AI Literacy PD for Teachers: A 90-Minute Agenda You Can Run

The directive arrived in your inbox on a Tuesday afternoon in June — “Please plan something on AI for the department before September” — and the meeting is already on the calendar for late August. You are not an AI enthusiast. You are a department head or instructional coach who also has curriculum maps to update, a co-teacher schedule to finalize, and colleagues who range from politely skeptical to openly annoyed at the mention of ChatGPT. The last thing you need is a vendor workshop that costs money and lands zero usable takeaways.

Here is the thing: you can run a defensible, standards-anchored AI literacy PD session yourself. And the materials worth trusting for that session come from someone who has actually taught students, not just read the marketing deck.

TL;DR: AI literacy PD for teachers works best as a short, hands-on session built around a clear framework, not a lecture. Anchor the content to the AI4K12 Five Big Ideas, give teachers time to try a tool and connect it to their own subject, and end with a concrete classroom plan. A 90-minute agenda — welcome, AI basics, hands-on try, standards connection, subject application, debrief — covers what a mixed grades 6-12 department needs. The most useful PD materials come from someone who has actually taught and done the AI research.

What should teachers learn in an AI professional development session?

The answer is not “everything about AI.” It is the five foundational ideas teachers need in order to have honest, accurate conversations with students — and with parents who ask questions after dinner.

The AI4K12 Five Big Ideas are the clearest content skeleton available for this work. Developed by a consortium of CS education researchers and adopted by districts across the country, they map directly to what a grades 6-12 classroom teacher needs to know.

• AI4K12 Big Idea #1 — Perception: AI systems sense the world through data inputs — images, text, audio, numbers. Teachers need to understand that AI “sees” the world through datasets, not experience.
• AI4K12 Big Idea #2 — Representation and Reasoning: AI represents knowledge internally and uses it to draw conclusions. This is why an AI can sound confident while being completely wrong — it reasons from patterns, not understanding.
• AI4K12 Big Idea #3 — Learning: AI systems learn from data. Who collected that data, and whose experiences are in it? That question is the gateway to bias conversations.
• AI4K12 Big Idea #4 — Natural Interaction: Humans communicate with AI through language, images, and voice. Prompt writing is the skill students are actually practicing when they type into a chatbot.
• AI4K12 Big Idea #5 — Societal Impact: AI changes who has access to what, who gets surveilled, and whose work gets displaced. This is where the classroom meets citizenship.

The responsible-use piece — acceptable use, ethical behavior, data privacy — maps cleanly to ISTE 1.2.b, which calls on students to engage in positive, safe, legal, and ethical behavior as digital citizens. Teachers cannot lead that conversation credibly if they have never thought through the question themselves. The PD session is where teachers do the thinking first.

A print-ready 90-minute AI literacy PD agenda

This is the AI PD agenda for teachers that works in a mixed department with a range of comfort levels. Six blocks, 90 minutes total. You can facilitate this without a consultant.

Time	Block	What the facilitator does	Leave-behind
0:00 – 0:10	Welcome + why now	Name the reality: AI tools are already in students’ hands. Acknowledge that not everyone is excited about this PD. Introduce the AI4K12 Five Big Ideas as the content frame for the session.	One-page Big Ideas reference card
0:10 – 0:30	AI basics: how it works, hallucination, bias	Walk through Big Ideas #1–3 using plain analogies (autocomplete on steroids; trained on human-written text, so human bias is baked in; confident-sounding wrong answers are a feature, not a glitch). Show a live or printed hallucination example — a plausible-sounding citation that does not exist.	Hallucination example handout
0:30 – 0:50	Hands-on: everyone tries one prompt	Teachers open a free AI tool (no account required) or work from a printed prompt card if devices are unavailable. Each person types the same prompt — the same-prompt evaluation exercise appears further down. Debrief in pairs: what surprised you?	Prompt card + observation notes sheet
0:50 – 1:05	Standards connection	Show the standards crosswalk from the next section. Explain that AI literacy is not an add-on — it lives inside standards teachers already have. Connect Big Idea #4 (Natural Interaction) to ELA writing standards; Big Idea #5 (Societal Impact) to social studies civics.	Standards crosswalk one-pager
1:05 – 1:25	Subject application: plan one classroom move	Each teacher writes one sentence: “In my [subject] class, students will [do what] with AI, connected to [standard].” Table groups share. A department head could collect these and use them as the school’s initial AI-use documentation.	Subject-application planning sheet
1:25 – 1:30	Debrief + one commitment	Each person says one sentence aloud: “One thing I will do differently after today is ___.” Record on a shared document. This becomes your post-PD accountability artifact for admin.	Shared commitment log

