Welcome & approaches to teaching AI Fluency

Hello, and welcome to Teaching AI Fluency. I'm Zoe, joined by Maggie Vo, Drew Feller, and Rick Taketa.

If you're here, you've already been working with the AI Fluency Framework. Maybe you've discovered something powerful in how it helps you think about AI collaboration, and now you're wondering: "How do I share this with my students?" That's exactly what this course is about. Whether you're teaching students or fellow educators, this course will equip you with pedagogical strategies, assessment approaches, and ways to effectively communicate the AI Fluency Framework to others.

The Dual Nature of the AI Fluency Framework

Before we dive into the course, let's make sure we're on the same page about the nature of the framework. The framework operates on two levels:

1. Descriptive: It describes what actually happens when people work with AI—the decisions, the communication, the evaluation, and the responsibility. It gives us a map of the territory.
2. Prescriptive: It guides people toward better practice. It doesn't just describe what people do; it shows them what they should do to work with AI effectively, efficiently, ethically, and safely.

This dual nature is crucial for teaching AI Fluency. Your students need to understand both how AI interactions work and how to make those interactions work better. The framework gives us a language and a structure for both.

Course Structure and Essential Questions

We've organized this course around three essential questions every educator faces when introducing AI Fluency:

• Lesson 1: How can we introduce the framework to students? We'll look at pedagogy and different ways of exploring the framework with your students.
• Lesson 2: How do we know if students are "getting it"? We'll look at assessment and how to measure and evaluate AI Fluency.
• Lesson 3: How do we connect this to what we're already teaching? We'll look at curriculum and how to integrate AI Fluency within a specific field or discipline.

Pedagogical Approaches to the AI Fluency Framework

In our experience, we have found it useful to share the AI Fluency Framework with students using four different entry points or approaches. These strategies each add conceptual richness and depth of detail to support learning for different groups and contexts.

1. The Linear Process (The Waterfall Approach)

The most straightforward approach is to treat the framework as a linear process in which we move from Delegation to Description to Discernment to Diligence.

For students just starting to learn how to work with AI, this approach feels natural, manageable, and intuitive. It's similar to how you might have first learned the framework yourself. This "Waterfall-style" approach helps students build a basic process for their AI interactions and approach the competencies sequentially.

Example Assignment Structure:

• Part 1 (Delegation): Focuses on project planning and deciding what to hand off to AI.
• Part 2 (Description): Develops skills through hands-on workshops where students interact directly with AI via prompts.
• Part 3 (Discernment): Builds skills by evaluating AI outputs together.
• Part 4 (Diligence): Addresses ethical discussions and transparency practices.

2. The Interconnected System (The Holistic Approach)

The second approach acknowledges that the "Four Ds" aren't actually a fixed sequence. Rather, they are an interconnected system, which means you can start anywhere and bounce around as needed. For students with more AI experience, this approach adds real-world complexity and flexibility.

Consider these scenarios:

• Discernment to Delegation: A student evaluating AI-generated code might realize that the AI system they chose isn't suited for their programming paradigm, prompting them to reconsider their initial delegation.
• Diligence to Description: When working on sensitive data, privacy requirements might dictate that students craft prompts without sensitive information, fundamentally changing how they communicate with the AI.
• Description to Delegation: A student struggling to articulate what they want from an AI partner might realize they haven't clearly defined their own project goals, sending them back to revisit fundamental delegation questions.

This approach lends itself to project-based and case-based learning, letting students discover which competencies are needed and when.

3. Deep Exploration (The Focused Approach)

The third approach involves deep explorations of just one competency at a time. This is ideal for specialized purposes, like a skill-building session.

• Description Focus: You could spend an entire workshop on concrete techniques for subcomponents of prompting with hands-on practice.
• Diligence Focus: You could focus a discussion seminar entirely on ethics and responsibility.
• Discernment Focus: You could run three sessions on discernment alone:
  • Product Discernment: Evaluating AI-generated content for accuracy and utility.
  • Process Discernment: Evaluating the AI's problem-solving approach.
  • Performance Discernment: Assessing whether the AI's communication style is helping or hindering the collaboration process.

This focused approach helps students master a specific area without the cognitive load of juggling all four Ds simultaneously.

4. Nested Processes (The Loop Approach)

The fourth approach understands AI Fluency as nested processes or loops. This represents the most conceptually rich understanding of how the framework is applied in practice. We can break human-AI interaction into two decision spaces:

• The Delegation-Diligence Loop (Strategic): This handles the big picture. It involves strategic and ethical decision-making: What am I trying to do? How should I divide work? Which systems align with my values? How do I ensure responsible collaboration?
• The Description-Discernment Loop (Tactical): This handles the moment-to-moment decisions. It involves the tactical, iterative work of AI collaboration: How do I communicate this specific need? Is this output meeting my standards? How do I refine my approach?

In practice, these two loops constantly inform each other. Strategic decisions shape tactical execution, and tactical discoveries reshape strategic thinking.

Conclusion

Teaching AI Fluency is about helping students develop values, judgment, and practical skills while fostering more thoughtful approaches to human-AI collaboration. We've kept this course flexible to suit your specific subject, students, and constraints.

In our next lesson, we'll look at the Delegation-Diligence Loop and how to teach responsible design and decision-making in AI collaboration. After that, we'll examine the Description-Discernment Loop to help students build effective collaboration environments with AI. Together, we can help our students navigate this transformative technology with competence and confidence.