# Teaching AI Literacy: A K-12 Scope and Sequence > K-12 · AI Course · K12-6 > Source: https://ibl.ai/solutions/k-12/course/k12-ai-literacy-scope-and-sequence > Last updated: 2026-08-25 **A vertically-aligned AI literacy progression from elementary through high school — what to teach at each band, and the activities that make it concrete.** ## The Short Answer **AI literacy works as a vertical progression across grade bands, not a standalone unit. ibl.ai gives districts a platform to teach it on that they control themselves — self-hosted, model-agnostic, and yours because you own all the code and the data, so students can experiment with real models without their prompts leaving the district.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore K-12](https://ibl.ai/solutions/k-12) ## Course facts - **Level:** Foundational - **Duration:** 5 hours across 8 modules - **Format:** Curriculum design workshop - **Modules:** 8 - **Catalog code:** K12-6 - **Frameworks covered:** ISTE Standards, State academic standards, CTE frameworks ## 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? ### Module 1 — What does AI literacy mean at each stage? A developmental framework that avoids both oversimplification and abstraction beyond reach. _(40 min)_ **Objectives** - Define AI literacy components by developmental stage - Avoid concepts that are developmentally out of reach - Set the vertical progression's endpoints **Topics:** Developmental framework · Component definition · Progression endpoints · Common overreach **Activity:** Place twenty AI concepts on a developmental progression and defend the boundaries. ### Module 2 — What can elementary students actually understand? Pattern, prediction, and 'the computer is guessing' — taught through unplugged activities. _(45 min)_ **Objectives** - Teach prediction through unplugged activities - Introduce the idea that computers guess and can be wrong - Avoid anthropomorphizing the technology **Topics:** Unplugged activities · Prediction concepts · Fallibility · Anthropomorphism avoidance **Activity:** Design and test an unplugged prediction activity with an elementary class. ### Module 3 — What should middle schoolers know about training data? Where answers come from, why bias exists, and the first real critical evaluation. _(45 min)_ **Objectives** - Teach training data as the source of model behavior - Make bias concrete through hands-on comparison - Introduce critical evaluation of output **Topics:** Training data concepts · Concrete bias demonstration · Critical evaluation · Source questioning **Activity:** Design an activity where students find bias in model output themselves. ### Module 4 — What do high school students need? Verification, disclosure norms, and understanding model limits well enough to use them responsibly. _(45 min)_ **Objectives** - Teach systematic verification of model claims - Establish disclosure norms as a transferable practice - Teach model limits precisely enough to be useful **Topics:** Verification practice · Disclosure norms · Limit understanding · Responsible use **Activity:** Design a verification exercise where students fact-check model output against primary sources. ### Module 5 — How do you integrate across subjects? Cross-curricular integration instead of a standalone unit nobody has room for. _(45 min)_ **Objectives** - Map AI literacy components to existing subject standards - Design integrated rather than additive activities - Secure subject-teacher buy-in **Topics:** Standards mapping · Integrated design · Additive versus integrated · Teacher buy-in **Activity:** Map the progression onto three subjects' existing standards. ### Module 6 — How does this connect to CTE pathways? Career and technical education routes into AI-adjacent work, without overpromising. _(40 min)_ **Objectives** - Identify realistic CTE pathways connected to AI skills - Connect classroom literacy to workplace competency - Avoid overpromising about the job market **Topics:** CTE pathway mapping · Workplace competency · Industry connection · Realistic framing **Activity:** Map one CTE pathway from a middle school entry point to a credential. ### Module 7 — How do you assess AI literacy? Performance assessment rather than recall, because the recall version measures nothing. _(45 min)_ **Objectives** - Design performance assessment per grade band - Assess judgment rather than terminology - Build rubrics that transfer across teachers **Topics:** Performance assessment · Judgment measurement · Transferable rubrics · Recall test limits **Activity:** Design a performance assessment for one grade band with a transferable rubric. ### Module 8 — Assembling the district scope and sequence The workshop module: a complete K-12 progression document ready for curriculum review. _(55 min)_ **Objectives** - Assemble the full K-12 progression - Verify vertical alignment across bands - Prepare it for curriculum council review **Topics:** Progression assembly · Vertical verification · Review preparation · Implementation sequencing **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? - [Curriculum Alignment Agent](https://ibl.ai/solutions/k-12/agent/curriculum-alignment-agent) - [Content Creation Agent](https://ibl.ai/solutions/k-12/agent/content-creation-agent) - [Professional Development Agent](https://ibl.ai/solutions/k-12/agent/professional-development-agent) - [Research Agent](https://ibl.ai/solutions/k-12/agent/research-agent) ## 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](https://iste.org/) — International Society for Technology in Education. Student technology standards forming the progression's backbone. - [CoSN](https://www.cosn.org/) — Consortium for School Networking. District-level guidance on technology literacy implementation. - [Office of Educational Technology](https://tech.ed.gov/) — U.S. Department of Education. Federal framing for AI literacy in K-12. - [AI Index Report](https://hai.stanford.edu/ai-index) — 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. ## More K-12 courses - [The K-12 AI Compliance Stack: FERPA, COPPA, and CIPA](https://ibl.ai/solutions/k-12/course/k12-ai-compliance-stack): The three federal rules governing AI in a district, what each requires of a vendor, and the deployment architecture that satisfies all three at once. - [Safe AI Tutoring for Minors: Guardrails and Escalation](https://ibl.ai/solutions/k-12/course/safe-ai-tutoring-for-minors): Building a tutoring agent for children — content moderation, self-harm escalation, grooming-pattern detection, and the mandatory-reporter workflow behind it. - [AI Lesson Planning Aligned to State Standards](https://ibl.ai/solutions/k-12/course/ai-lesson-planning-standards-aligned): Generate standards-aligned lessons that survive a curriculum audit — grounded on your state's standards, your adopted materials, and your scope and sequence. - [AI in the IEP Process: Drafting, Compliance, and the Human Signature](https://ibl.ai/solutions/k-12/course/ai-in-the-iep-process): Cut IEP paperwork without ceding a legally binding decision — drafting present levels, goal writing, and the IDEA requirements no agent can satisfy for you. - [Academic Integrity and Assessment in K-12](https://ibl.ai/solutions/k-12/course/academic-integrity-and-assessment-k12): What to do when every student has a writing machine — grade-band policy, assessment redesign, and why detection tools create more problems than they solve. - [District AI Procurement: Evaluating Vendors and Contracts](https://ibl.ai/solutions/k-12/course/district-ai-procurement): A procurement process a school board will approve and a privacy officer will sign — evaluation rubric, contract clauses, and the questions vendors dodge.