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I am looking for a way to understand what the goal posts are around AI Readiness and how to think about what success looks like.

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AI Readiness Framework

Explore our research-based framework defining what students, educators, and leaders need to know — with rubrics for all four audiences.

I am interested in professional learning for educators.

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Professional Learning

Browse our online PD series, Trailblazers fellowship, and school-based coaching options for educators at all levels.

I am looking for ways to increase the capacity of my district to deliver on AI Readiness for all students.

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Strategic Advisory Services

Work with aiEDU to build system-level capacity for AI Readiness — from needs assessment to leadership development to strategic planning.

I am looking for resources for classrooms to build AI Readiness.

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Curricular Resources

Explore our free, standards-aligned curriculum — AI Snapshots, elementary explorations, and AP CSP projects for grades K–12.

AI Readiness Framework v2.0

What students, educators, and leaders need to know

aiEDU's AI Readiness Framework provides research-based competencies and rubrics for four key audiences. Each layer supports the next — districts set conditions, school leaders set the instructional environment, educators build skills, and students achieve readiness.

View & Download Full Framework

This framework has four interconnected parts

All four layers work together with student learning at the center.

Student AI Literacy & Readiness Competencies

Concrete skills for building AI literacy across K–12, all subject areas

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Educator AI Literacy & Readiness Competencies

Knowledge and skills educators need to teach and model AI readiness

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School AI Readiness Rubric

Guidance for school leaders on implementation within their school

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District AI Readiness Rubric

Guidance for district leaders on building AI-ready systems districtwide

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Domain 1: Know Your Basics

AI Literacy · 3 competencies

A. Define & Identify AI Systems

K–5

Use age-appropriate definitions of AI (e.g., AI gathers information and makes predictions from that information)

Describe that AI gathers data and follows instructions
Identify personally identifiable data and why it matters

6–8

Refine definitions using more specific terms; identify and describe machine learning

Identify what datasets were used to train AI models
Know how personal information is collected and shared by AI tools
Build understanding of how AI systems use energy and space

9–12

Use nuanced definitions of AI; take care not to assign human traits to AI

Identify supervised vs. unsupervised learning, reasoning models
Describe differences in models, their strengths and weaknesses, and best use cases
Consider costs and benefits of AI to individuals, society, and the environment

B. Use AI Tools Safely and Effectively

K–5

Recognize and ask questions about AI and non-AI in familiar technologies
Describe responsible use of AI and non-AI tools

6–8

Use and critically compare outputs of age-appropriate AI tools
Evaluate when and how AI tools should be used; consider energy, bias, and access
Develop metacognitive skills with AI use to describe levels of thinking and creativity

9–12

Evaluate AI tool use in larger technology ecosystems
Responsibly engage in creation or sharing of AI-enabled products, including ethical citation
Act as a decision-maker around AI tool use, not just a consumer

C. Develop Core Content Knowledge

Develop grade-level appropriate literacy, math, science, and social studies skills, aligned to relevant national and/or state standards — across all grade bands.

Domain 2: Be a Critical Thinker

AI Literacy · 3 competencies

A. Determine Responsible Use of AI

K–5

Compare strengths and weaknesses of different tools and processes for problem solving, including AI tools

6–8

When presented with a novel problem, critically examine the possible use of AI tools, considering energy use, bias, and access

9–12

Critically assess the appropriateness of using AI for novel problems, including analyzing ethical implications in various contexts

B. Identify and Address AI Biases

K–5

Identify why outputs from AI tools have discrepancies
Define bias in data; connect discrepancies to ideas of fairness
Gather and sort data, noticing patterns and outliers

6–8

Discuss how AI systems might reflect biases of their creators or training data
Identify benefits and drawbacks of AI tools for a job, considering who might be left out
Choose appropriate data for AI tasks, considering potential biases

9–12

Critique AI systems for embedded biases and propose ways to make them more inclusive
Engage in ethical impact assessments of AI tools
Identify technical and policy solutions that could improve equity of tools

C. Examine AI Use and Outputs

K–5

Ask and answer questions about products created by AI and non-AI tools
Determine if AI outputs are factual or not

6–8

Assess the reliability and limitations of AI outputs in various scenarios
Compare different tools for a given problem; identify benefits and drawbacks
Compare multiple perspectives and sources before finalizing a response using an AI tool

9–12

Critically assess AI outputs, considering potential biases and limitations
Evaluate outputs for appropriate tone, audience, and content
Analyze and synthesize multiple perspectives to support lateral reading

Domain 3: Lead with the Human Advantage

AI Readiness · 3 competencies

A. Build Emotional Intelligence

K–5

Build foundations of uniquely human emotional intelligence: collaboration, empathy, compassion, self-regulation, and active listening

6–8

Deepen these skills applied to various social situations; engage in more robust empathy-building exercises like human-centered design

9–12

Apply human-centered skills to social and academic situations; engage in collaborative work for complex problems requiring implementation of solutions

B. Apply Creativity and Interdisciplinary Thinking

K–5

Ask "what if," "what else," "why," or "why not" questions that foster flexible thinking
Identify points of connection across subject areas

6–8

Apply brainstorming methods that go beyond sequential approaches
Ask questions that consider multiple subjects when creating or problem-solving

9–12

Apply creative and interdisciplinary thinking to novel situations
Develop creative approaches to complex problems requiring human reflection throughout
Build cross-content connections that leverage human expertise

C. Be a Life-Long Learner

K–5

Identify roadblocks and ask questions about how to get around them
Make adjustments to planned approaches when new information is presented

6–8

Approach novel situations with an orientation to context and knowledge-building
Iterate on possible solutions to refine them
Seek support from others when faced with an unknown

9–12

Reflect on approaches to novel situations that refine knowledge
Embrace productive struggle; preserve through novel problems
Continue to seek new knowledge and experience

Coming Soon

Student Curriculum Examples

We'll embed sample student activities — including AI Snapshots and Learning Journeys — aligned to each competency so partners can explore before committing.

Domain 1: Know and Model the Basics

Competency	What Educators Know and Do
A. Build Foundational Knowledge of AI	Define what AI is, how data is used in AI, and how AI is situated within the broader tech ecosystem Effectively use AI tools Understand the impacts of AI on human skill development and cognition Understand the history of AI development and implications for the future
B. Build Pedagogical Knowledge of AI Literacy & Readiness Skills	Build learning experiences that center human skill and learning — using AI as a tool when applicable, not a replacement for human thinking Identify the uniquely human skills students need: empathy, collaboration, creativity, resilience, adaptability Identify connections between core subject content and AI literacy skills Explain AI in age-appropriate ways and share best practices with students
C. Identify, Describe, and Apply District AI Policies	Situate safe and responsible individual and classroom AI use within district AI use policy Understand and apply district policies on AI citations and plagiarism in student work Understand and apply policies on collection, storage, and use of student data within AI tools per FERPA Implement family communication and engagement per district policy

