# AI Fluency for Educators

Raw note — how to bring AI into teaching practice.

Source: Anthropic, *AI Fluency for Educators* course.

## The 4Ds

The whole framework is four moves. Each one has a meaning and a question you actually ask yourself.

- **Delegation** — decide what you're doing with AI and why
  - _Ask:_ what parts of my work can be augmented with AI?
- **Description** — invite AI into your specific problem space
  - _Ask:_ what do I want AI to do, and how should it do it?
- **Discernment** — evaluate AI suggestions against your domain expertise
  - _Ask:_ what's wrong here, and what's unexpectedly good?
- **Diligence** — take responsibility for what ends up in front of students
  - _Ask:_ is this quality-assured, owned, and transparent?

## Applying it to course design and learning outcomes

- TODO: the course covered how outcomes drive delegation — I didn't capture this part. Revisit.
- Diligence for educators is public, not private:
  - model responsible AI collaboration
  - demonstrate AI-enhanced teaching
  - share pedagogical innovations

## Applying it to learning materials and assignments

- **Description–Discernment loop** — describe, review, re-describe. One prompt is never the material.
- Three kinds of diligence in material design:
  - **Creation diligence** — protect sensitive data and model responsible use
  - **Transparency diligence** — write a diligence statement wherever AI was used
  - **Deployment diligence** — review materials carefully before they reach students

## Open questions

- Where's the line between a diligence statement that builds trust and one that's just noise?
- How do you teach the 4Ds to students, not only apply them as the educator?