- Framework Version: 1.0
- Last Updated: [Date]
- District Customization: [District Name]
- Approved By: [Governance Body]
- Effective Date: [Implementation Date]
- Framework Overview
- Core Principles
- Governance Structure
- Stakeholder Roles and Responsibilities
- AI Tool Evaluation Process
- Student Privacy and Data Protection
- Academic Integrity Standards
- Equity and Bias Prevention
- Professional Development Requirements
- Student AI Literacy Standards
- Acceptable Use Guidelines
- Risk Management and Incident Response
- Implementation Timeline
- Monitoring and Assessment
- Community Engagement
This framework establishes guidelines for the responsible, ethical, and effective use of Artificial Intelligence (AI) technologies in [District Name]. Our goal is to harness AI’s potential to enhance educational outcomes while protecting student privacy, promoting equity, and maintaining academic integrity.
This framework applies to:
- All AI-powered tools and applications used in district operations
- Educational software with AI components
- Administrative systems utilizing AI
- Student-facing AI applications
- Staff use of AI for professional purposes
[This section guides districts on adapting the framework]
- Replace bracketed placeholders with district-specific information
- Modify policies to align with local laws and regulations
- Adjust timelines based on district capacity and resources
- Add or remove sections based on district needs
AI implementation must demonstrably improve learning outcomes and educational experiences for all students.
AI serves to augment, not replace, human judgment, creativity, and relationship-building in education.
AI tools must promote equitable access to education and avoid perpetuating or amplifying existing biases.
Student privacy and data protection are fundamental considerations in all AI implementations.
AI use will be transparent to stakeholders with clear accountability measures and oversight.
AI implementation must support, not undermine, authentic learning and assessment.
The district commits to ongoing evaluation and improvement of AI practices.
Composition:
- Superintendent or designee (Chair)
- Curriculum Director
- Technology Director
- Data Privacy Officer
- Teacher representatives (2)
- Parent representative
- Student representative (high school)
- Community member
- Legal counsel (advisory)
Responsibilities:
- Policy development and updates
- Tool approval and evaluation
- Incident response oversight
- Community engagement coordination
- Annual framework review
Meeting Schedule: [Quarterly/Monthly as needed]
- Proposal Submission: Any stakeholder may propose AI initiatives
- Initial Review: Technical and educational assessment
- Committee Evaluation: Full committee review using established criteria
- Community Input: Public comment period for significant changes
- Final Approval: Formal committee vote and documentation
- Superintendent: Framework oversight and community communication
- Board of Education: Policy approval and resource allocation
- Curriculum Directors: Educational alignment and standards compliance
- Technology Director: Technical implementation and security oversight
- Principals: Building-level implementation and staff support
- Assistant Principals: Daily operations and incident response
- Department Heads: Subject-area guidance and training coordination
- Teachers: Classroom implementation and student guidance
- Librarians: Information literacy and research support
- Counselors: Student support and digital citizenship education
- Support Staff: Appropriate use within job responsibilities
- Rights: Access to AI-enhanced learning opportunities
- Responsibilities: Ethical use and academic integrity compliance
- Reporting: Obligation to report misuse or concerns
- Information Access: Right to understand AI use affecting their children
- Consent: Input on data sharing and AI tool usage
- Feedback: Participation in evaluation and improvement processes
- Security and privacy compliance assessment
- Data handling and storage review
- Integration capability with existing systems
- Reliability and performance testing
- Accessibility compliance (WCAG 2.1 AA)
- Alignment with curriculum standards
- Pedagogical value assessment
- Age-appropriateness review
- Learning outcome potential
- Teacher training requirements
- Bias detection and testing
- Equity impact analysis
- Academic integrity considerations
- Transparency and explainability
- Vendor ethical practices review
