Teacher PD Blueprint

EdSpeak • September 21, 2025

State ed-tech leaders say AI is now the top priority—with 26% naming it their most urgent issue, ahead of cybersecurity (21%) and professional development/tech support (18%). That’s the headline from this year’s SETDA survey and Education Week’s coverage, and it tracks with what we’re hearing from schools: it’s time to move from policy memos to practical teacher learning. See also SETDA’s press release.

In our last EdSpeak post, we framed the implications for districts and sustainability. Today, we translate that into a concrete, classroom-first professional development plan—what to teach teachers, how to structure it, and how to measure impact. Read the earlier post: “AI Just Became States’ #1 Ed-Tech Priority. What Should Schools Do Next?”

Principles for AI PD that actually helps teachers

1. Purpose before products. Start from the instructional job to be done (faster feedback on writing, small-group formation, differentiation)—not from tool tours.
2. Human-in-the-loop by design. Keep teacher judgment in charge; make AI assistance transparent and editable.
3. Privacy & security as habits, not disclaimers. Build lightweight data-safety routines into every module (no student PII in public models, role-based access, deletion timelines).
4. Job-embedded learning. Learn it today, use it tomorrow, reflect next week.
5. Equity & accessibility. Check for bias, readability, language supports, and accommodations in every workflow.

The PD framework (K–12, adaptable by grade band)

Module A — AI Literacy for Educators (2 hours)

• What generative AI is/does/doesn’t do; strengths/limits; citation and verification routines.
• Classroom guardrails: disclosure to students, “show-your-sources,” and academic integrity norms.
• Quick wins: draft → revise → cite workflow for lesson plans and family communications.

Outcome: Teachers can explain AI clearly to students and families; they adopt a “transparent use + human oversight” stance.

Module B — Planning & Feedback Time-Savers (2 hours)

• Generate and edit exemplars, rubrics, and feedback stems; convert rubrics into student-friendly checklists.
• Speed up writing feedback cycles (comment banks + revision prompts); summarize exit tickets into small-group targets.

Outcome: Planning minutes saved; faster feedback loop visible in student drafts by week 2.

Module C — Differentiation & Small-Group Instruction (2 hours)

• Build leveled texts, vocabulary supports, and choice boards aligned to your standards/pacing.
• Use evidence (checks for understanding) to regroup students; keep teacher override central.

Outcome: More minutes of targeted small-group instruction per week; clearer next steps posted for students.

Module D — Safe & Sustainable Use (90 minutes)

• Data practices: local vs. cloud tools, what not to paste, anonymization, and deletion.
• Vendor checklist (privacy, security posture, model transparency, accessibility) teachers and coaches can use.

Module E — Students as Responsible Users (90 minutes)

• Teach students to prompt, verify, cite, and reflect (metacognition prompts); accommodations/UDL supports.
• Short routines: “Show your thinking,” “Compare and critique two outputs,” and “Fact-check + source.”

Outcome: Student AI use is explicit, source-aware, and integrated into content learning (not siloed).

A 6–8 week PD arc (job-embedded)

Week 0 (Leaders/Coaches): Pick 2–3 high-impact use cases per grade band; set metrics (minutes saved; feedback turnaround; small-group minutes; one growth signal).

Weeks 1–2: Modules A–B with in-class try-outs.
Weeks 3–4: Module C with small-group routines; collect artifacts (before/after lessons, student work).
Week 5: Module D safety clinic; finalize the vendor/data checklist teachers can use locally.
Week 6: Module E student-facing routines; classroom walkthroughs focused on feedback cycles.
Weeks 7–8: Share results; decide what to standardize and what to sunset.

Deliverables: a teacher prompt & feedback library (editable); small-group planning templates; a one-page AI disclosure & integrity statement for families/students; and the privacy & procurement checklist staff actually use.

What to measure (simple, credible, fast)

• Time saved in planning/feedback (self-report + calendar sample).
• Feedback speed/quality (turnaround time; student revision rates).
• Targeted instruction minutes (small-group time logged weekly).
• Student growth signals tied to the use case (e.g., writing rubric dimensions, reading fluency).
• Equity checks: Are supports reaching multilingual learners and students with IEPs/504s?

K–12 grade-band tweaks

• K–2: Picture-rich prompts, phonics/decodables generation, family notes in plain language.
• 3–5: Summarizing strategies, leveled passages, vocabulary games; peer feedback stems.
• 6–8: Argument writing supports, lab report scaffolds, choice boards; explicit citation habits.
• 9–12: Discipline-specific writing, research verification, code comments/testing (where relevant); career-connected tasks.

Leaders: set teachers up to win

• Publish a short responsible use policy and an approved tools list that match the PD.
• Offer release time or micro-stipends for the try-out cycle.
• Negotiate districtwide SSO/rostering and clear data-retention terms (reduce friction and risk).

Want a head start?

In our previous post we shared a practical roadmap for districts moving from pilots to policy. This PD blueprint turns that roadmap into teacher moves—ready to run in your next cycle.

Sources & further reading

• Education Week: “We’re Entering a New Phase of AI in Schools. How Are States Responding?”
• SETDA Press Release (2025): “Artificial Intelligence Tops List of State EdTech Priorities for the First Time.”
• SchoolWorks Lab (EdSpeak): “AI Just Became States’ #1 Ed-Tech Priority. What Should Schools Do Next?”