Domain 2: Foster and Model Critical Thinking

Competency	What Educators Know and Do
A. Critically Evaluate AI Tools in Teaching & Learning	Critically evaluate if AI tools are best for a given instructional task using three criteria: Purpose — impact on student learning Privacy — data protection for students Practicality — implementation feasibility Anchor any AI use to further student outcomes in research-based learning pedagogy
B. Model Critical Evaluation of AI Tools	Establish and discuss AI use guidance with students, modeling when and how students can work with AI tools Engage students with credibility checks on sources and AI outputs Facilitate age-appropriate discussions on current and long-term effects of AI on individuals, the workforce, society, and the environment
C. Collaboratively Iterate and Refine AI Use with Colleagues	Evaluate established assignments for ways for students to intentionally engage with AI tools that deepen learning, collaboration, critical thinking, and creativity Engage in ongoing reflections on the efficacy of AI tools based on student learning data and levels of engagement

Domain 3: Lead with the Teacher Advantage

Competency	What Educators Know and Do
A. Create Opportunities for Students to Build Emotional Intelligence	Create learning opportunities for students to identify specific emotional, social, and collaboration skills they bring to their work Design collaborative work that anchors on emotional and social skills Highlight differences in AI-only products and human + AI products to build student awareness
B. Model and Create Opportunities for Creativity and Interdisciplinary Thinking	Create learning opportunities that build from students' interests and real-world situations, requiring multiple subjects/perspectives Build class structures that support metacognitive and "what if" moments to build creative & interdisciplinary thinking skills
C. Create Opportunities to Support Life-Long Learning	Develop and model routines for resilience, prompting students to reflect on their process when tools change or fail them Model refining personal understanding of when to use tools, when to question tools, and how to develop uniquely human capabilities

Coming Soon

Educator Professional Development Examples

We'll embed sample PD activities and resources aligned to each educator competency domain — including session agendas and facilitator guides.

Connect District Vision to School Implementation

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Vision for Student AI Readiness	Identify points of alignment and gaps between current instructional goals and district student AI Readiness outcomes	Develop school instructional goals aligned to updated district vision for student AI Readiness	Embed student AI Readiness outcomes within student assessment structures
Vision for School AI Use	Map district AI Readiness competencies for different roles to given school structure	Implement aligned support for developing AI Readiness competencies across all school staff roles	Consistently use AI Readiness competencies in existing district performance management systems
Vision for AI Use in Instruction	Internalize district criteria for AI use in instruction anchored in strong pedagogical practices	Pilot implementation of criteria for AI use in instruction for tool evaluation and instructional trainings	Expand and iterate on implementation of criteria for AI use in instruction across instructional domains

Galvanize Stakeholders

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Context Gathering	Informally gather input from students, teachers, staff, and families on AI in education to inform key priorities	Implement regular communications with families about AI Readiness efforts; engage community partners	Use family and community feedback to continuously improve AI Readiness strategies; position school as a community resource for AI literacy
Communicate and Educate	Identify key communication pathways to various stakeholders (parent newsletters, teacher messages, student notes)	Set a regular cadence of communication to all stakeholders on AI Readiness implementation, including learning opportunities	Refine communication and have regular opportunities for community stakeholders to engage in learning about AI literacy and AI Readiness

Set Conditions through Policy & Operations

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Guardrails	Identify gaps or areas for adaptation for AI Readiness in existing technology and academic integrity policies	Draft inclusive, adaptive, and transparent guardrails for ethical use of AI and safeguarding of student data	Iterate on AI guardrails, leveraging stakeholder feedback and integrating with existing acceptable use guidelines
Processes	Evaluate current tech procurement processes, existing tech stack in use, and tech efficacy measures in place	Create an updated tech procurement process that accounts for AI tool-specific needs; pilot the process	Full-system roll out of updated tech procurement and piloting process; monitor efficacy through an ongoing review of tools

Enable Teaching & Learning

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Instructional Leadership	Assess current teacher AI knowledge and comfort levels; identify early AI adopters to serve as AI Readiness champions	Establish and implement collaborative, structured opportunities for teachers to develop AI Readiness skills aligned to school-specific goals	Integrate ongoing, job-embedded professional learning on AI Readiness within teacher development systems
Observation & Coaching	Conduct observations to identify levels of AI Readiness happening in classrooms	Incorporate AI Readiness considerations into informal classroom observations and 1:1 coaching	Establish ongoing, differentiated coaching cycles using multiple data sources to deepen individual teachers' AI Readiness
Instructional Materials	Identify key criteria to evaluate existing and new instructional materials for supporting student AI Readiness outcomes	Lead instructional team in evaluating prioritized materials; share recommendations for curriculum changes	Support instructional teams in implementing updated curriculum; establish ongoing analysis of efficacy through student and teaching data
Tool Selection for Instruction	Define what a strong instructional AI tool is, anchored in student learning and AI Readiness outcomes	Implement criteria for instructional AI tool selection to evaluate and streamline existing district-approved instructional tools	Evolve criteria for instructional tool selection and apply to existing and new tool selection processes

Coming Soon

School Leader Implementation Examples

We'll embed resources, guides, and examples to support school leaders at each level of the rubric — so you can see what implementation looks like in practice.

Develop Equitable Vision for AI Readiness

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Vision for Student Readiness	Identify AI Readiness points of connection and gaps in existing district student outcome goals (e.g., examining the Portrait of a Graduate in the world of AI)	Develop and share clear student outcome goals that incorporate AI Readiness (e.g., reimagining the Portrait of a Graduate in the world of AI)	Embed AI-ready student outcome goals in student assessment structures and have a process established for reporting on and revising student outcome goals
Vision for System AI Use	Define AI Readiness competencies for all district roles; define vision for AI use and integration across district systems	Create and implement aligned support for developing AI Readiness competencies across all district roles; pilot AI use integration in systems across the district	Embed AI Readiness competencies in existing performance management systems; embed system-wide AI use training and ongoing evaluation of efficacy
Vision for AI Use in Instruction	Define criteria for AI use in instruction anchored in strong pedagogical practices	Pilot implementation of criteria for AI use in instruction for tool evaluation and instructional trainings	Expand and iterate on implementation of criteria for AI use in instruction across instructional domains

Case Study | Domain 1

Anaheim Union High School District

Orange County, CA27,195 students21 schools

Vision for Student ReadinessL1 → L2

Catalyst for Action

Superintendent Dr. Michael Matsuda’s vision beginning in 2018 drove AUHSD’s integration of AI — not as a distant future concern, but as an urgent imperative tied to whole-child, career-ready education. What started as an AI-focused CTE pathway at a single high school evolved into a district-wide initiative aimed at reaching all students.

Development of Their AI Framework

2021–22: Initial draft created after leaders found no California-specific AI guidance, developed collaboratively by 9 schools, educators, and a Columbia University research assistant
2023–24: District gathered input through family meetings, surveys, and teacher leader convenings
2025: Final revision based on gallery walks with students, site visits, and qualitative data collection

Advice for Other Districts

Start with a task force or workgroupBring together diverse voices (educators, IT, students, parents, community partners) before beginning planning.

Develop a clear “why”AUHSD framed theirs around student agency, ethics, and preparing critical thinkers, not just tech skills.

Center ethics, equity, and privacyDesign inclusive frameworks from the start; track and evaluate work through surveys and feedback loops.