- Initial Request: Staff submits tool proposal with justification
- Technical Review: IT department assessment (5 business days)
- Educational Review: Curriculum team evaluation (10 business days)
- Pilot Testing: Limited implementation with feedback collection (30 days)
- Committee Review: Full oversight committee evaluation
- Final Decision: Approval, conditional approval, or rejection with rationale
- Quarterly usage and effectiveness reviews
- Annual comprehensive evaluation
- Incident tracking and response
- User feedback collection and analysis
- Collect only data necessary for educational purposes
- Limit access to authorized personnel only
- Implement automatic data deletion schedules
- Require explicit consent for non-essential data collection
- All AI vendors must sign FERPA-compliant agreements
- Student data cannot be used for commercial purposes
- Parents retain rights to access and correct student data
- Directory information policies apply to AI-generated insights
Mandatory Provisions:
- Data processing agreements compliant with applicable privacy laws
- Regular security audits and certifications
- Breach notification procedures
- Data portability and deletion capabilities
- Transparency reports on data usage
Prohibited Practices:
- Sale of student data to third parties
- Use of student data for advertising or commercial purposes
- Retention of data beyond contractual necessity
- Cross-contamination between educational and commercial data sets
- Right to know what data is collected and how it’s used
- Right to access their data and AI-generated insights
- Right to request correction of inaccurate data
- Right to request deletion of data when legally permissible
- Research assistance and source identification
- Grammar and writing mechanics support
- Brainstorming and idea generation
- Language translation assistance
- Accessibility accommodations
- Study guide creation
Students must disclose AI assistance when:
- AI generates substantial content in assignments
- AI tools influence core arguments or analysis
- AI assists with problem-solving in assessments
- AI provides research insights or data analysis
Format Example: “This assignment was completed with assistance from [AI Tool Name] for [specific purpose, e.g., grammar checking, research suggestions]. The core ideas and analysis remain my original work.”
- Complete assignment generation without substantial student input
- Circumventing learning objectives through AI shortcuts
- Submitting AI-generated work as original student creation
- Using AI during assessments unless explicitly permitted
- Sharing AI-generated work without proper attribution
Traditional Assessments:
- Open-book AI policies for specific assignments
- Process-focused evaluation rather than product-only
- Collaborative assignments incorporating AI literacy
- Real-time demonstration of understanding
AI-Resistant Assessment Strategies:
- In-class handwritten assignments
- Oral presentations and discussions
- Project-based learning with documented process
- Peer collaboration and group work
- Experiential and hands-on learning activities
- First Offense: Educational conversation and resubmission opportunity
- Second Offense: Parent conference and academic integrity education
- Repeated Violations: Formal disciplinary action per district policy
- Severe Cases: Administrative review and potential course impact
- Pre-implementation bias testing with diverse data sets
- Regular algorithmic auditing by third-party evaluators
- Community feedback mechanisms for bias reporting
- Intersectional analysis considering multiple demographic factors
Accessibility Standards:
- Screen reader compatibility
- Multiple language support
- Culturally responsive content
- Varied learning style accommodations
- Economic accessibility considerations
Representation Monitoring:
- Demographic data analysis of AI recommendations
- Cultural sensitivity review of AI-generated content
- Regular assessment of differential impacts
- Community input on cultural appropriateness
- Diverse training data requirements for AI vendors
- Human oversight for high-stakes decisions
- Alternative pathways for students affected by bias
- Regular staff training on bias recognition and response
Annual Review Questions:
- Are all student populations benefiting equally from AI tools?
- Do AI recommendations reflect and respect cultural diversity?
- Are there patterns of differential impact based on demographics?
- How are we addressing identified equity gaps?