For a ready-made curriculum teachers can take directly from this PD into their classrooms, the year-long AI literacy curriculum for middle school is a natural leave-behind for the standards-connection block — it gives teachers a paced scope and sequence they can plug into their existing planning.

Department heads running the middle school track might also look at the MS AI Department Head Premium Pack (PD + Curriculum + Admin Tools), which bundles the PD facilitation guide, student curriculum, and admin documentation templates into one place. The high school equivalent is the HS AI Department Head Premium Pack.

Map your PD to the standards so you can defend it to admin

An admin-facing crosswalk matters for one reason: when your principal asks “what standards does this professional development address?”, you need an answer that takes ten seconds to deliver, not ten minutes. A table beats a paragraph every time.

The table below maps each PD block to the ISTE Standards for Educators (by name and number), the ISTE Student Standard the block prepares teachers to deliver, and the AI4K12 Big Idea it covers.

PD Block	ISTE Standards for Educators	ISTE Student Standard teachers will deliver	AI4K12 Big Idea
AI basics: hallucination + bias	2.1 Learner (teachers pursue growth to improve practice)	ISTE 1.3.b (evaluate accuracy, perspective, credibility, relevance)	Big Idea #2 Representation & Reasoning; Big Idea #3 Learning
Hands-on prompt try	2.5 Designer (design authentic learning experiences)	ISTE 1.3.d (build knowledge by actively exploring real-world problems)	Big Idea #4 Natural Interaction
Standards connection	2.1 Learner; 2.5 Designer	ISTE 1.3.d; ISTE 1.3.b	Big Idea #1 Perception
Responsible use framing	2.3 Citizen (model digital citizenship)	ISTE 1.2.b (positive, safe, legal, ethical behavior)	Big Idea #5 Societal Impact
Subject application planning	2.5 Designer	ISTE 1.3.d	Big Ideas #4 + #5

This crosswalk is the document you hand your principal when the walk-through question comes. It also protects you: if a parent or school board member asks whether teachers are addressing AI responsibly, you have a named-framework answer ready.

Run one activity live: the same prompt, four evaluations

The most effective single activity for a skeptical mixed department is one where every teacher does the same thing at the same time and then compares results. No login required if you use printed output cards.

The prompt (give this to everyone, verbatim):

“Explain the causes of World War I in three paragraphs for a 9th-grade history student.”

Run the prompt in any available AI tool, or distribute a pre-printed AI response if devices are scarce. Then give teachers four evaluation questions pulled directly from ISTE 1.3.b — which asks students to evaluate accuracy, perspective, credibility, and relevance:

1. Accuracy: Is every factual claim in this response verifiable? Mark any claim you would want to check before using it in class.
2. Perspective: Whose viewpoint is centered in this account? Is the Austro-Hungarian perspective present? The Serbian perspective?
3. Credibility: If a student submitted this as research evidence, what source citation would accompany it? What is the credibility of “an AI-generated response”?
4. Relevance: Is this response actually calibrated for a 9th grader, or is the vocabulary level off? What revision would make it more relevant to the intended audience?

The debrief produces the same insight every time: the AI response looks authoritative, reads fluently, and contains real gaps. Teachers leave with direct experience of what a student sees when they use AI for homework — not a theoretical concern, but a felt one. A social-studies teacher who has personally found a perspective gap in an AI history response is far more likely to teach students to look for that same gap than one who only heard about it in a slide deck.

