AI for Teachers 2026: 7-Day K-12 Classroom Starter Course

Eight core understandings that form the foundation of AI literacy for any teacher in any subject. Not a tech checklist — a thinking framework for navigating AI confidently, critically, and ethically.
? Objectives

• Name and briefly describe all 8 essentials

• Explain why each matters for classroom practice

• Identify which 2–3 feel most relevant to your subject/grade

• Understand how these 8 form a connected framework, not a list

The critical thinking, source evaluation, and questioning skills that make great teachers great are exactly the skills students need to navigate AI. This lesson reframes what you already do — and why it matters more than any new tool you might learn.
? Objectives

• Identify 3–4 teaching skills that transfer directly to AI literacy

• Articulate why deep subject knowledge is an AI-literacy asset

• Feel confidence replacing any residual impostor syndrome

• Commit to one "By the Way" lesson concept to try this week

AI reflects the biases of the data it was trained on — which means the biases of human society, at scale. Understanding how bias shows up in AI, and having language to discuss it with students, is one of the most important things a teacher can take away from this course.
? Objectives

• Define AI bias in plain terms and explain its origin

• Identify at least 3 domains where AI bias shows up

• Use the ABC critique framework to surface bias in an AI output

• Have one classroom-ready phrase for introducing this topic

This lesson connects the big-picture research on 21st century education to the everyday reality of your classroom, and makes the case for why what you already teach matters more than ever.
? Objectives

• Describe the 4-dimensional model of education (Knowledge, Skills, Character, Meta-learning)

• Explain why adaptability and transfer matter more than content recall in an AI age

• Connect your current teaching to at least one of the four dimensions

• Articulate why good teaching is the antidote to AI displacement of learning

The first prompt experience is different for everyone. This discussion thread is where honest reactions — skepticism very much included — become part of the learning for the whole group.

AI tools are data-hungry by design. When teachers use them — even with the best intentions — the stakes for students are real. This lesson sets the stage for why the rules in today's section aren't bureaucratic formalities. They're protections your students depend on.
? Objectives

• Understand why AI tools create new privacy risks that older EdTech didn't

• See the connection between AI training data and student data exposure

• Feel the personal stakes — not just the policy stakes — of this issue

• Be motivated and ready for the legal specifics in lessons 3.2–3.4

The Family Educational Rights and Privacy Act protects every student in your class. Here's what it actually means — in plain English — and exactly how it applies when AI tools are in the picture.
? Objectives

• Define FERPA and the two things it does

• List what counts as a "protected education record"

• Apply FERPA to 4 specific AI-use scenarios

• Know the difference between consumer and licensed AI tools re: FERPA

• Have one rule-of-thumb to use every time before prompting

The Children's Online Privacy Protection Act places strict limits on data collection from children under 13 — and nearly every major consumer AI tool falls under its requirements. What elementary and middle school teachers need to know before letting students anywhere near an AI interface.
? Objectives

• Define COPPA and its age threshold in plain terms

• Explain why most consumer AI tools cannot legally be used with students under 13

• Distinguish between tools that have COPPA compliance and those that don't

• Know what parental consent actually looks like in an educational context

New York's Education Law 2-d builds on federal protections with state-specific requirements — including the Parents Bill of Rights for Data Privacy and mandatory Data Privacy Agreements for any vendor handling student data. Here's what NYS educators need to know and do.

? Objectives

• Define NYS Education Law 2-d and the two key mechanisms it creates

• Understand what a Data Privacy Agreement (DPA) is and why it matters

• Know what the Parents Bill of Rights requires schools to communicate

• Identify your specific responsibility as a classroom teacher under 2-d

• Know what BOCES offers to districts regarding DPAs and AI tools

The practical payoff from three lessons of legal grounding. A clear, memorable list of what constitutes Personally Identifiable Information (PII) and how to get the same value from AI tools without ever putting student data at risk.
? Objectives

• Define PII and recognize it across different categories

• Identify what belongs on the "never enter" list and why

• Rewrite a problematic prompt into a safe, equally useful one

• Distinguish consumer tools (no DPA) from licensed tools (with DPA) in practice

How to set up clear, simple norms with students around AI and privacy — including age-appropriate language for why data privacy matters, three policy principles any teacher can implement tomorrow, and the difference between a classroom norm and a school policy.

? Objectives

• Distinguish between a classroom AI norm and a district AI policy

• Implement three classroom-level privacy protections immediately

• Explain data privacy to students at an age-appropriate level

• Model AI transparency as a professional practice

Parents have real questions — and real fears — about AI in classrooms. This lesson gives you the talking points, the framing, and a sample parent communication you can adapt and send this week. Proactive transparency builds trust. Silence creates fear.

? Objectives

• Anticipate and address the 4 most common parent concerns about AI

• Frame AI in your classroom proactively — before families hear about it elsewhere

• Use the sample family communication template to draft your own message

• Know what you are — and are not — required to communicate about AI tool use

The most effective use of AI for teachers isn't replacing your lessons — it's planning them faster and more creatively. This lesson reframes AI from "threat to teaching" to "teaching assistant you never had," with concrete examples across the planning workflow.

? Objectives

• Identify at least 4 planning tasks where AI saves meaningful time

• Articulate the difference between AI as replacement vs. AI as amplifier

• Try at least one planning prompt before the end of the lesson

• Know the one question to always ask after any AI planning output

You don't need an "AI unit." You need micro-moments inside the lessons you already teach. The Sprinkle Method and the "Be the Bot" activity give you a concrete, low-prep framework for embedding AI literacy into any subject — without rebuilding your curriculum from scratch.