“Our AI framework is about equity, agency, and innovation. We’re not just preparing students for careers — we’re preparing them to shape the future responsibly and ethically.”
Seema Sidhu, Director of Learning and Development, AUHSD

Explore More

View Interactive Infographic

Develop a Strategy

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Set Strategy	Review current strategic plan and change management; identify points of connection and possible challenges that AI Readiness presents	Identify places ready for innovation to pilot AI Readiness implementations (e.g., a particular school, a particular content area across multiple schools); implement pilots	Apply learnings from pilot implementations to expand AI Readiness implementation across all district functions; establish feedback loops to gather data on implementations
Plan for Change	Identify key people (leaders of areas of impact: operations, teaching & learning, programs) within the district to bring into the strategic planning	Establish and implement a formal structure (e.g., task force) for diverse stakeholder input on AI Readiness strategic plan and decisions on information flow going out of the district	Establish ownership of ongoing leadership of the formal structure that continues to meet to assess implementation, discuss issues that are arising, and make timely revisions to the strategic approach

Case Study | Domain 2

San Diego Unified School District

San Diego, CA103,024 students226 facilities

Plan for ChangeL1 → L2

Catalyst for Action

In response to growing AI use among students and increasing requests for guidance from teachers, SDUSD initiated a cross-functional AI Task Force in Fall 2024. It was intentionally designed with representation from educators, IT leadership, district operations, parents, students, and departments like nursing, transportation, and construction.

Task Force Timeline

Nov 2024: Kickoff, building shared AI literacy understanding and reviewing Ohio’s AI Toolkit as an example
Dec 2024: Focused on values; sub-groups drafted guidelines by audience (students, educators, etc.)
Jan 2025: Full teacher workday on acceptable use guidelines; topic groups completed templates
Mar 2025: Final task force meeting with tuning protocol and group review of draft guidelines
Mar–Jun 2025: Senior leadership review; preparing for Board of Education approval

Advice for Other Districts

Involve a broad range of stakeholdersExtend beyond instructional staff to include operations, construction, transportation.

Use the structures you already haveSDUSD leveraged its existing 1:1 Ambassador program to recruit student task force members.

Write guidelines, not policiesAI evolves too fast for rigid policies. Guidelines are broader, more flexible, and easier to update.

“We knew we couldn’t wait for the perfect plan — AI evolves too quickly. Our goal wasn’t to control it, but to understand it together, build responsible guardrails, and empower our schools to adapt with intention.”
Julie Garcia, Senior Director of Future Ready Learning, SDUSD

Set Conditions through Policy & Operations

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Guardrails	Identify gaps or areas for adaptation for AI Readiness in existing technology and academic integrity policies (e.g., acceptable use policies)	Draft inclusive, adaptive, and transparent guardrails for ethical use of AI and safeguarding of student data by students, teachers, leadership, and central office staff	Iterate on AI guardrails, leveraging feedback from stakeholders, and integrating to existing tech stack acceptable use guidelines
Processes	Evaluate current tech procurement processes and existing tech stack (identifying which tools use AI), and tech efficacy measures in place	Create an updated tech procurement process that accounts for AI tool-specific needs, a plan for sunsetting existing tools, and an evaluation plan for measuring success; pilot the process	Full-system rollout of updated tech procurement and piloting process; monitor efficacy through an ongoing review that accommodates the pace of change of AI technology and new tools

Case Study | Domain 3

Agua Fria High School District

Arizona~10,000 students6 high schools

GuardrailsL1 → L2

Catalyst for Action

Beginning exploratory work in Spring 2023, AFHSD saw AI as both an instructional opportunity and an operational challenge requiring intentional guardrails. Rather than reacting defensively, district leaders moved quickly to align AI implementation with their strategic plan, Portrait of a Graduate, and “Roadmap to 2035.”

Key Frameworks They Developed

The Stoplight FrameworkTeachers set AI use levels per assignment: Green (AI encouraged), Yellow (AI with limitations/disclosure), Red (no AI). Default is Red unless the teacher says otherwise.

The CARE FrameworkEmbedded into the Freshman Experience course so every 9th grader gets foundational AI literacy, focused on Clarity, Accuracy, Relevance, and Ethics.

AI AmbassadorsStipended teacher leaders on each campus running PLCs, full-day PD, and lunch-and-learns. By 2025–26, 71% of employees reported using AI weekly or more.

Lessons Learned

Guardrails should empower teachers, not restrict them. Structured flexibility accelerates adoption.
Pilot before scaling procurement. Test with a focused cohort before investing systemwide.
AI must be embedded, not adjacent. Align to strategic plan to avoid “side initiative” framing.

“AI isn’t an initiative on the side — it’s embedded in how we prepare students for 2035 and beyond. Our job is to build the structures that let innovation happen responsibly.”
Drew Olsen, Director of AI and Instructional Technology, Agua Fria HSD

Galvanize Stakeholders

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Context Gathering	Informally gather input (e.g., survey, focus groups) from students, families, teachers, staff, and board members on AI literacy and AI Readiness needs and concerns	District stakeholders (students, families, teachers, staff, and board members) have a role in the AI Readiness task force and/or are providing feedback and guidance on the strategic vision and plan	Establish regular cadence for gathering feedback and needs from stakeholders (students, families, teachers, staff, board members)
Engage and Learn	Identify potential local industry and post-secondary organizations to learn from on AI impacts on the community	Develop partnerships with local industry and post-secondary organizations to identify key AI impacts on community	Deepen ongoing partnerships with local and post-secondary organizations to support AI readiness skill-building aligned to community needs
Communicate and Educate	Identify key communication pathways to various stakeholders (e.g., parent newsletters, teacher messages, board meetings)	Set a regular cadence of communication to all stakeholders on AI readiness implementation aligned to existing communication structures, including learning opportunities for stakeholders	Refine communication on AI readiness implementation and have regular opportunities for community stakeholders to engage in learning on AI literacy and readiness implementation

Case Study | Domain 4

San Diego Unified School District

San Diego, CA103,024 students226 facilities

Content GatheringL1 → L2

Catalyst for Action

As SDUSD’s AI Task Force began developing districtwide guidelines, leaders recognized that community questions about AI — sparked by media coverage and growing classroom use — called for proactive, transparent engagement. The answer: a districtwide AI Town Hall in December 2024.

Town Hall Approach

Held virtually in December 2024 with two sessions (day + evening) to maximize access
~275 participants across SDUSD’s 226 schools
Translation services provided; Family Engagement Department managed outreach
Program grounded participants in AI basics before inviting dialogue, improving the quality of feedback
Positioned as the first in an ongoing series, not a one-time event

Lessons Learned

Transparency reduces resistanceEngaging stakeholders before guidelines were finalized signaled that their voice mattered.

Education must precede feedbackGrounding the conversation in shared definitions produced more nuanced, productive input.

Engagement is a system, not an eventMaintaining momentum requires a predictable cadence of updates and multiple feedback channels.