For deeper ISTE standards activities that carry this exercise into the student-facing classroom, see the ISTE AI standards activities for middle school.

Show AI literacy across subjects

A mixed ELA-plus-social-studies-plus-science department is the common case, not the exception. AI literacy is not a discipline — it applies inside every subject’s existing work. The goal in this block is to give each content area one concrete move they can actually plan.

ELA — source evaluation on AI summaries

A department head could frame this as an extension of close-reading work students already do. Give students an AI-generated summary of a novel chapter alongside the original text. The task: identify three places where the summary omits, flattens, or misrepresents the original. This is ISTE 1.3.b (evaluate accuracy, perspective, credibility, relevance) inside a familiar ELA context. Students are doing literary analysis; the AI output is the text under scrutiny.

Social studies — detecting bias and representation in AI historical accounts

AI systems generate historical accounts by pattern-matching on text that was predominantly written by certain voices in certain languages. When you ask an AI to describe daily life in the Ottoman Empire in 1850, the response reflects whose accounts were digitized, indexed, and included in training data. A social-studies teacher could prompt students: “Ask the AI about the same historical event from two different geographic perspectives. Where do the accounts diverge? Why might they?” This directly addresses AI4K12 Big Idea #2 (Representation and Reasoning) and aligns to the historical-thinking standards most social-studies departments already document.

Science — how AI models learn from data (Big Idea #3)

AI4K12 Big Idea #3 — Learning — is the most directly teachable in a science classroom because it maps to scientific-method thinking. Science teachers can introduce the concept of a training dataset as analogous to an experiment: you get results that reflect your sample. If your sample was biased (incomplete, skewed, non-representative), your results carry that bias. A concrete version: show students how a medical AI trained primarily on adult male data produces less accurate results for other populations. The question is not “is AI bad?” — it is “what does the data tell us about the training set?” That is a scientific-reasoning question science teachers are already asking.

For teachers wanting to go deeper on the subject-specific applications — including lesson plans organized by content area — browsing the standards-aligned lessons in the shop gives a quick picture of what grade 6-12 AI literacy looks like built into existing course content.

Who should build (or buy) your AI PD materials?

This is the honest decision aid most PD planning guides skip.

A generic vendor AI workshop gives you a polished slide deck, a keynote speaker who has never met your students, and takeaways calibrated to a corporate upskilling audience. The activity examples involve job performance and productivity metrics. Your ELA colleagues come back with a single sticky note that says “AI is the future” and nothing to teach on Monday.

Teacher-built materials look different. They start with the actual classroom situation — the source evaluation lesson that already lives in unit 3, the research project that always produces a plagiarism conversation, the sub plan that needs to survive a day without you. AI literacy PD built from that starting point produces things teachers can use immediately.

The trust question is simple: who did the research? A credible AI PD resource cites the actual ISTE standard codes, not just “21st-century skills.” It names the AI4K12 Big Ideas by number, not just “critical thinking about technology.” It gives you a hallucination example you can print and use on day one, not a slide about how hallucination is “an emerging challenge.”

Maya is a former teacher who did that research — built the curriculum first in a real classroom context, then documented the standards alignment, then made the materials available to other departments navigating the same questions you are. If you are looking for materials that can anchor your department’s AI PD and carry into student-facing lessons afterward, the MS AI Department Head Premium Pack and HS AI Department Head Premium Pack are built for exactly this situation. For a department head who wants to build toward a documented school-wide AI literacy leadership role, the AI Teacher Leadership Certification (6-Month Program) gives the full arc: PD facilitation, curriculum design, admin documentation, and a capstone.

You have eight weeks before September. The meeting is already on the calendar. The session above is one you can run yourself, with materials you can trust, anchored to standards you can name. That is what AI literacy PD for teachers 2026 actually looks like — not a vendor event, but a department conversation facilitated by someone who knows the work.

This post was drafted with AI assistance and human-finalized.