? Objectives

• Explain the Sprinkle Method in one sentence

• Walk through all four steps of a "Be the Bot" activity

• Identify one lesson in your current unit where it would fit

• Write one "Be the Bot" style question for your subject

Specific, ready-to-use AI integration examples for ELA, math, science, social studies, world languages, arts, health, and elementary grades. Not abstract frameworks — actual prompts, activities, and conversation starters you can adapt for this week.

? Objectives

• Identify at least 2 AI integration ideas directly relevant to your subject

• Adapt one example to fit a unit you're currently teaching

• See how the ABC Rule applies across different content areas

• Leave with 1–2 ideas ready to try this week

Always Be Critiquing. The ABC Rule gives students — at any grade level, in any subject — a structured, memorable framework for evaluating AI outputs. This lesson walks through the framework, explains the "bullseye" critique method, and gives you scripts for introducing it at different grade levels.

? Objectives

• Explain the ABC Rule in one sentence students will remember

• Apply all three ABC dimensions to a sample AI output

• Use the bullseye method to move critique between specific and general

• Introduce the ABC Rule to students at an appropriate grade level

AI will fail you in class. It's not a matter of if — it's when. This lesson reframes that inevitability as one of the most powerful AI literacy opportunities available to teachers, with concrete language for turning an in-class AI failure into an unforgettable lesson.

? Objectives

• Reframe AI failures as teaching opportunities rather than embarrassments

• Have ready language for responding to an AI misfire in real time

• Understand why deliberately imperfect prompts are a valid pedagogical tool

• Model intellectual honesty about AI limitations for students

Top-down AI rules produce compliance at best and evasion at worst. Co-created norms — developed through honest conversation with students — produce genuine ownership. This lesson walks through the step-by-step process for building that culture, including the AI Writing Spectrum and how to handle the messy implementation phase.

? Objectives

• Distinguish between rules imposed on students and norms co-created with them

• Walk through the 5-step norm co-creation process

• Apply the AI Writing Spectrum to at least one of your own assignments

• Know how to handle the messy implementation phase honestly

The most durable response to AI and academic integrity isn't detection — it's design. When assignments require authentic human engagement, AI becomes irrelevant as a shortcut. Three specific design principles you can apply to assignments you're already giving.

? Objectives

• Identify why AI detectors always lose in the long run

• Apply all three process-based assignment design principles

• Adapt one existing assignment using at least one principle

• Implement the "defend your thinking" layer with minimal disruption

Scripts, sentence starters, and real classroom language for talking directly with students about why their own thinking matters — not because it's the rule, but because it's what education is actually for. This lesson gives you the words for the conversation most teachers have been avoiding.

? Objectives

• Have ready language for 4 common student integrity situations

• Articulate the "learning vs. producing" distinction to students

• Model personal AI use transparently as an integrity practice

• Lead one honest classroom conversation about AI and learning this week

AI can personalize learning at scale in genuinely useful ways. But personalization without relationship is hollow — and the most important thing about every student remains something no algorithm can fully know. This lesson holds both truths simultaneously.

? Objectives

• Articulate the genuine personalization value AI offers teachers

• Explain Ken Shelton's caveat: "You have to know the person"

• Identify where AI personalization helps — and where it falls short

• Connect personalization to equity: who benefits most, and who might be left out

Too much AI use erodes the human qualities that make learning meaningful. Too little wastes time and misses genuine opportunity. The ability to navigate that balance — for teachers and students alike — is itself a teachable, learnable skill.

? Objectives

• Describe the consequences of too much and too little AI use

• Explain the "shortcut psychology" of AI and why it's by design

• Articulate why cognitive struggle is worth protecting

• Give students the language for their own balance decisions

Empathy. Ethical reasoning. Contextual judgment. The capacity to build relationships and be known by another person. These are the qualities that make humans irreplaceable — and they're precisely the ones that atrophy when AI does too much of the work. This lesson makes the affirmative case for great teaching.

? Objectives

• Identify what machines do well vs. what humans do well — the research-grounded list

• Describe the T-shaped and M-shaped learner models

• Explain why character, empathy, and ethical reasoning remain irreplaceable

• Make the affirmative case for great teaching in one compelling sentence

The specific skills AI will displace in the future are largely unknown. But the research is clear on what remains valuable regardless: adaptability, broad foundations, deep curiosity, and the capacity to keep learning throughout life. This lesson closes Day 6 with a hopeful, practical, research-grounded vision of what education is for.

? Objectives

• Describe the T-shaped and M-shaped learner and why adaptability is the core

• Explain why curiosity is the "superpower" for an uncertain future

• Articulate what "expert amateurism" means and why it matters more than expertise alone

• Connect today's teaching decisions to long-term student readiness

You've just spent a week building something most of your colleagues don't yet have: a grounded, practical, legally-informed understanding of AI in education. This lesson gives you the language, the posture, and the specific scripts for sharing that without coming across as an AI evangelist — or making skeptics feel left behind.

? Objectives

• Approach colleagues from a place of invitation, not evangelism

• Have ready language for 4 common colleague reactions to AI

• Know when to speak up about privacy concerns — and how

• Model the "curious, critical, transparent" approach for your school community

A realistic, sustainable 30-day plan built from everything this week produced. Not an overwhelming list — three weeks of specific, doable actions that build the habits of AI-literate teaching without adding unmanageable new demands to your already full plate.

? Objectives

• Commit to 5 specific actions across the next 30 days

• Know exactly what each week's focus is and why

• Identify at least one resource for continuing the learning beyond this course

• Feel the week's learning crystallize into confident forward motion