“Our goal was to make sure the community understood that we weren’t ignoring AI — we were addressing it thoughtfully. Transparency builds trust, and trust allows us to move forward together.”
Derek Suzuki, Program Manager of Instructional Technology, SDUSD

Enable Teaching & Learning

Sub-domain	L1 Demonstrate Commitment	L2 Invest & Implement	L3 Deepen & Iterate
Capacity Building	Provide foundational-level trainings with targeted school and teacher leaders, aligning training with AI readiness competencies for given roles	Scale foundational level training for all instructional staff and provide deeper application for those who have completed foundational trainings; aligned to AI readiness competencies for given roles	Provide ongoing, differentiated, integrated deeper application trainings for all instructional staff; foundational training integrated into new hire trainings; aligned to AI readiness competencies
Instructional Materials	Identify processes for evaluating district-approved instructional materials to support AI Readiness student outcomes; align process to vision for AI use in instruction	Evaluate district-approved instructional materials for AI Readiness and provide recommendations for changes to curriculum to meet student AI Readiness outcomes, content outcomes, and vision for AI use in instruction	Implement updated curriculum and engage in ongoing analysis of efficacy of instructional materials to meet student AI Readiness outcomes, content outcomes, and vision for AI use in instruction
Departmental Alignment	Identify all district-wide departments (e.g., multi-lingual learners, career pathways, after-school programs) that will need updating to align with student AI Readiness outcomes	Prioritize departments for student AI Readiness alignment updates based on district vision	Prioritized department programs are updated for student AI Readiness outcomes and aligned to district vision, with clear processes and recommendations for replicating with other departments
Tool Selection for Instruction	Define what a strong instructional AI tool is, anchored in student learning and AI Readiness outcomes	Implement criteria for instructional AI tool selection to evaluate and streamline existing district-approved instructional tools; ensure alignment to overall district tech procurement processes	Evolve criteria for instructional tool selection and apply to existing and new tool selection processes

Case Study | Domain 5

Multnomah Education Service District

Multnomah County, OR~85,000 students8 local districts

Capacity BuildingL1 → L2

Catalyst for Action

With the rapid arrival of generative AI in 2023, MESD recognized a dual responsibility: help districts thoughtfully integrate AI while addressing ethical risks, equity considerations, and implementation challenges. As an ESD, they build capacity rather than mandate curriculum, focusing on structured professional learning and regional collaboration.

How They Built Capacity

Spring 2024: Co-hosted large-scale AI conference with two other ESDs, leading to AI Innovator Forums (145 registrants across 3 ESDs)
Foundational PD: 3-part virtual AI series (1.5 hrs each); school-based coaching; statewide EdTech Cadre (78 participants)
Spring 2025 AI Educators Cohort: 13 districts, diverse roles, grounded in the UNESCO AI Competency Framework and the aiEDU AI Readiness Framework
May 2025 Regional Conference: 335 attendees, 29 districts; cohort members presented capstone projects

Lessons Learned

Regional collaboration multiplies capacityPartnering across ESDs expanded reach and reduced duplication, enabling consistent messaging at scale.

Depth requires structure and timeShort sessions build awareness; sustained cohort models drive instructional change. Stipends matter.

Balance innovation with ethical reflectionEducators are eager to experiment but also deeply concerned about bias, privacy, and academic integrity.

“Our role isn’t to rush tools into classrooms — it’s to help educators thoughtfully filter what matters, build their capacity, and ensure AI integration supports students responsibly and ethically.”
Katy Tibbs, Digital Learning and AI Integration Specialist, MESD

Professional Learning Services

aiEDU Professional Learning Offerings

aiEDU professional learning is a flexible, district-friendly ecosystem. Every offering is grounded in the aiEDU AI Readiness Framework, ends with a concrete takeaway, and builds capacity that holds over time—not just exposure. Districts mix and match to meet their goals, timelines, and budgets.

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Synchronous: Training Sessions

60–90 min per session Virtual or In-Person Teachers & Leaders Groups of 10–100+

14 sessions across 6 topic areas—mix and match to fit your goals. Each session ends with a ready-to-use takeaway. Offered in two formats; no async work required for either option.

Suggested Sequences

Why AI Readiness Matters?
AI, Cognition, and the Future of Learning
Student Engagement in an AI World
When Should AI Be Used for Learning?
Redesigning Tasks for Academic Integrity
Guiding Student Use of AI

Setting the Stage for Academic Integrity
When Should AI Be Used for Learning?
Redesigning Tasks for Academic Integrity
Grading & Feedback with AI

AI, Cognition, and the Future of Learning
Setting the Stage for Academic Integrity
When Should AI Be Used for Learning?
Redesigning Tasks for Academic Integrity
Responsible AI Use in Schools

When Should AI Be Used for Learning?
Redesigning Tasks for Academic Integrity
Student Engagement in an AI World
Guiding Student Use of AI
From Product to Process: What AI Transcripts Reveal
Getting Real About Deepfakes

Optional add-on: Teaching AI Readiness Across Subjects

When Should AI Be Used for Learning?
Designing Accessible Learning with AI
Guiding Student Use of AI
Redesigning Tasks for Academic Integrity

Optional add-on: Teaching AI Readiness Across Subjects

AI Readiness Foundations

Session	Description	Time
Why AI Readiness Matters?	This session builds the foundational understanding educators need to navigate AI confidently by exploring its mechanics and limitations. Participants develop conceptual grounding and build AI fluency through hands-on practice focused on responsible use.	60–90 min
AI, Cognition, and the Future of Learning	This session examines how AI is reshaping human learning and why educators must protect “productive struggle” in the classroom. Participants learn to distinguish between using AI to extend student thinking versus outsourcing the cognitive work students must do themselves.	60–90 min
Enhancing Your Practice	Educators move from basic curiosity to purposeful application by identifying exactly where AI adds value to their teaching tasks. Participants practice effective prompting and leave with a comparative understanding of tools they can apply immediately to their workflows.	60–90 min
What is Vibecoding? New	This session introduces “vibe coding,” where participants use natural language rather than traditional programming to build functional digital tools. Through a guided build, educators experience the full creation cycle and leave with a working prototype.	60–90 min

Ethics, Policy & Responsible Use

Session	Description	Time
Setting the Stage for Academic Integrity New	This session establishes the conceptual foundation educators need before redesigning assignments or communicating expectations around student AI use. Participants leave with a concrete roadmap for building integrity through transparency and rethinking assessment.	60–90 min
Responsible AI Use in Schools	Designed for leaders, this session focuses on developing durable, principled AI guidance that adapts to a rapidly changing landscape. Participants examine what makes AI guidance flexible enough to adapt across grade levels and subject areas, and why transparency with students, families, and staff is essential to building long-term trust.	60–90 min

Instructional Decision-Making

Session	Description	Time
When Should AI Be Used for Learning?	This session gives educators a process for determining when AI use genuinely supports learning—and when it risks undermining it. Participants practice evaluating AI tools against learning goals, identifying bias and value misalignment in AI outputs, and thinking through tradeoffs around quality, data privacy, and student impact.	60–90 min
Redesigning Tasks for Academic Integrity	This session translates principles of integrity into hands-on task redesigns that make student thinking visible across all subjects. Participants reimagine existing assignments to capture evidence from both the process and the product.	60–90 min
Teaching AI Readiness Across Subjects New	Educators explore how AI readiness integrates into every content area by identifying where durable skills are shifting in their specific fields. Participants leave with a concrete integration plan and access to curriculum resources tailored to their grade-level standards.	60–90 min
Designing Accessible Learning with AI New	This session explores how to use AI to reduce learning barriers for students with disabilities without removing cognitive responsibility. Participants practice designing goal-aligned supports that maintain high rigor while providing necessary scaffolds.	60–90 min
Grading & Feedback with AI New	Participants learn how AI can provide students with timely, actionable insights while significantly reducing educator workload. The session balances productivity gains against essential ethical considerations to ensure human judgment remains at the center of the feedback loop.	60–90 min

