What is this course about?
AI literacy taught as a one-off unit does not stick. This course builds a vertical progression: what a second grader can understand about prediction, what a middle schooler should know about training data and bias, and what a high school student needs about verification and disclosure — integrated across subjects rather than bolted on.
Who is this course for?
- Curriculum directors and instructional leaders
- Library media specialists and technology teachers
- Grade-band teams designing vertical alignment
- CTE coordinators
What do I need before starting?
- Familiarity with your district's curriculum planning process
- No technical background required
What will I be able to do afterwards?
- Define AI literacy concretely at each developmental stage
- Design activities that make abstract concepts tangible per grade band
- Integrate AI literacy across subjects rather than as a separate course
- Connect high school AI literacy to CTE pathways
- Assess AI literacy through performance rather than a multiple-choice test
What does each module cover?
What does AI literacy mean at each stage?
40 minA developmental framework that avoids both oversimplification and abstraction beyond reach.
Objectives
- Define AI literacy components by developmental stage
- Avoid concepts that are developmentally out of reach
- Set the vertical progression's endpoints
Topics
Activity. Place twenty AI concepts on a developmental progression and defend the boundaries.
What can elementary students actually understand?
45 minPattern, prediction, and 'the computer is guessing' — taught through unplugged activities.
Objectives
- Teach prediction through unplugged activities
- Introduce the idea that computers guess and can be wrong
- Avoid anthropomorphizing the technology
Topics
Activity. Design and test an unplugged prediction activity with an elementary class.
What should middle schoolers know about training data?
45 minWhere answers come from, why bias exists, and the first real critical evaluation.
Objectives
- Teach training data as the source of model behavior
- Make bias concrete through hands-on comparison
- Introduce critical evaluation of output
Topics
Activity. Design an activity where students find bias in model output themselves.
What do high school students need?
45 minVerification, disclosure norms, and understanding model limits well enough to use them responsibly.
Objectives
- Teach systematic verification of model claims
- Establish disclosure norms as a transferable practice
- Teach model limits precisely enough to be useful
Topics
Activity. Design a verification exercise where students fact-check model output against primary sources.
How do you integrate across subjects?
45 minCross-curricular integration instead of a standalone unit nobody has room for.
Objectives
- Map AI literacy components to existing subject standards
- Design integrated rather than additive activities
- Secure subject-teacher buy-in
Topics
Activity. Map the progression onto three subjects' existing standards.
How does this connect to CTE pathways?
40 minCareer and technical education routes into AI-adjacent work, without overpromising.
Objectives
- Identify realistic CTE pathways connected to AI skills
- Connect classroom literacy to workplace competency
- Avoid overpromising about the job market
Topics
Activity. Map one CTE pathway from a middle school entry point to a credential.
How do you assess AI literacy?
45 minPerformance assessment rather than recall, because the recall version measures nothing.
Objectives
- Design performance assessment per grade band
- Assess judgment rather than terminology
- Build rubrics that transfer across teachers
Topics
Activity. Design a performance assessment for one grade band with a transferable rubric.
Assembling the district scope and sequence
55 minThe workshop module: a complete K-12 progression document ready for curriculum review.
Objectives
- Assemble the full K-12 progression
- Verify vertical alignment across bands
- Prepare it for curriculum council review
Topics
Activity. Assemble the scope and sequence and check every band boundary for gaps and overlaps.
What is the capstone project?
District K-12 AI literacy scope and sequence
Produce a complete vertically-aligned AI literacy progression with grade-band components, integrated activities mapped to subject standards, CTE pathway connections, and performance assessments with rubrics.
Deliverable: A scope and sequence document ready for curriculum council adoption.
How are learners assessed?
- Vertical alignment checked for gaps and unintended repetition
- Activities piloted with real students where possible
- Assessment rubrics tested for consistency across two raters
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for k-12.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- ISTE
International Society for Technology in Education
Student technology standards forming the progression's backbone.
- CoSN
Consortium for School Networking
District-level guidance on technology literacy implementation.
- Office of Educational Technology
U.S. Department of Education
Federal framing for AI literacy in K-12.
- AI Index Report
Stanford HAI
Current capability data so the curriculum reflects what models actually do.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Elementary activities must be genuinely unplugged and classroom-tested. Screen-based activities for young children raise separate policy questions this course should not smuggle past a district.
- Module 3's bias demonstration must be one students discover, not one a teacher narrates. Build it against a model where the bias is reliably reproducible.
- Module 6 should resist career hype. AI job-market claims age badly; frame pathways in terms of durable skills rather than projected demand figures.
- Curriculum directors need the standards crosswalk more than the activities. Make Module 5's mapping the polished artifact.
- Re-verify capability claims at each revision. A progression built on 2024 model limitations teaches students something false about 2026 models.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Teaching AI Literacy: A K-12 Scope and Sequence course cover?
AI literacy taught as a one-off unit does not stick. This course builds a vertical progression: what a second grader can understand about prediction, what a middle schooler should know about training data and bias, and what a high school student needs about verification and disclosure — integrated across subjects rather than bolted on. It runs 5 hours across 8 modules across 8 modules, at foundational level, and closes with a capstone: District K-12 AI literacy scope and sequence.
Who should take Teaching AI Literacy: A K-12 Scope and Sequence?
It is written for Curriculum directors and instructional leaders, Library media specialists and technology teachers, Grade-band teams designing vertical alignment, CTE coordinators. Prerequisites: Familiarity with your district's curriculum planning process; No technical background required.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for k-12 teams that cannot send work to a public AI tool.
How do we get access to Teaching AI Literacy: A K-12 Scope and Sequence?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for k-12 cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.