All staff working with AI tools must demonstrate proficiency in:
- Basic AI literacy and functionality understanding
- Ethical decision-making frameworks
- Student privacy protection protocols
- Academic integrity guidelines
- Bias recognition and mitigation
- Emergency response procedures
Initial Implementation:
- Month 1: AI Literacy Foundations (4 hours)
- Month 2: Policy Deep Dive (3 hours)
- Month 3: Hands-On Tool Training (6 hours)
- Month 4: Ethical Scenarios Workshop (2 hours)
Ongoing Requirements:
- Annual policy update training (2 hours)
- New tool orientation as needed (1-3 hours per tool)
- Quarterly discussion groups (1 hour)
- Optional advanced workshops (available monthly)
- Self-paced online modules
- Interactive workshop sessions
- Peer mentoring programs
- Expert guest presentations
- Hands-on practice labs
- Case study discussions
- Completion certificates for all required training
- Competency demonstrations for advanced tools
- Peer observation and feedback systems
- Self-reflection and goal-setting exercises
Foundational Concepts:
- What is artificial intelligence?
- How do computers learn and make decisions?
- When do we encounter AI in daily life?
- Basic digital citizenship with AI tools
Learning Objectives:
- Identify AI in familiar contexts (voice assistants, recommendations)
- Understand that computers need human guidance
- Practice asking good questions to get helpful answers
- Recognize when information might be inaccurate
Expanding Understanding:
- How AI systems are trained and make predictions
- Bias in AI and its real-world impacts
- Privacy considerations with AI tools
- Appropriate academic use of AI assistance
Learning Objectives:
- Explain how AI learns from data and examples
- Identify potential bias in AI recommendations
- Make informed decisions about data sharing
- Use AI tools appropriately for learning enhancement
Advanced Applications:
- Algorithmic decision-making and societal implications
- AI’s role in various career fields
- Ethical considerations in AI development
- Critical evaluation of AI-generated content
Learning Objectives:
- Analyze the societal impact of AI systems
- Understand career implications of AI advancement
- Evaluate sources and validity of AI-generated information
- Contribute to discussions about AI ethics and policy
- Cross-curricular AI literacy projects
- Subject-specific AI tool applications
- Student-led AI ethics discussions
- Community AI impact research projects
Approved Uses:
- Personalized learning recommendations
- Automated feedback on student work
- Language translation for multilingual learners
- Accessibility accommodations and support
- Content creation assistance for teachers
- Assessment data analysis and insights
Approval Required:
- New AI-powered educational software
- Student data analysis beyond basic metrics
- AI-generated content for direct instruction
- Experimental or pilot AI applications
Approved Uses:
- Scheduling optimization
- Resource allocation analysis
- Communication drafting assistance
- Data reporting and visualization
- Professional development recommendations
Restricted Uses:
- Student discipline decision-making
- Staff evaluation primary criteria
- Budget allocation determinations
- High-stakes testing accommodations
Students may use approved AI tools for:
- Study guide creation and organization
- Practice problem generation
- Concept explanation and clarification
- Research topic exploration
- Writing brainstorming and planning
Disclosure Obligations:
- Document AI assistance in assignments
- Specify which AI tools were used and how
- Explain the extent of AI contribution
- Maintain original thinking and analysis
Quality Standards:
- Verify accuracy of AI-generated information
- Cross-reference sources and citations
- Ensure work meets assignment objectives
- Demonstrate personal understanding and learning
Encouraged Applications:
- Lesson plan development assistance
- Assessment creation and rubric development
- Differentiation strategy suggestions
- Parent communication drafting
- Professional learning resource discovery
Professional Standards:
- Maintain student confidentiality in AI interactions
- Verify accuracy of AI-generated educational content
- Apply professional judgment to AI recommendations
- Seek human collaboration for complex decisions
Potential Issues:
- Unauthorized data access or breaches
- Inappropriate data sharing with third parties
- Student information exposed through AI interactions
- Vendor security vulnerabilities
Mitigation Strategies:
- Regular security audits and penetration testing
- Vendor security requirement compliance
- Staff training on data protection protocols
- Incident response plan activation procedures
Potential Issues:
- Undetected plagiarism through AI assistance
- Academic dishonesty normalization