Guiding Student Use of AI

Session	Description	Time
Guiding Student Use of AI New	This session helps educators move beyond rule-setting to become active guides and models for how students engage with AI tools. Participants learn how to facilitate credible, age-appropriate conversations about AI’s broader impact on the skills and habits of mind students will need to develop. Educators leave with routines and discussion protocols they can put into practice right away.	60–90 min
Student Engagement in an AI World New	This session reframes the AI challenge by designing learning experiences that build student agency and make work worth doing. Educators leave with concrete classroom moves that protect productive struggle and keep the thinking process central to learning.	60–90 min
Getting Real About Deepfakes	This session examines how AI has fundamentally changed what it means to evaluate credibility—and what educators need to teach students to navigate that shift with confidence. Participants explore realistic scenarios and apply strategies to identify deepfakes and their impact on schools and students.	60–90 min
From Product to Process: What AI Transcripts Reveal New	This session introduces AI interaction logs as a powerful new way to assess student reasoning and revision logic. Participants leave with rubrics and routines for using transcripts to shift the focus of assessment from the final output to the thinking process.	60–90 min

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Cohort-Based PLCs

Multi-session Synchronous + Async Virtual or In-Person 90-min sessions

PLCs provide structured, collaborative learning over multiple sessions anchored to a problem of practice. Each session is 90 min unless otherwise noted.

AI Readiness Foundations (101)

AI-101: Building Your AI Readiness

All Educators & Leaders

Accessible, hands-on entry point into AI for educators and staff across roles. Focuses on the individual—foundational AI skills, tool exploration, and confidence through practice and peer community. Core areas: LLM prompting, custom bot building, personal workflow integration. Ideal starting point before deeper offerings.

5 sessions (90 min each) + ~30 min async between sessions

Deepening Your Practice (201)

AI-201: Educator AI Readiness in Action

Teachers

Designed for educators ready to go deeper. Participants identify a specific instructional problem of practice—differentiating lessons, improving student self-reflection, or building student AI readiness skills—then plan, build, and test solutions through peer collaboration.

5 sessions (90 min each) + 60–90 min async between sessions

School AI Readiness in Action

School Leaders

Designed for school leaders to understand what AI readiness means at the school level. Leaders self-assess on the aiEDU AI Readiness Rubric, explore examples in action, identify areas of focus, and work through a problem-of-practice cycle and impact review.

5–6 sessions (90 min each) + 60–90 min async between sessions

District AI Readiness in Action

District Leaders

Designed for district leaders to understand what AI readiness means at the district level. Leaders self-assess on the aiEDU AI Readiness Rubric, explore examples in action, identify district areas of focus, and work through a district-level problem-of-practice and impact cycle.

5–6 sessions (90 min each) + 60–90 min async between sessions

School Teams: Problem of Practice

LeadersTeachers

A hybrid fellowship for school teams (1 leader + 2–3 teacher-leaders). Teams explore educator workflow and student learning scenarios, then specialize in one pathway. Culminates in a showcase from each participant’s role perspective. Optional coaching sessions available.

5 sessions (90 min each) + 60–90 min async + optional coaching

Building Expertise

Building Internal Capacity with AI New

System & Org Leaders

As AI evolves, organizations need to evolve their internal training systems. Participants identify key areas of focus, explore examples of training structures, and build their own internal AI learning plan to take back to their system.

4 sessions (1.5 hrs each) + 30–60 min async between sessions

Vibe Coding New

All Educators & Staff

Build with AI using plain-language prompting—designed for everyone in a school building, not just the tech-savvy few. Teachers, operations staff, deans, front office, and principals all leave with a vibecoded solution to a real workflow. No technical background required.

1 launch session (2.5 hrs) + 4 workshops (1.5 hrs each) + 60–90 min async between sessions

Curriculum Internalization New

Instructional LeadsTeachers

Supports instructional leads and teachers in aligning and implementing AI readiness curriculum to existing instructional goals. Sessions cover content design, implementation strategies, peer review and feedback, and data analysis to monitor impact.

5 sessions (90 min each) + 30–60 min async between sessions

Evaluating AI Tools New

Educators, School Teams & Leaders

Provides educators, school teams, and leaders a structured process for evaluating AI tools for school-based purposes. Participants complete mini tool tests and form their own context-specific recommendations for tool adoption.

5 sessions (90 min each) + ~60 min async between sessions

⚡

Spark the Future: Train the Trainer

3 sessions × 90 min Virtual or In-Person Leaders

Equips school and district leaders to facilitate the Teaching for Tomorrow course for their own staff, building sustainable, schoolwide AI literacy capacity from the inside out.

What Leaders Will Do

Be trained to facilitate the Teaching for Tomorrow course
Develop a rollout plan for schoolwide AI literacy training
Build a network of internal facilitators to sustain AI readiness efforts

🎓

Asynchronous Courses

Self-paced Virtual No scheduling required

Teachers

Course	Description	Time
Teaching for Tomorrow	A comprehensive, flexible course that empowers educators with the knowledge and strategies needed to confidently use and teach AI—covering what AI is, how to analyze it critically, and how to build student AI literacy. Hosted on Mighty Network.	~5 hours

TeachersLeaders

Course	Description	Time
What is AI?	A SCORM-compliant micro-course ideal for district-wide rollouts that introduces AI basics, explores risks, and models responsible use—with an option to embed district-specific guidelines. Compatible with most LMS systems.	15 minutes

Note: In-person delivery is available for any synchronous session or PLC at additional cost.

Ready to bring professional learning to your educators?

Get in Touch

Strategic Advisory Services

District-Level AI Readiness Guidance

aiEDU’s Strategic Advisory Services provide a structured yet flexible pathway for districts to build AI Readiness — aligned to the AI Readiness Framework Rubric, so you can set measurable goals at the start and track progress over time. Partners can select one offering or combine several based on their context, priorities, and stage.

AI Readiness Foundations

Your goal: You’re looking to get a clear picture of where your system stands on AI readiness and build a shared, actionable strategy to move forward — with expert support to surface gaps, identify leverage points, and prioritize next steps.

Prerequisite: None

What’s included:

AI Readiness Orientation & Goal Setting AI Readiness Assessment & Gap Analysis Action Planning

Engagement timeline: 3–6 months

Rubric Connection

Subdomain	Demonstrate Commitment	Invest & Implement	Deepen & Iterate
D1.1 Vision for Student Readiness	Identify AI Readiness points of connection and gaps in existing student outcome goals (e.g., Portrait of a Graduate)	Develop and share clear student outcome goals that incorporate AI Readiness	Embed AI-ready student outcome goals in assessment structures; establish process for reporting and revising
D1.2 Vision for System AI Use	Define AI Readiness competencies for all district roles; define vision for AI use and integration across district systems	Create and implement aligned support for developing AI Readiness competencies across all roles; pilot AI use integration	Embed AI Readiness competencies in performance management systems; embed system-wide AI use training and ongoing evaluation
D1.3 Vision for AI Use in Instruction	Define criteria for AI use in instruction anchored in strong pedagogical practices	Pilot implementation of criteria for AI use in instruction for tool evaluation and instructional trainings	Expand and iterate on implementation of criteria for AI use in instruction across instructional domains
D2.1 Set Strategy	Review current strategic plan and change management; identify points of connection and possible challenges that AI Readiness presents	Identify places ready for innovation to pilot AI Readiness implementations (e.g., a particular school, a particular content area across multiple schools); implement pilots	Apply learnings from pilot implementations to expand AI Readiness implementation across all district functions; establish feedback loops to gather data on implementations
D2.2 Plan for Change	Identify key people (i.e., leaders of areas of impact: operations, teaching & learning, programs) within the district to bring into the strategic planning	Establish and implement a formal structure (e.g., task force) for diverse stakeholder input on AI Readiness strategic plan and decisions on information flow going out of the district	Establish ownership of ongoing leadership of the formal structure that continues to meet to assess implementation, discuss issues that are arising, and make revisions/adjustments to the strategic approach

