Overview

AI can reduce busywork (planning, drafting rubrics, generating exemplars), personalize supports (reading levels, modalities), and power tutoring/Q&A assistants. Start with clear goals, lightweight pilots, and strong guardrails (privacy, integrity, bias mitigation).

What AI can help with today

• Planning & preparation: lesson outlines, rubrics, parent comms, exemplars.
• Differentiation & accessibility: adjust reading levels, formats, and supports (UDL).
• Feedback at scale: rubric-aligned suggestions on drafts with teacher oversight.
• Course/LMS assistants: syllabus-aware Q&A over your materials (retrieval-augmented).

Risks & policy basics

• Academic integrity: design AI-inclusive tasks; collect process evidence.
• Bias & safety: review outputs; keep humans in the loop for consequential decisions.
• Privacy & data: understand data flows; align with FERPA/COPPA/GDPR where relevant.

See the guidance below for checklists and model policies.

Getting started (quick playbook)

1. Pick two workflows to pilot (e.g., unit planning and feedback on lab reports).
2. Define guardrails (allowed uses, disclosure, citation, integrity, privacy).
3. Use private data safely (avoid PII; prefer institution-approved tools).
4. Measure impact (time saved, quality rubrics, student outcomes).
5. Share exemplars and iterate.

Essential guidance & policy

• U.S. Dept. of Education — Artificial Intelligence & the Future of Teaching and Learning (2023)
• UNESCO — Guidance for Generative AI in Education & Research (2023)
• UK Department for Education — Generative AI in Education (guidance)
• OECD — AI, Education & Skills hub · Curriculum implications (2025)
• ISTE — Bringing AI to School: Tips for School Leaders

Practice & implementation

• ISTE AI in Education hub (courses, tool vetting, frameworks)
• U.S. Dept. of Ed — core messages one-pager