- Unfair advantages for some students
- Assessment validity compromise
Mitigation Strategies:
- Clear policy communication and training
- Modified assessment strategies
- Detection tool implementation
- Regular policy effectiveness review
Potential Issues:
- Discriminatory AI recommendations or decisions
- Unequal access to AI-enhanced learning
- Cultural insensitivity in AI-generated content
- Perpetuation of existing educational inequities
Mitigation Strategies:
- Diverse stakeholder input in tool selection
- Regular bias testing and community feedback
- Alternative pathways for affected students
- Ongoing equity impact monitoring
- Incident Identification: Staff member identifies potential AI-related issue
- Initial Assessment: Determine severity and scope of impact
- Containment: Limit further exposure or damage
- Notification: Alert appropriate administrators and oversight committee
- Documentation: Record incident details and initial response actions
- Detailed Analysis: Comprehensive review of incident circumstances
- Stakeholder Communication: Inform affected parties as appropriate
- Vendor Coordination: Engage AI tool providers if necessary
- Impact Assessment: Evaluate educational and privacy implications
- Corrective Actions: Implement immediate fixes and improvements
- Final Report: Document findings, actions taken, and lessons learned
- Policy Updates: Revise procedures based on incident insights
- Community Communication: Share appropriate information with stakeholders
- Training Updates: Modify professional development based on needs identified
- Monitoring Enhancement: Strengthen systems to prevent similar incidents
- AI Oversight Committee Chair: [Contact Information]
- Technology Director: [Contact Information]
- Data Privacy Officer: [Contact Information]
- Legal Counsel: [Contact Information]
- Vendor Support Contacts: [Maintain current list]
Objectives: Establish governance, policies, and initial training
Key Activities:
- Form AI Oversight Committee
- Conduct community stakeholder meetings
- Finalize policy customization for district
- Begin core staff training program
- Evaluate and approve initial AI tools
Success Metrics:
- Committee fully operational
- 100% core staff complete foundational training
- 3-5 AI tools approved for pilot implementation
- Community engagement sessions completed
Objectives: Test approved tools and refine procedures
Key Activities:
- Launch pilot programs in select schools/classrooms
- Implement student AI literacy curriculum
- Establish data collection and monitoring systems
- Conduct ongoing training and support
- Gather feedback from all stakeholder groups
Success Metrics:
- Pilot programs running smoothly in [X]% of targeted sites
- Initial student AI literacy assessments completed
- Regular feedback collection and analysis
- No major policy violations or security incidents
Objectives: Expand successful implementations district-wide
Key Activities:
- Roll out approved tools to all applicable schools
- Complete district-wide AI literacy training
- Implement full monitoring and assessment protocols
- Establish ongoing professional development programs
- Conduct first annual comprehensive review
Success Metrics:
- [X]% of schools actively using approved AI tools
- All staff completed required training and certification
- Student AI literacy benchmarks met
- Community satisfaction with AI implementation
Objectives: Continuous improvement and innovation
Key Activities:
- Regular policy updates based on experience and technology changes
- Advanced training opportunities and specialization
- Research partnerships and innovation pilots
- Community of practice development
- Annual comprehensive framework review
Success Metrics:
- Measurable improvements in educational outcomes
- High stakeholder satisfaction ratings
- Successful integration of new AI technologies
- Recognition as model implementation
- Student learning outcome improvements
- Teacher efficiency and satisfaction measures
- Assignment quality and authentic learning indicators
- Skills development in AI literacy and critical thinking
- Training completion rates and assessment scores
- Policy violation frequency and severity
- Incident response effectiveness
- Stakeholder awareness and understanding levels
- Demographic analysis of AI tool usage and benefits
- Bias incident reporting and resolution
- Achievement gap monitoring in AI-enhanced learning
- Community satisfaction across diverse populations
- Usage analytics from AI tools and platforms
- Academic performance data analysis
- Training completion and assessment scores
- Incident frequency and response time metrics
- Survey responses with Likert scale measurements
- Focus groups with students, teachers, and families
- Open-ended survey responses and comment analysis
- Case studies of successful and challenging implementations