Teal = target outcome of this engagement • Grayed = beyond scope

Category	Objectives	Engagement Scope
AI Readiness Orientation & Goal Setting	Orient to aiEDU’s District Readiness Rubric Map readiness and set goals	One two-hour meeting
AI Readiness Assessment & Gap Analysis	Review district’s current AI Readiness work Identify strengths, weaknesses, overlaps, gaps, and leverage points	One 1-hr meeting Up to four staff interviews 5 consulting hours
Action Planning	Identify key implementation activities and milestones (communications, learning, stakeholder engagement)	Three one-hour meetings with leadership team 10 consulting hours

Engagement Phases

Phase I: Orient to the AI Readiness Framework and set goals for the engagement.
Phase II: Meet with leadership and conduct staff interviews to draft an AI readiness assessment and gap analysis with recommendations.
Phase III: Work with leadership to develop an action plan. Reassess goals and conclude engagement.

Policies and Guidelines

Your goal: You’re ready to develop a clear, shared systemwide approach to AI — one that sets expectations for responsible use, earns genuine stakeholder buy-in, and is built to adapt as AI continues to evolve.

Prerequisite: None • Available with or without a task force

What’s included:

AI Readiness Orientation & Goal Setting Vision & Guiding Principles Policies & Guidelines Development Launch Strategy

Engagement timeline: One full school year

Rubric Connection

Subdomain	Demonstrate Commitment	Invest & Implement	Deepen & Iterate
D1.1 Vision for Student Readiness	Identify AI Readiness points of connection and gaps in existing district student outcome goals (e.g., examining the Portrait of a Graduate in the world of AI)	Develop and share clear student outcome goals that incorporate AI Readiness (e.g., reimagining the Portrait of a Graduate in the world of AI)	Embed AI-ready student outcome goals in student assessment structures and have a process established for reporting on and revising student outcome goals
D1.2 Vision for System AI Use	Define AI Readiness competencies for all district roles; define vision for AI use and integration across district systems	Create and implement aligned support for developing AI Readiness competencies across all district roles; pilot AI use integration in systems across the district	Embed AI Readiness competencies in existing performance management systems or structures; embed system-wide AI use training and ongoing evaluation of efficacy
D1.3 Vision for AI Use in Instruction	Define criteria for AI use in instruction anchored in strong pedagogical practices	Pilot implementation of criteria for AI use in instruction for tool evaluation and instructional trainings	Expand and iterate on implementation of criteria for AI use in instruction across instructional domains
D3.1 Guardrails	Identify gaps or areas for adaptation in existing technology and academic integrity policies	Draft inclusive, adaptive, and transparent guardrails for ethical use of AI and safeguarding of student data	Iterate on AI guardrails leveraging stakeholder feedback; integrate into existing tech stack acceptable use guidelines
D3.2 Processes	Evaluate current tech procurement processes and existing tech stack (identifying AI tools) and efficacy measures in place	Create an updated tech procurement process that accounts for AI tool-specific needs; pilot the process	Full-system rollout of updated tech procurement; monitor efficacy through ongoing review accommodating the pace of AI change

Teal = target outcome of this engagement • Grayed = beyond scope

Instructional Staff Learning

Your goal: You’re ready to create meaningful, structured professional learning that helps instructional staff build AI readiness — with space for safe exploration and a process for developing shared instructional priorities around AI use.

Recommended prerequisite: Policies & Guidelines

What’s included:

AI Readiness Orientation & Goal Setting Instructional Vision for AI Use Professional Learning Plan Curricular Resource Scope & Sequencing

Engagement timeline: 6–9 months

Rubric Connection

Subdomain	Demonstrate Commitment	Invest & Implement	Deepen & Iterate
D1.1 Vision for Student Readiness	Identify AI Readiness points of connection and gaps in existing district student outcome goals (e.g., examining the Portrait of a Graduate in the world of AI)	Develop and share clear student outcome goals that incorporate AI Readiness (e.g., reimagining the Portrait of a Graduate in the world of AI)	Embed AI-ready student outcome goals in student assessment structures and have a process established for reporting on and revising student outcome goals
D1.3 Vision for AI Use in Instruction	Define criteria for AI use in instruction anchored in strong pedagogical practices	Pilot implementation of criteria for AI use in instruction for tool evaluation and instructional trainings	Expand and iterate on implementation of criteria for AI use in instruction across instructional domains

Teal = target outcome of this engagement • Grayed = beyond scope

Category	Objectives	Engagement Scope
AI Readiness Orientation & Goal Setting	Orient to aiEDU’s District Readiness Rubric Map readiness and set goals	One two-hour meeting
Vision & Guiding Principles Development	Develop a vision for instructional AI use for students and educators	Three 90-min meetings with leadership/stakeholders 8 consulting hours
Building a Professional Learning Plan	Develop a PL plan for all roles and levels of instructional staff, aligned to competencies Develop a plan for monitoring, feedback, and iteration	Four one-hour meetings with leadership 10 consulting hours
Curricular Resource Scope & Sequencing	Identify touch points in existing curriculum for AI readiness resources Share aiEDU resources and support scope and sequence planning	Five 90-min meetings with stakeholder group Five 60-min planning sessions with leadership 10 consulting hours

Engagement Phases

Phase I: Orient to framework, set goals. aiEDU supports developing an instructional vision for AI use.
Phase II: Draft AI readiness professional learning plan and implementation roadmap.
Phase III: Identify opportunities to integrate curricular resources into existing curriculum. Reassess goals and conclude engagement.

AI Tool Evaluation and Selection

Your goal: You want to bring clarity and coherence to your district’s AI tool landscape — developing a shared instructional vision and a principled, collaborative process for evaluating and selecting tools that genuinely support student learning.

Recommended prerequisite: Policies & Guidelines • Available with or without a task force

What’s included:

AI Readiness Orientation & Goal Setting Instructional Vision for AI Use Co-Developed Evaluation Criteria Tool Exploration & Assessment Implementation & Training Plan

Engagement timeline: 6 months

Rubric Connection

Subdomain	Demonstrate Commitment	Invest & Implement	Deepen & Iterate
D5.4 Tool Selection for Instruction	Define what a strong instructional AI tool is, anchored in student learning and AI Readiness outcomes	Implement criteria for instructional AI tool selection to evaluate and streamline existing district-approved tools	Evolve criteria for instructional tool selection and apply to existing and new tool selection processes
D3.2 Processes	Evaluate current tech procurement processes and existing tech stack (identifying AI tools) and efficacy measures in place	Create an updated tech procurement process that accounts for AI tool-specific needs; pilot the process	Full-system rollout of updated tech procurement; monitor efficacy through ongoing review accommodating the pace of AI change

Teal = target outcome of this engagement • Grayed = beyond scope

Internal Learning Plan

Your goal: You’re committed to building AI readiness across your entire district — including admin and operational staff — so that all teams are aligned, confident, and moving forward together with a clear plan for internal learning.

