommend approval, revision, or rejection.

Objectives:

  • Identify criteria for ethical and effective vendor selection.
  • Apply vetting processes collaboratively.
  • Discuss implications for instruction, equity, and privacy.
  • Establish shared decision-making language.

Simplified Rubric Summary:

  • Privacy & Compliance: Is it fully FERPA/COPPA compliant and secure?
  • Educational Alignment: Does it support curriculum and equity goals?
  • Transparency & Ethics: Are AI functions explainable and fair?
  • Usability & Support: Is it accessible with adequate training?
  • Sustainability & Cost: Are pricing and contract terms clear and scalable?

Examples of Safe AI Vendors for Schools

Below are vetted examples of AI tools that align with student privacy laws (FERPA/COPPA) and educational goals. These vendors demonstrate responsible AI development and implementation in K–12 contexts.

  • MagicSchool AI – Tools designed specifically for educators, including lesson planning, rubric creation, and feedback generation. Fully FERPA and COPPA compliant.
  • Curipod – Creates AI-generated interactive lessons and presentations aligned to learning standards. Teachers can customize and scaffold content easily.
  • Khanmigo by Khan Academy – AI-powered tutor integrated with Khan Academy content. Provides student tutoring and teacher support with a strong ethical AI focus.
  • Diffit – Generates leveled texts and comprehension questions from any source, supporting differentiated instruction and English language learners.
  • Perplexity.ai (Education Mode) – AI research assistant offering source-based answers and citations. The education mode promotes safe and transparent classroom use.
  • SchoolAI – Provides customizable, educator-controlled AI environments for schools, emphasizing safety, privacy, and instructional alignment.

💬 AI Communication

Clear, transparent communication about AI builds community trust. Districts must explain how AI is used, how data is protected, and how it aligns with learning goals. Messaging should be accessible, culturally inclusive, and regularly updated as technologies and laws evolve.

Key Considerations:

  • Define clearly where and how AI is used.
  • Keep messaging consistent district-wide.
  • Be transparent about data collection and storage.
  • Offer opportunities for families and staff to provide input.
  • Address equity, ethics, and accessibility.
  • Update communications as tools and policies change.
  • Train staff to speak knowledgeably about AI.

Pros:

  • Builds trust with families and staff.
  • Reduces misinformation.
  • Promotes ethical use and compliance.
  • Increases understanding of AI’s educational value.

Cons:

  • Requires continual updates.
  • May prompt difficult questions.
  • Adds to communication workload.

Staff Activity – Drafting an AI Communication Framework

Participants review communication models from other districts and co-create messaging for families, staff, and community.

Objectives:

  • Identify common questions and concerns.
  • Develop clear, values-aligned messages.
  • Practice transparent responses to stakeholders.
  • Draft an initial district AI messaging plan.

⚖️ Equity and Ethics in AI

Equitable access and ethical design are core to responsible AI use. Teachers and administrators must understand how to integrate AI purposefully while ensuring inclusion and accessibility for all learners.

Key Considerations:

  • Train educators on when, why, and how to use AI.
  • Use the Universal Design for Learning (UDL) framework: Engagement, Representation, and Action/Expression.
  • Leverage AI to provide flexible supports and accessibility tools.
  • Teach early AI literacy—how AI learns, impacts society, and solves problems.
  • Emphasize computational thinking: decomposition, pattern recognition, abstraction, and algorithms.
  • Ensure transparency, fairness, and accountability in all AI applications.
  • Educate students to be critical consumers and ethical creators.

Ethics and Bias Mitigation Focus:

  • Promote accuracy, integrity, and privacy.
  • Address fairness and justice in algorithms.
  • Recognize sources of bias such as skewed data or misused tools.
  • Encourage reflection on human oversight and lived experience.

Pros:

  • Helps identify patterns for better decision-making.
  • Improves accessibility for diverse learners.
  • Supports ethical digital citizenship.

Cons:

  • AI lacks true critical reasoning.
  • Misinformation or “hallucinations” may occur.

Staff Activity – Ethical Scenarios Carousel

Teams rotate through real-world AI classroom scenarios, identifying ethical risks and proposing mitigation strategies.

Objectives:

  • Recognize ethical and bias challenges.
  • Apply fairness and inclusion principles.
  • Build educator confidence with responsible AI use.

🧭 Frameworks and Decision-Making

AI guidelines and frameworks help districts integrate AI responsibly and sustainably. They ensure ethical, effective, and equitable practices while aligning AI with educational mission and human values.