- Peer observations and professional learning community discussions
- Community forum feedback and suggestions
- Basic usage statistics and incident summaries
- Training progress and completion rates
- Immediate issues requiring attention
- Success stories and best practice identification
- Comprehensive data analysis and trend identification
- Stakeholder feedback summary and response plans
- Policy effectiveness assessment
- Budget and resource utilization review
- Complete framework evaluation and recommendations
- Community presentation of outcomes and improvements
- Strategic planning for following year
- Public transparency report for broader community
- Data Collection: Ongoing gathering of quantitative and qualitative information
- Analysis: Regular review of trends, patterns, and outcomes
- Stakeholder Input: Structured feedback collection from all groups
- Recommendation Development: Evidence-based suggestions for improvements
- Implementation: Systematic rollout of approved changes
- Evaluation: Assessment of improvement effectiveness
- Monthly newsletter articles on AI implementation progress
- Quarterly community forums for questions and feedback
- Annual presentation to school board and community
- Website dedicated to AI transparency and resources
- Online portal for questions, concerns, and suggestions
- Regular surveys of students, families, and staff
- Open office hours with AI oversight committee members
- Community advisory group participation opportunities
- Complete framework document available online
- Regular reporting on AI tool usage and outcomes
- Clear explanation of data collection and usage practices
- Open meeting policies for AI oversight committee
- Public comment periods for major policy changes
- Transparent criteria for AI tool evaluation and approval
- Community input integration in framework updates
- Clear appeal processes for stakeholder concerns
- AI literacy workshops for parents and guardians
- Resources for supporting student AI learning at home
- Regular communication about classroom AI applications
- Guidance for family discussions about AI ethics
- Collaboration with local businesses using AI technologies
- Partnerships with higher education institutions
- Connection with community organizations and libraries
- Engagement with local government and civic groups
This framework will be comprehensively reviewed annually, with minor updates as needed throughout the year based on:
- Technology developments and new AI capabilities
- Changes in federal or state regulations
- Community feedback and stakeholder input
- Educational research and best practice evolution
- Incident learnings and policy effectiveness data
- Trigger Identification: Determine need for framework revision
- Stakeholder Consultation: Gather input from all affected groups
- Draft Development: Create proposed changes with rationale
- Community Review: Public comment period and feedback integration
- Committee Approval: Final review and approval by oversight committee
- Implementation Planning: Develop rollout strategy for changes
- Communication: Inform all stakeholders of updates and implications
- All framework versions maintained with clear change documentation
- Implementation dates tracked for all major revisions
- Historical decision rationale preserved for future reference
- Regular archiving of supporting documents and communications
[Detailed scoring criteria for technical, educational, and ethical assessment]
[Links to professional development content, student curriculum, and family resources]
[Relevant federal, state, and local laws and regulations]
[Standard contract language for AI tool procurement]
[Templates for documenting and responding to AI-related issues]
[Sample letters, presentations, and FAQ documents]
- Replace all bracketed placeholders with district-specific information
- Review and modify policies to align with local laws and regulations
- Adjust timelines and requirements based on district capacity
- Add district-specific policies or remove sections as appropriate
- Obtain legal review of customized framework
- Engage stakeholders in review and feedback process
- Secure formal approval from appropriate governance body
- Develop implementation plan and communication strategy
For questions about this framework template or to contribute to the open-source project:
- Project Repository: [GitHub URL]
- Discussion Forum: [Community Platform URL]
- Project Lead: [Contact Information]
- Contributing Guidelines: [Link to contribution documentation]
This framework is released under [License Type] and may be freely used, modified, and distributed by educational institutions. Attribution to the original project is appreciated but not required.
Framework Version: 1.0
Template Release Date: [Date]
Last Updated: [Date]