Recommended prerequisite: Policies & Guidelines

What’s included:

AI Readiness Orientation & Goal Setting Five-Part Leadership Learning Series Internal Staff Learning Plan

Engagement timeline: 3 months

Rubric Connection

Subdomain	Demonstrate Commitment	Invest & Implement	Deepen & Iterate
D1.2 Vision for System AI Use	Define AI Readiness competencies for all district roles; define vision for AI use and integration across district systems	Create and implement aligned support for developing AI Readiness competencies across all roles; pilot AI use integration in systems	Embed AI Readiness competencies in performance management systems; embed system-wide AI use training and ongoing evaluation
D5.1 Capacity Building	Provide foundational-level trainings with targeted school and teacher leaders, aligned to AI readiness competencies	Scale foundational training for all instructional staff; provide deeper application for those who completed foundational trainings	Provide ongoing, differentiated, integrated deeper application trainings for all staff; integrate into new hire onboarding

Teal = target outcome of this engagement • Grayed = beyond scope

Category	Objectives	Engagement Scope
AI Readiness Orientation & Goal Setting	Orient to aiEDU’s District Readiness Rubric Map readiness and set goals	One two-hour meeting
Internal Staff Learning	Support leadership team to build expertise with AI technology and tools Support partner in developing an internal staff learning plan	Two one-hour planning meetings Five 90-min learning sessions with leadership team 5 consulting hours

Engagement Phases

Phase I: Orient to framework, set goals.
Phase II: Plan five-part learning series; aiEDU facilitates series with partner leadership.
Phase III: Support leadership to draft a plan for rolling out an internal learning initiative. Reassess goals and conclude engagement.

Interested in Strategic Advisory Services?

Let’s talk about where your district is and what kind of support would make the biggest difference.

Schedule a Conversation

AVAILABLE NOW

Current Curriculum Offerings

Elementary Explorations

Grades 3–5

Short, engaging exploration activities that introduce foundational AI concepts to elementary students. No prior AI knowledge needed — for students or teachers. Each exploration is hands-on, discussion-driven, and designed to be accessible across subject areas.

VIEW SAMPLES →

AI Snapshots

Grades 6–12

Snapshots are quick, discussion-based activities that build AI literacy inside existing subjects — no dedicated AI class required. They're designed to be picked up by any secondary teacher and dropped into a lesson with minimal prep.

VIEW SAMPLES →

AP CSP SmartTeach Manual

Grades 9–12

A practical teacher guide for integrating AI tools directly into the AP CSP curriculum. Designed to help educators move beyond AI awareness and into hands-on application — with ready-to-use scenarios, mini-lessons, and strategies for the Create Task.

EXPLORE THE MANUAL 📄

Project Dashboard

Grades 9–12

Structured, inquiry-based AI projects organized by subject area — 8 for general classrooms and 6 aligned to AP CSP. Students investigate a topic in depth and produce a shareable artifact.

VIEW SAMPLES →

Introduction to Artificial Intelligence Course

Grades 9–12

A structured 10-week course where students explain how AI systems are trained, evaluate real-world AI applications and their risks, analyze issues of bias and fairness, and build a working AI-powered application using accessible tools.

VIEW SAMPLE LESSON PLANS 📄

IN DEVELOPMENT · 2026–2027

The Expanded Curriculum Ecosystem

aiEDU's 2026 curriculum strategy introduces two new product types that together create a comprehensive, scaffolded K–12 AI Readiness learning pathway.

COMING 2026 · GRADES K–12

Learning Journeys

Multi-day instructional sequences that move students through meaningful, scaffolded learning on key AI Readiness concepts with practice-based assessments built in.

Deeper than Snapshots, shorter than a full course
Practice-based assessments included
For all content areas and grade levels
Regular release cadence across 2026–2027

COMING 2026 · GRADES 6–12

Curriculum Companions

Resources that help districts and schools embed AI Readiness into existing core curriculum at scale — complementing instructional materials without replacing them.

Integrates AI literacy into existing content area curriculum
Designed for district-wide adoption
Supports sustainability and scale

aiEDU

Professional Learning Offerings

A flexible, district-friendly ecosystem — every offering grounded in the aiEDU AI Readiness Framework, ends with a concrete takeaway, and builds capacity that lasts

programs@aiedu.org
www.aiedu.org

Synchronous: Training Sessions — 14 Sessions Across 4 Topic Areas

60–90 min each · Virtual or In-Person · Educators & Leaders · Groups of 10–100+ · No async work required · Available standalone or combined into 5 curated sequences (see back)

AI Readiness Foundations

Why AI Readiness Matters? Updated

Builds the foundational understanding educators need to navigate AI confidently by exploring its mechanics and limitations. Participants develop conceptual grounding and build AI fluency through hands-on practice focused on responsible use.

AI, Cognition, and the Future of Learning Updated

Examines how AI is reshaping human learning and why educators must protect “productive struggle” in the classroom. Participants learn to distinguish between using AI to extend student thinking versus outsourcing the cognitive work students must do themselves.

Enhancing Your Practice Updated

Educators move from basic curiosity to purposeful application by identifying exactly where AI adds value to their teaching tasks. Participants practice effective prompting and leave with a comparative understanding of tools they can apply immediately to their workflows.

What is Vibecoding? New

Introduces “vibe coding,” where participants use natural language rather than traditional programming to build functional digital tools. Through a guided build, educators experience the full creation cycle and leave with a working prototype.

Ethics, Policy & Responsible Use

Setting the Stage for Academic Integrity New

Establishes the conceptual foundation educators need before redesigning assignments or communicating expectations around student AI use. Participants leave with a concrete roadmap for building integrity through transparency and rethinking assessment.

Responsible AI Use in Schools Updated Leaders

Designed for leaders, focuses on developing durable, principled AI guidance that adapts to a rapidly changing landscape. Examines what makes AI guidance flexible enough to adapt across grade levels and why transparency with students, families, and staff is essential to building long-term trust.

Instructional Decision-Making

When Should AI Be Used for Learning? Updated

Gives educators a process for determining when AI use genuinely supports learning—and when it risks undermining it. Participants practice evaluating AI tools against learning goals, identifying bias and value misalignment in outputs, and thinking through tradeoffs around quality, data privacy, and student impact.

Redesigning Tasks for Academic Integrity Updated

Translates principles of integrity into hands-on task redesigns that make student thinking visible across all subjects. Participants reimagine existing assignments to capture evidence from both the process and the product.

Instructional Decision-Making (continued)

Teaching AI Readiness Across Subjects New

Educators explore how AI readiness integrates into every content area by identifying where durable skills are shifting in their specific fields. Participants leave with a concrete integration plan and access to curriculum resources tailored to their grade-level standards.

Designing Accessible Learning with AI New

Explores how to use AI to reduce learning barriers for students with disabilities without removing cognitive responsibility. Participants practice designing goal-aligned supports that maintain high rigor while providing necessary scaffolds.

Grading & Feedback with AI New

Participants learn how AI can provide students with timely, actionable insights while significantly reducing educator workload. The session balances productivity gains against essential ethical considerations to ensure human judgment remains at the center of the feedback loop.