Key Considerations:

  • Develop frameworks that support both innovation and accountability.
  • Promote ethical use and protect privacy.
  • Guarantee equitable access for all students.
  • Ensure technology supplements—not replaces—human interaction.
  • Train educators to enhance, not automate, teaching practices.
  • Address resource allocation, resistance, and professional learning.
  • Collaborate across districts and research partners.
  • Monitor effectiveness and equity continuously.

Pros:

  • Enhances data-informed decision-making.
  • Enables personalized learning.
  • Improves efficiency and predictive insight.
  • Strengthens teacher support and community engagement.

Cons:

  • Risk of bias or inequity.
  • Privacy and over-reliance concerns.
  • High implementation cost.
  • Complex integration and change management.

Staff Activity – CS Visions Reflection Adaptation

Adapt the “CS Visions” framework from CSforALL to reflect on core values guiding AI in education. Teams identify rationales, map values to design implications, and share vision statements.

Objectives:

  • Clarify team beliefs about AI’s role in learning.
  • Connect values to implementation choices.
  • Foster shared purpose and transparency.

📜 Policy Development and Review

AI policy must evolve as technology advances. Districts should create adaptable, living documents that uphold legal, ethical, and instructional priorities.

Key Considerations:

  • Establish continuous review cycles for all AI policies.
  • Engage diverse stakeholders in policy creation.
  • Balance innovation with risk management.
  • Align with ethical frameworks emphasizing fairness, transparency, and human oversight.
  • Ensure compliance with FERPA, COPPA, and state laws.
  • Avoid overreliance on AI detection tools due to inaccuracy.

Pros:

  • Keeps policy current and relevant.
  • Encourages proactive risk mitigation.
  • Builds stakeholder trust and ownership.
  • Prepares districts for future AI evolution.

Cons:

  • Resource-intensive maintenance.
  • Risk of over-regulation.
  • Monitoring enforcement challenges.
  • Potential bias in policy creation.

Staff Activity – AI Policy Review Workshop

Staff audit current or draft policies using ethical and governance principles. They analyze real-world AI case studies and identify strengths, gaps, and updates needed for future-readiness.

Simplified Rubric Summary:

  • Identify policy gaps and strengths.
  • Provide actionable recommendations.
  • Apply key principles (equity, transparency, adaptability).
  • Demonstrate collaborative consensus and clear rationale.

Discussion Prompts:

  • Should policy be detailed or broad with linked references?
  • Should guidelines be flexible “living documents” requiring no board vote for minor updates?

🧩 Protocols for Use

AI protocols define clear expectations for safe, ethical, and effective AI use in schools. They must prioritize privacy, academic integrity, and human-centered learning.

Key Areas:

  • Data Privacy: Limit student data collection; comply with laws; ensure consent and security.
  • Academic Integrity: Clarify plagiarism and citation expectations; emphasize critical thinking and learning purpose.
  • Professional Practice: Provide PD, lesson-planning support, and guidance on disclosure.
  • Student Literacy: Teach ethical and informed AI use.
  • Governance: Create a stakeholder committee; maintain living documents; assess risk.
  • Communication and Training: Offer ongoing staff and family education.

Pros:

  • Increases clarity and consistency.
  • Builds shared responsibility and trust.
  • Strengthens student safety and literacy.

Cons:

  • Time-intensive to maintain.
  • Requires ongoing enforcement.

Staff Activity – Building Classroom Protocols

Educators review model AI protocols and collaboratively create classroom or schoolwide guidelines that reflect district values.

Objectives:

  • Identify ethical use practices.
  • Draft concise, actionable classroom protocols.
  • Share models for district alignment.

🚀 Awareness, Readiness, and Action

Awareness and readiness determine how effectively districts can adopt AI. Intentional preparation prevents reactive, inequitable, or unsafe implementation.

Key Questions:

  • How aware are stakeholders of AI’s presence in schools?
  • What indicators show readiness for AI policy?
  • What conversations are needed before policy writing?
  • What should Boards and Superintendents consider before adoption?

Pros:

  • Promotes collaboration and shared ownership.
  • Builds innovation culture.
  • Protects students and staff through clarity.

Cons:

  • Overly specific policies may exclude contexts.
  • Lack of policy risks legal and ethical consequences.

Staff Activity – Awareness and Readiness Discussion

Facilitators lead exploratory conversations on district AI awareness, readiness, and policy implications. Small groups analyze guiding prompts and resources, then report key takeaways for next steps.

Objectives:

  • Increase understanding of district readiness.
  • Identify ethical and legal considerations.
  • Inform next steps toward task force or design sprint.

📚 References and Resources

Vendor Acquisition & Procurement

Communication

Frameworks, Governance, and Policy Development

Readiness and Broader Policy Context