The Four Ds of AI Fluency - A Framework That Actually Makes Sense

Delegation, Description, Discernment, Diligence. Rick Dakan and Joseph Feller built a practical framework for working with AI that focuses on competencies, not hype.

Most AI literacy frameworks are either too vague (“understand AI’s impact on society”) or too technical (“learn gradient descent”). Neither helps someone actually work with AI tools effectively. Rick Dakan and Joseph Feller built something different—a framework that recognizes AI as a thinking partner while acknowledging that this only works if humans develop specific competencies.

Their Framework for AI Fluency, developed through courses at Ringling College of Art and Design and Cork University Business School, identifies what people actually need to know to use AI well. Not theory. Not ethics lectures disconnected from practice. Practical competencies that determine whether AI makes your work better or just creates more cleanup.

Education and industry are flooding the market with AI frameworks. Most follow predictable patterns: start with AI history, explain technical concepts nobody needs, add ethics as an afterthought, conclude with vague guidance about “responsible use.”

These frameworks treat AI as something to understand rather than something to use. They optimize for comprehensive coverage instead of practical competence. Students learn about neural networks but can’t evaluate whether an AI output is actually useful. Employees complete training modules but still don’t know when to use AI versus when to do the work themselves.

The Dakan-Feller framework inverts this approach. It starts with actual human-AI interaction patterns, identifies the competencies those patterns require, and builds a structure that works across tools, platforms, and use cases. Platform-agnostic, context-flexible, ethics-centered by design rather than addendum.

Before defining competencies, the framework maps how humans actually interact with AI. Three distinct modalities emerge from observing current practice.

AI performs human-defined tasks independently. You give clear instructions, the model executes, you use the output. This is the efficiency play—offloading repetitive, time-consuming, or data-intensive work.