Guiding Student Use of AI

Guiding Student Use of AI New

Helps educators move beyond rule-setting to become active guides and models for how students engage with AI tools. Participants learn to facilitate credible, age-appropriate conversations about AI’s broader impact on the skills and habits of mind students will need to develop.

Student Engagement in an AI World New

Reframes the AI challenge by designing learning experiences that build student agency and make work worth doing. Educators leave with concrete classroom moves that protect productive struggle and keep the thinking process central to learning.

Getting Real About Deepfakes Updated

Examines how AI has fundamentally changed what it means to evaluate credibility—and what educators need to teach students to navigate that shift with confidence. Participants explore realistic scenarios and apply strategies to identify deepfakes and their impact on schools and students.

From Product to Process: What AI Transcripts Reveal New

Introduces AI interaction logs as a powerful new way to assess student reasoning and revision logic. Participants leave with rubrics and routines for using transcripts to shift the focus of assessment from the final output to the thinking process.

Training Sequences — 5 Curated Pathways

Combine sessions into a coherent 4–6 session arc tailored to a district goal · Optional add-on for any sequence: Teaching AI Readiness Across Subjects

Core Readiness to Classroom PracticeAll Educators · 6 sessions

Academic Integrity FirstAll Educators · 4 sessions

Policy + Implementation for LeadersLeaders · 5 sessions

Critical Thinking with AIAll Educators · 6 sessions

Inclusive AI ReadinessAll Educators · 4 sessions

Optional add-on: Teaching AI Readiness Across Subjects

aiEDU

Professional Learning Offerings

A flexible, district-friendly ecosystem — every offering grounded in the aiEDU AI Readiness Framework, ends with a concrete takeaway, and builds capacity that lasts

programs@aiedu.org
www.aiedu.org

Cohort-Based PLCs

Multi-session · Synchronous + Async · Virtual or In-Person · Cohort Format · 90-min live sessions

AI Readiness Foundations (101)

AI-101: Building Your AI Readiness

All Educators & Leaders

Hands-on entry point into AI—LLM prompting, custom bot building, and personal workflow integration. Ideal starting point before deeper offerings.

5 sessions (90 min) + ~30 min async

Deepening Your Practice (201)

AI-201: Educator AI Readiness in Action

Teachers

Identify a specific instructional problem of practice and plan, build, and test AI-supported solutions through peer collaboration.

5 sessions (90 min) + 60–90 min async

School AI Readiness in Action

School Leaders

Self-assess on the aiEDU AI Readiness Rubric, explore examples in action, identify school-level areas of focus, and work through a problem-of-practice and impact cycle.

5–6 sessions (90 min) + 60–90 min async

District AI Readiness in Action

District Leaders

Self-assess on the aiEDU AI Readiness Rubric, explore examples in action, identify district areas of focus, and work through a district-level problem-of-practice and impact cycle.

5–6 sessions (90 min) + 60–90 min async

School Teams: Problem of Practice

LeadersTeachers

Hybrid fellowship for school teams (1 leader + 2–3 teacher-leaders). Teams specialize in educator workflows or student learning, culminating in a role-based showcase. Optional coaching available.

5 sessions (90 min) + 60–90 min async + optional coaching

Building Expertise

Building Internal Capacity with AI New

System & Org Leaders

Identify focus areas, explore training structures, and build an internal AI learning plan to take back to your system.

4 sessions (1.5 hrs) + 30–60 min async

Vibe Coding New

All Educators & Staff

Build with AI using plain-language prompting. Everyone leaves with a vibecoded solution to a real workflow. No tech background required.

1 launch (2.5 hrs) + 4 workshops (1.5 hrs) + 60–90 min async

Curriculum Internalization New

Instructional LeadsTeachers

Align and implement AI readiness curriculum to instructional goals with peer review, feedback cycles, and data analysis.

5 sessions (90 min) + 30–60 min async

Evaluating AI Tools New

Educators, School Teams & Leaders

Structured process for evaluating AI tools for school-based purposes. Participants complete mini tool tests and form context-specific adoption recommendations.

5 sessions (90 min) + ~60 min async

Asynchronous Courses

Self-paced · Virtual · No scheduling required

Teaching for Tomorrow

Teachers

~5 hours · Hosted on Mighty Network

Comprehensive course covering what AI is, how to analyze it critically, and how to build student AI literacy. Empowers educators to confidently use and teach AI.

What is AI?

TeachersLeaders

15 minutes · SCORM-compliant · Compatible with most LMS systems

Micro-course ideal for district-wide rollout. Introduces AI basics, explores risks, and models responsible use. Option to embed district-specific guidelines.

aiEDU

Strategic Advisory Services

Structured yet flexible pathways for districts to build AI Readiness — aligned to the AI Readiness Framework Rubric, so you can set measurable goals and track progress over time

programs@aiedu.org
www.aiedu.org

Every engagement begins with an AI Readiness Orientation & Goal Setting session — a 2-hour meeting to orient your leadership team to the District Readiness Rubric, map current readiness, and set priorities for the engagement.

AI Readiness Foundations

You’re looking to get a clear picture of where your system stands on AI readiness and build a shared, actionable strategy — with expert support to surface gaps, identify leverage points, and prioritize next steps.

Prerequisite: None

AI Readiness Assessment & Gap Analysis Action Planning

Timeline: 3–6 months

Policies & Guidelines

You’re ready to develop a clear, shared systemwide approach to AI — one that sets expectations for responsible use, earns genuine stakeholder buy-in, and is built to adapt as AI evolves.

Prerequisite: None • Available with or without a task force

Vision & Guiding Principles Policies & Guidelines Development Launch Strategy

Timeline: One full school year

Instructional Staff Learning

You’re ready to create meaningful, structured professional learning that helps instructional staff build AI readiness — with space for safe exploration and a process for developing shared instructional priorities.

Recommended prerequisite: Policies & Guidelines

Instructional Vision for AI Use Professional Learning Plan Curricular Resource Scope & Sequencing

Timeline: 6–9 months

AI Tool Evaluation & Selection

You want to bring clarity and coherence to your district’s AI tool landscape — developing a shared instructional vision and a principled, collaborative process for selecting tools that genuinely support student learning.

Recommended prerequisite: Policies & Guidelines • Available with or without a task force

Instructional Vision for AI Use Co-Developed Evaluation Criteria Tool Exploration & Assessment Implementation & Training Plan

Timeline: 6 months

Internal Learning Plan

You’re committed to building AI readiness across your entire district — including admin and operational staff — so all teams are aligned, confident, and moving forward together with a clear internal learning plan.

Recommended prerequisite: Policies & Guidelines

Five-Part Leadership Learning Series Internal Staff Learning Plan

Timeline: 3 months

How Every Engagement Works

Each engagement follows a structured three-phase model — ensuring every partnership is grounded in your district’s specific context and tied to measurable progress on the District AI Readiness Rubric.

Phase IOrient to the AI Readiness Framework, map your current readiness, and set goals for the engagement

Phase IICore work with leadership and stakeholders — developing plans, policies, or learning systems tailored to your goals

Phase IIIFinalize deliverables, reassess goals against the rubric, and conclude engagement with a clear path forward

Offerings can be combined into a multi-year partnership • Custom scopes available